>>> from astrapy import DataAPIClient
>>> my_client = DataAPIClient("AstraCS:...")
>>> my_astra_db_admin = my_client.get_admin()
>>> database_list = my_astra_db_admin.list_databases()
>>> len(database_list)
3
>>> database_list[2].id
'01234567-...'
>>> my_db_admin = my_astra_db_admin.get_database_admin("01234567-...")
>>> my_db_admin.list_namespaces()
['default_keyspace', 'staging_namespace']

class AstraDBAdmin:
    """
    An "admin" object, able to perform administrative tasks at the databases
    level, such as creating, listing or dropping databases.

    Args:
        token: an access token with enough permission to perform admin tasks.
        environment: a label, whose value is one of Environment.PROD (default),
            Environment.DEV or Environment.TEST.
        caller_name: name of the application, or framework, on behalf of which
            the DevOps API calls are performed. This ends up in the request user-agent.
        caller_version: version of the caller.
        dev_ops_url: in case of custom deployments, this can be used to specify
            the URL to the DevOps API, such as "https://api.astra.datastax.com".
            Generally it can be omitted. The environment (prod/dev/...) is
            determined from the API Endpoint.
        dev_ops_api_version: this can specify a custom version of the DevOps API
            (such as "v2"). Generally not needed.

    Example:
        >>> from astrapy import DataAPIClient
        >>> my_client = DataAPIClient("AstraCS:...")
        >>> my_astra_db_admin = my_client.get_admin()
        >>> database_list = my_astra_db_admin.list_databases()
        >>> len(database_list)
        3
        >>> database_list[2].id
        '01234567-...'
        >>> my_db_admin = my_astra_db_admin.get_database_admin("01234567-...")
        >>> my_db_admin.list_namespaces()
        ['default_keyspace', 'staging_namespace']
    """

    def __init__(
        self,
        token: str,
        *,
        environment: Optional[str] = None,
        caller_name: Optional[str] = None,
        caller_version: Optional[str] = None,
        dev_ops_url: Optional[str] = None,
        dev_ops_api_version: Optional[str] = None,
    ) -> None:
        self.token = token
        self.environment = environment or Environment.PROD
        if dev_ops_url is None:
            self.dev_ops_url = DEV_OPS_URL_MAP[self.environment]
        else:
            self.dev_ops_url = dev_ops_url
        self._caller_name = caller_name
        self._caller_version = caller_version
        self._dev_ops_url = dev_ops_url
        self._dev_ops_api_version = dev_ops_api_version
        self._astra_db_ops = AstraDBOps(
            token=self.token,
            dev_ops_url=self.dev_ops_url,
            dev_ops_api_version=dev_ops_api_version,
            caller_name=caller_name,
            caller_version=caller_version,
        )

    def __repr__(self) -> str:
        env_desc: str
        if self.environment == Environment.PROD:
            env_desc = ""
        else:
            env_desc = f', environment="{self.environment}"'
        return f'{self.__class__.__name__}("{self.token[:12]}..."{env_desc})'

    def __eq__(self, other: Any) -> bool:
        if isinstance(other, AstraDBAdmin):
            return all(
                [
                    self.token == other.token,
                    self.environment == other.environment,
                    self.dev_ops_url == other.dev_ops_url,
                    self.dev_ops_url == other.dev_ops_url,
                    self._caller_name == other._caller_name,
                    self._caller_version == other._caller_version,
                    self._dev_ops_url == other._dev_ops_url,
                    self._dev_ops_api_version == other._dev_ops_api_version,
                    self._astra_db_ops == other._astra_db_ops,
                ]
            )
        else:
            return False

    def _copy(
        self,
        *,
        token: Optional[str] = None,
        environment: Optional[str] = None,
        caller_name: Optional[str] = None,
        caller_version: Optional[str] = None,
        dev_ops_url: Optional[str] = None,
        dev_ops_api_version: Optional[str] = None,
    ) -> AstraDBAdmin:
        return AstraDBAdmin(
            token=token or self.token,
            environment=environment or self.environment,
            caller_name=caller_name or self._caller_name,
            caller_version=caller_version or self._caller_version,
            dev_ops_url=dev_ops_url or self._dev_ops_url,
            dev_ops_api_version=dev_ops_api_version or self._dev_ops_api_version,
        )

    def with_options(
        self,
        *,
        token: Optional[str] = None,
        caller_name: Optional[str] = None,
        caller_version: Optional[str] = None,
    ) -> AstraDBAdmin:
        """
        Create a clone of this AstraDBAdmin with some changed attributes.

        Args:
            token: an Access Token to the database. Example: `"AstraCS:xyz..."`.
            caller_name: name of the application, or framework, on behalf of which
                the Data API and DevOps API calls are performed. This ends up in
                the request user-agent.
            caller_version: version of the caller.

        Returns:
            a new AstraDBAdmin instance.

        Example:
            >>> another_astra_db_admin = my_astra_db_admin.with_options(
            ...     caller_name="caller_identity",
            ...     caller_version="1.2.0",
            ... )
        """

        return self._copy(
            token=token,
            caller_name=caller_name,
            caller_version=caller_version,
        )

    def set_caller(
        self,
        caller_name: Optional[str] = None,
        caller_version: Optional[str] = None,
    ) -> None:
        """
        Set a new identity for the application/framework on behalf of which
        the DevOps API calls will be performed (the "caller").

        New objects spawned from this client afterwards will inherit the new settings.

        Args:
            caller_name: name of the application, or framework, on behalf of which
                the DevOps API calls are performed. This ends up in the request user-agent.
            caller_version: version of the caller.

        Example:
            >>> my_astra_db_admin.set_caller(
            ...     caller_name="the_caller",
            ...     caller_version="0.1.0",
            ... )
        """

        logger.info(f"setting caller to {caller_name}/{caller_version}")
        self._caller_name = caller_name
        self._caller_version = caller_version
        self._astra_db_ops.set_caller(caller_name, caller_version)

    @ops_recast_method_sync
    def list_databases(
        self,
        *,
        max_time_ms: Optional[int] = None,
    ) -> CommandCursor[AdminDatabaseInfo]:
        """
        Get the list of databases, as obtained with a request to the DevOps API.

        Args:
            max_time_ms: a timeout, in milliseconds, for the API request.

        Returns:
            A CommandCursor to iterate over the detected databases,
            represented as AdminDatabaseInfo objects.

        Example:
            >>> database_cursor = my_astra_db_admin.list_databases()
            >>> database_list = list(database_cursor)
            >>> len(database_list)
            3
            >>> database_list[2].id
            '01234567-...'
            >>> database_list[2].status
            'ACTIVE'
            >>> database_list[2].info.region
            'eu-west-1'
        """

        logger.info("getting databases")
        gd_list_response = self._astra_db_ops.get_databases(
            timeout_info=base_timeout_info(max_time_ms)
        )
        logger.info("finished getting databases")
        if not isinstance(gd_list_response, list):
            raise DevOpsAPIException(
                "Faulty response from get-databases DevOps API command.",
            )
        else:
            # we know this is a list of dicts which need a little adjusting
            return CommandCursor(
                address=self._astra_db_ops.base_url,
                items=[
                    _recast_as_admin_database_info(
                        db_dict,
                        environment=self.environment,
                    )
                    for db_dict in gd_list_response
                ],
            )

    @ops_recast_method_async
    async def async_list_databases(
        self,
        *,
        max_time_ms: Optional[int] = None,
    ) -> CommandCursor[AdminDatabaseInfo]:
        """
        Get the list of databases, as obtained with a request to the DevOps API.
        Async version of the method, for use in an asyncio context.

        Args:
            max_time_ms: a timeout, in milliseconds, for the API request.

        Returns:
            A CommandCursor to iterate over the detected databases,
            represented as AdminDatabaseInfo objects.
            Note that the return type is not an awaitable, rather
            a regular iterable, e.g. for use in ordinary "for" loops.

        Example:
            >>> async def check_if_db_exists(db_id: str) -> bool:
            ...     db_cursor = await my_astra_db_admin.async_list_databases()
            ...     db_list = list(dd_cursor)
            ...     return db_id in db_list
            ...
            >>> asyncio.run(check_if_db_exists("xyz"))
            True
            >>> asyncio.run(check_if_db_exists("01234567-..."))
            False
        """

        logger.info("getting databases, async")
        gd_list_response = await self._astra_db_ops.async_get_databases(
            timeout_info=base_timeout_info(max_time_ms)
        )
        logger.info("finished getting databases, async")
        if not isinstance(gd_list_response, list):
            raise DevOpsAPIException(
                "Faulty response from get-databases DevOps API command.",
            )
        else:
            # we know this is a list of dicts which need a little adjusting
            return CommandCursor(
                address=self._astra_db_ops.base_url,
                items=[
                    _recast_as_admin_database_info(
                        db_dict,
                        environment=self.environment,
                    )
                    for db_dict in gd_list_response
                ],
            )

    @ops_recast_method_sync
    def database_info(
        self, id: str, *, max_time_ms: Optional[int] = None
    ) -> AdminDatabaseInfo:
        """
        Get the full information on a given database, through a request to the DevOps API.

        Args:
            id: the ID of the target database, e. g.
                "01234567-89ab-cdef-0123-456789abcdef".
            max_time_ms: a timeout, in milliseconds, for the API request.

        Returns:
            An AdminDatabaseInfo object.

        Example:
            >>> details_of_my_db = my_astra_db_admin.database_info("01234567-...")
            >>> details_of_my_db.id
            '01234567-...'
            >>> details_of_my_db.status
            'ACTIVE'
            >>> details_of_my_db.info.region
            'eu-west-1'
        """

        logger.info(f"getting database info for '{id}'")
        gd_response = self._astra_db_ops.get_database(
            database=id,
            timeout_info=base_timeout_info(max_time_ms),
        )
        logger.info(f"finished getting database info for '{id}'")
        if not isinstance(gd_response, dict):
            raise DevOpsAPIException(
                "Faulty response from get-database DevOps API command.",
            )
        else:
            return _recast_as_admin_database_info(
                gd_response,
                environment=self.environment,
            )

    @ops_recast_method_async
    async def async_database_info(
        self, id: str, *, max_time_ms: Optional[int] = None
    ) -> AdminDatabaseInfo:
        """
        Get the full information on a given database, through a request to the DevOps API.
        This is an awaitable method suitable for use within an asyncio event loop.

        Args:
            id: the ID of the target database, e. g.
                "01234567-89ab-cdef-0123-456789abcdef".
            max_time_ms: a timeout, in milliseconds, for the API request.

        Returns:
            An AdminDatabaseInfo object.

        Example:
            >>> async def check_if_db_active(db_id: str) -> bool:
            ...     db_info = await my_astra_db_admin.async_database_info(db_id)
            ...     return db_info.status == "ACTIVE"
            ...
            >>> asyncio.run(check_if_db_active("01234567-..."))
            True
        """

        logger.info(f"getting database info for '{id}', async")
        gd_response = await self._astra_db_ops.async_get_database(
            database=id,
            timeout_info=base_timeout_info(max_time_ms),
        )
        logger.info(f"finished getting database info for '{id}', async")
        if not isinstance(gd_response, dict):
            raise DevOpsAPIException(
                "Faulty response from get-database DevOps API command.",
            )
        else:
            return _recast_as_admin_database_info(
                gd_response,
                environment=self.environment,
            )

    @ops_recast_method_sync
    def create_database(
        self,
        name: str,
        *,
        cloud_provider: str,
        region: str,
        namespace: Optional[str] = None,
        wait_until_active: bool = True,
        max_time_ms: Optional[int] = None,
    ) -> AstraDBDatabaseAdmin:
        """
        Create a database as requested, optionally waiting for it to be ready.

        Args:
            name: the desired name for the database.
            cloud_provider: one of 'aws', 'gcp' or 'azure'.
            region: any of the available cloud regions.
            namespace: name for the one namespace the database starts with.
                If omitted, DevOps API will use its default.
            wait_until_active: if True (default), the method returns only after
                the newly-created database is in ACTIVE state (a few minutes,
                usually). If False, it will return right after issuing the
                creation request to the DevOps API, and it will be responsibility
                of the caller to check the database status before working with it.
            max_time_ms: a timeout, in milliseconds, for the whole requested
                operation to complete.
                Note that a timeout is no guarantee that the creation request
                has not reached the API server.

        Returns:
            An AstraDBDatabaseAdmin instance.

        Example:
            >>> my_new_db_admin = my_astra_db_admin.create_database(
            ...     "new_database",
            ...     cloud_provider="aws",
            ...     region="ap-south-1",
            ... )
            >>> my_new_db = my_new_db_admin.get_database()
            >>> my_coll = my_new_db.create_collection("movies", dimension=512)
            >>> my_coll.insert_one({"title": "The Title"}, vector=...)
        """

        database_definition = {
            k: v
            for k, v in {
                "name": name,
                "tier": "serverless",
                "cloudProvider": cloud_provider,
                "region": region,
                "capacityUnits": 1,
                "dbType": "vector",
                "keyspace": namespace,
            }.items()
            if v is not None
        }
        timeout_manager = MultiCallTimeoutManager(
            overall_max_time_ms=max_time_ms, exception_type="devops_api"
        )
        logger.info(f"creating database {name}/({cloud_provider}, {region})")
        cd_response = self._astra_db_ops.create_database(
            database_definition=database_definition,
            timeout_info=base_timeout_info(max_time_ms),
        )
        logger.info(
            "devops api returned from creating database "
            f"{name}/({cloud_provider}, {region})"
        )
        if cd_response is not None and "id" in cd_response:
            new_database_id = cd_response["id"]
            if wait_until_active:
                last_status_seen = STATUS_PENDING
                while last_status_seen in {STATUS_PENDING, STATUS_INITIALIZING}:
                    logger.info(f"sleeping to poll for status of '{new_database_id}'")
                    time.sleep(DATABASE_POLL_SLEEP_TIME)
                    last_db_info = self.database_info(
                        id=new_database_id,
                        max_time_ms=timeout_manager.remaining_timeout_ms(),
                    )
                    last_status_seen = last_db_info.status
                if last_status_seen != STATUS_ACTIVE:
                    raise DevOpsAPIException(
                        f"Database {name} entered unexpected status {last_status_seen} after PENDING"
                    )
            # return the database instance
            logger.info(
                f"finished creating database '{new_database_id}' = "
                f"{name}/({cloud_provider}, {region})"
            )
            return AstraDBDatabaseAdmin.from_astra_db_admin(
                id=new_database_id,
                astra_db_admin=self,
            )
        else:
            raise DevOpsAPIException("Could not create the database.")

    @ops_recast_method_async
    async def async_create_database(
        self,
        name: str,
        *,
        cloud_provider: str,
        region: str,
        namespace: Optional[str] = None,
        wait_until_active: bool = True,
        max_time_ms: Optional[int] = None,
    ) -> AstraDBDatabaseAdmin:
        """
        Create a database as requested, optionally waiting for it to be ready.
        This is an awaitable method suitable for use within an asyncio event loop.

        Args:
            name: the desired name for the database.
            cloud_provider: one of 'aws', 'gcp' or 'azure'.
            region: any of the available cloud regions.
            namespace: name for the one namespace the database starts with.
                If omitted, DevOps API will use its default.
            wait_until_active: if True (default), the method returns only after
                the newly-created database is in ACTIVE state (a few minutes,
                usually). If False, it will return right after issuing the
                creation request to the DevOps API, and it will be responsibility
                of the caller to check the database status before working with it.
            max_time_ms: a timeout, in milliseconds, for the whole requested
                operation to complete.
                Note that a timeout is no guarantee that the creation request
                has not reached the API server.

        Returns:
            An AstraDBDatabaseAdmin instance.

        Example:
            >>> asyncio.run(
            ...     my_astra_db_admin.async_create_database(
            ...         "new_database",
            ...         cloud_provider="aws",
            ...         region="ap-south-1",
            ....    )
            ... )
            AstraDBDatabaseAdmin(id=...)
        """

        database_definition = {
            k: v
            for k, v in {
                "name": name,
                "tier": "serverless",
                "cloudProvider": cloud_provider,
                "region": region,
                "capacityUnits": 1,
                "dbType": "vector",
                "keyspace": namespace,
            }.items()
            if v is not None
        }
        timeout_manager = MultiCallTimeoutManager(
            overall_max_time_ms=max_time_ms, exception_type="devops_api"
        )
        logger.info(f"creating database {name}/({cloud_provider}, {region}), async")
        cd_response = await self._astra_db_ops.async_create_database(
            database_definition=database_definition,
            timeout_info=base_timeout_info(max_time_ms),
        )
        logger.info(
            "devops api returned from creating database "
            f"{name}/({cloud_provider}, {region}), async"
        )
        if cd_response is not None and "id" in cd_response:
            new_database_id = cd_response["id"]
            if wait_until_active:
                last_status_seen = STATUS_PENDING
                while last_status_seen in {STATUS_PENDING, STATUS_INITIALIZING}:
                    logger.info(
                        f"sleeping to poll for status of '{new_database_id}', async"
                    )
                    await asyncio.sleep(DATABASE_POLL_SLEEP_TIME)
                    last_db_info = await self.async_database_info(
                        id=new_database_id,
                        max_time_ms=timeout_manager.remaining_timeout_ms(),
                    )
                    last_status_seen = last_db_info.status
                if last_status_seen != STATUS_ACTIVE:
                    raise DevOpsAPIException(
                        f"Database {name} entered unexpected status {last_status_seen} after PENDING"
                    )
            # return the database instance
            logger.info(
                f"finished creating database '{new_database_id}' = "
                f"{name}/({cloud_provider}, {region}), async"
            )
            return AstraDBDatabaseAdmin.from_astra_db_admin(
                id=new_database_id,
                astra_db_admin=self,
            )
        else:
            raise DevOpsAPIException("Could not create the database.")

    @ops_recast_method_sync
    def drop_database(
        self,
        id: str,
        *,
        wait_until_active: bool = True,
        max_time_ms: Optional[int] = None,
    ) -> Dict[str, Any]:
        """
        Drop a database, i.e. delete it completely and permanently with all its data.

        Args:
            id: The ID of the database to drop, e. g.
                "01234567-89ab-cdef-0123-456789abcdef".
            wait_until_active: if True (default), the method returns only after
                the database has actually been deleted (generally a few minutes).
                If False, it will return right after issuing the
                drop request to the DevOps API, and it will be responsibility
                of the caller to check the database status/availability
                after that, if desired.
            max_time_ms: a timeout, in milliseconds, for the whole requested
                operation to complete.
                Note that a timeout is no guarantee that the deletion request
                has not reached the API server.

        Returns:
            A dictionary of the form {"ok": 1} in case of success.
            Otherwise, an exception is raised.

        Example:
            >>> database_list_pre = my_astra_db_admin.list_databases()
            >>> len(database_list_pre)
            3
            >>> my_astra_db_admin.drop_database("01234567-...")
            {'ok': 1}
            >>> database_list_post = my_astra_db_admin.list_databases()
            >>> len(database_list_post)
            2
        """

        timeout_manager = MultiCallTimeoutManager(
            overall_max_time_ms=max_time_ms, exception_type="devops_api"
        )
        logger.info(f"dropping database '{id}'")
        te_response = self._astra_db_ops.terminate_database(
            database=id,
            timeout_info=base_timeout_info(max_time_ms),
        )
        logger.info(f"devops api returned from dropping database '{id}'")
        if te_response == id:
            if wait_until_active:
                last_status_seen: Optional[str] = STATUS_TERMINATING
                _db_name: Optional[str] = None
                while last_status_seen == STATUS_TERMINATING:
                    logger.info(f"sleeping to poll for status of '{id}'")
                    time.sleep(DATABASE_POLL_SLEEP_TIME)
                    #
                    detected_databases = [
                        a_db_info
                        for a_db_info in self.list_databases(
                            max_time_ms=timeout_manager.remaining_timeout_ms(),
                        )
                        if a_db_info.id == id
                    ]
                    if detected_databases:
                        last_status_seen = detected_databases[0].status
                        _db_name = detected_databases[0].info.name
                    else:
                        last_status_seen = None
                if last_status_seen is not None:
                    _name_desc = f" ({_db_name})" if _db_name else ""
                    raise DevOpsAPIException(
                        f"Database {id}{_name_desc} entered unexpected status {last_status_seen} after PENDING"
                    )
            logger.info(f"finished dropping database '{id}'")
            return {"ok": 1}
        else:
            raise DevOpsAPIException(
                f"Could not issue a successful terminate-database DevOps API request for {id}."
            )

    @ops_recast_method_async
    async def async_drop_database(
        self,
        id: str,
        *,
        wait_until_active: bool = True,
        max_time_ms: Optional[int] = None,
    ) -> Dict[str, Any]:
        """
        Drop a database, i.e. delete it completely and permanently with all its data.
        Async version of the method, for use in an asyncio context.

        Args:
            id: The ID of the database to drop, e. g.
                "01234567-89ab-cdef-0123-456789abcdef".
            wait_until_active: if True (default), the method returns only after
                the database has actually been deleted (generally a few minutes).
                If False, it will return right after issuing the
                drop request to the DevOps API, and it will be responsibility
                of the caller to check the database status/availability
                after that, if desired.
            max_time_ms: a timeout, in milliseconds, for the whole requested
                operation to complete.
                Note that a timeout is no guarantee that the deletion request
                has not reached the API server.

        Returns:
            A dictionary of the form {"ok": 1} in case of success.
            Otherwise, an exception is raised.

        Example:
            >>> asyncio.run(
            ...     my_astra_db_admin.async_drop_database("01234567-...")
            ... )
            {'ok': 1}
        """

        timeout_manager = MultiCallTimeoutManager(
            overall_max_time_ms=max_time_ms, exception_type="devops_api"
        )
        logger.info(f"dropping database '{id}', async")
        te_response = await self._astra_db_ops.async_terminate_database(
            database=id,
            timeout_info=base_timeout_info(max_time_ms),
        )
        logger.info(f"devops api returned from dropping database '{id}', async")
        if te_response == id:
            if wait_until_active:
                last_status_seen: Optional[str] = STATUS_TERMINATING
                _db_name: Optional[str] = None
                while last_status_seen == STATUS_TERMINATING:
                    logger.info(f"sleeping to poll for status of '{id}', async")
                    await asyncio.sleep(DATABASE_POLL_SLEEP_TIME)
                    #
                    detected_databases = [
                        a_db_info
                        for a_db_info in await self.async_list_databases(
                            max_time_ms=timeout_manager.remaining_timeout_ms(),
                        )
                        if a_db_info.id == id
                    ]
                    if detected_databases:
                        last_status_seen = detected_databases[0].status
                        _db_name = detected_databases[0].info.name
                    else:
                        last_status_seen = None
                if last_status_seen is not None:
                    _name_desc = f" ({_db_name})" if _db_name else ""
                    raise DevOpsAPIException(
                        f"Database {id}{_name_desc} entered unexpected status {last_status_seen} after PENDING"
                    )
            logger.info(f"finished dropping database '{id}', async")
            return {"ok": 1}
        else:
            raise DevOpsAPIException(
                f"Could not issue a successful terminate-database DevOps API request for {id}."
            )

    def get_database_admin(self, id: str) -> AstraDBDatabaseAdmin:
        """
        Create an AstraDBDatabaseAdmin object for admin work within a certain database.

        Args:
            id: the ID of the target database, e. g. "01234567-89ab-cdef-0123-456789abcdef".

        Returns:
            An AstraDBDatabaseAdmin instance representing the requested database.

        Example:
            >>> my_db_admin = my_astra_db_admin.get_database_admin("01234567-...")
            >>> my_db_admin.list_namespaces()
            ['default_keyspace']
            >>> my_db_admin.create_namespace("that_other_one")
            {'ok': 1}
            >>> my_db_admin.list_namespaces()
            ['default_keyspace', 'that_other_one']

        Note:
            This method does not perform any admin-level operation through
            the DevOps API. For actual creation of a database, see the
            `create_database` method.
        """

        return AstraDBDatabaseAdmin.from_astra_db_admin(
            id=id,
            astra_db_admin=self,
        )

    def get_database(
        self,
        id: str,
        *,
        token: Optional[str] = None,
        namespace: Optional[str] = None,
        region: Optional[str] = None,
        api_path: Optional[str] = None,
        api_version: Optional[str] = None,
        max_time_ms: Optional[int] = None,
    ) -> Database:
        """
        Create a Database instance for a specific database, to be used
        when doing data-level work (such as creating/managing collections).

        Args:
            id: e. g. "01234567-89ab-cdef-0123-456789abcdef".
            token: if supplied, is passed to the Database instead of
                the one set for this object.
            namespace: used to specify a certain namespace the resulting
                Database will primarily work on. If not specified, similar
                as for `region`, an additional DevOps API call reveals
                the default namespace for the target database.
            region: the region to use for connecting to the database. The
                database must be located in that region.
                Note that if this parameter is not passed, an additional
                DevOps API request is made to determine the default region
                and use it subsequently.
                If both `namespace` and `region` are missing, a single
                DevOps API request is made.
            api_path: path to append to the API Endpoint. In typical usage, this
                should be left to its default of "/api/json".
            api_version: version specifier to append to the API path. In typical
                usage, this should be left to its default of "v1".
            max_time_ms: a timeout, in milliseconds, for the DevOps API
                HTTP request should it be necessary (see the `region` argument).

        Returns:
            A Database object ready to be used.

        Example:
            >>> my_db = my_astra_db_admin.get_database(
            ...     "01234567-...",
            ...     region="us-east1",
            ... )
            >>> coll = my_db.create_collection("movies", dimension=512)
            >>> my_coll.insert_one({"title": "The Title"}, vector=...)

        Note:
            This method does not perform any admin-level operation through
            the DevOps API. For actual creation of a database, see the
            `create_database` method of class AstraDBAdmin.
        """

        # lazy importing here to avoid circular dependency
        from astrapy import Database

        # need to inspect for values?
        this_db_info: Optional[AdminDatabaseInfo] = None
        # handle overrides
        _token = token or self.token
        if namespace:
            _namespace = namespace
        else:
            if this_db_info is None:
                this_db_info = self.database_info(id, max_time_ms=max_time_ms)
            _namespace = this_db_info.info.namespace
        if region:
            _region = region
        else:
            if this_db_info is None:
                this_db_info = self.database_info(id, max_time_ms=max_time_ms)
            _region = this_db_info.info.region

        _api_endpoint = build_api_endpoint(
            environment=self.environment,
            database_id=id,
            region=_region,
        )
        return Database(
            api_endpoint=_api_endpoint,
            token=_token,
            namespace=_namespace,
            caller_name=self._caller_name,
            caller_version=self._caller_version,
            api_path=api_path,
            api_version=api_version,
        )

    def get_async_database(
        self,
        id: str,
        *,
        token: Optional[str] = None,
        namespace: Optional[str] = None,
        region: Optional[str] = None,
        api_path: Optional[str] = None,
        api_version: Optional[str] = None,
    ) -> AsyncDatabase:
        """
        Create an AsyncDatabase instance for a specific database, to be used
        when doing data-level work (such as creating/managing collections).

        This method has identical behavior and signature as the sync
        counterpart `get_database`: please see that one for more details.
        """

        return self.get_database(
            id=id,
            token=token,
            namespace=namespace,
            region=region,
            api_path=api_path,
            api_version=api_version,
        ).to_async()

Methods

async def async_create_database(self, name: str, *, cloud_provider: str, region: str, namespace: Optional[str] = None, wait_until_active: bool = True, max_time_ms: Optional[int] = None) ‑> AstraDBDatabaseAdmin

Create a database as requested, optionally waiting for it to be ready. This is an awaitable method suitable for use within an asyncio event loop.

Args

name

the desired name for the database.

cloud_provider

one of 'aws', 'gcp' or 'azure'.

region

any of the available cloud regions.

namespace

name for the one namespace the database starts with. If omitted, DevOps API will use its default.

wait_until_active

if True (default), the method returns only after the newly-created database is in ACTIVE state (a few minutes, usually). If False, it will return right after issuing the creation request to the DevOps API, and it will be responsibility of the caller to check the database status before working with it.

max_time_ms

a timeout, in milliseconds, for the whole requested operation to complete. Note that a timeout is no guarantee that the creation request has not reached the API server.

Returns

An AstraDBDatabaseAdmin instance.

Example

>>> asyncio.run(
...     my_astra_db_admin.async_create_database(
...         "new_database",
...         cloud_provider="aws",
...         region="ap-south-1",
....    )
... )
AstraDBDatabaseAdmin(id=...)

Expand source code

@ops_recast_method_async
async def async_create_database(
    self,
    name: str,
    *,
    cloud_provider: str,
    region: str,
    namespace: Optional[str] = None,
    wait_until_active: bool = True,
    max_time_ms: Optional[int] = None,
) -> AstraDBDatabaseAdmin:
    """
    Create a database as requested, optionally waiting for it to be ready.
    This is an awaitable method suitable for use within an asyncio event loop.

    Args:
        name: the desired name for the database.
        cloud_provider: one of 'aws', 'gcp' or 'azure'.
        region: any of the available cloud regions.
        namespace: name for the one namespace the database starts with.
            If omitted, DevOps API will use its default.
        wait_until_active: if True (default), the method returns only after
            the newly-created database is in ACTIVE state (a few minutes,
            usually). If False, it will return right after issuing the
            creation request to the DevOps API, and it will be responsibility
            of the caller to check the database status before working with it.
        max_time_ms: a timeout, in milliseconds, for the whole requested
            operation to complete.
            Note that a timeout is no guarantee that the creation request
            has not reached the API server.

    Returns:
        An AstraDBDatabaseAdmin instance.

    Example:
        >>> asyncio.run(
        ...     my_astra_db_admin.async_create_database(
        ...         "new_database",
        ...         cloud_provider="aws",
        ...         region="ap-south-1",
        ....    )
        ... )
        AstraDBDatabaseAdmin(id=...)
    """

    database_definition = {
        k: v
        for k, v in {
            "name": name,
            "tier": "serverless",
            "cloudProvider": cloud_provider,
            "region": region,
            "capacityUnits": 1,
            "dbType": "vector",
            "keyspace": namespace,
        }.items()
        if v is not None
    }
    timeout_manager = MultiCallTimeoutManager(
        overall_max_time_ms=max_time_ms, exception_type="devops_api"
    )
    logger.info(f"creating database {name}/({cloud_provider}, {region}), async")
    cd_response = await self._astra_db_ops.async_create_database(
        database_definition=database_definition,
        timeout_info=base_timeout_info(max_time_ms),
    )
    logger.info(
        "devops api returned from creating database "
        f"{name}/({cloud_provider}, {region}), async"
    )
    if cd_response is not None and "id" in cd_response:
        new_database_id = cd_response["id"]
        if wait_until_active:
            last_status_seen = STATUS_PENDING
            while last_status_seen in {STATUS_PENDING, STATUS_INITIALIZING}:
                logger.info(
                    f"sleeping to poll for status of '{new_database_id}', async"
                )
                await asyncio.sleep(DATABASE_POLL_SLEEP_TIME)
                last_db_info = await self.async_database_info(
                    id=new_database_id,
                    max_time_ms=timeout_manager.remaining_timeout_ms(),
                )
                last_status_seen = last_db_info.status
            if last_status_seen != STATUS_ACTIVE:
                raise DevOpsAPIException(
                    f"Database {name} entered unexpected status {last_status_seen} after PENDING"
                )
        # return the database instance
        logger.info(
            f"finished creating database '{new_database_id}' = "
            f"{name}/({cloud_provider}, {region}), async"
        )
        return AstraDBDatabaseAdmin.from_astra_db_admin(
            id=new_database_id,
            astra_db_admin=self,
        )
    else:
        raise DevOpsAPIException("Could not create the database.")

async def async_database_info(self, id: str, *, max_time_ms: Optional[int] = None) ‑> AdminDatabaseInfo

Get the full information on a given database, through a request to the DevOps API. This is an awaitable method suitable for use within an asyncio event loop.

Args

id

the ID of the target database, e. g. "01234567-89ab-cdef-0123-456789abcdef".

max_time_ms

a timeout, in milliseconds, for the API request.

Returns

An AdminDatabaseInfo object.

Example

>>> async def check_if_db_active(db_id: str) -> bool:
...     db_info = await my_astra_db_admin.async_database_info(db_id)
...     return db_info.status == "ACTIVE"
...
>>> asyncio.run(check_if_db_active("01234567-..."))
True

Expand source code

@ops_recast_method_async
async def async_database_info(
    self, id: str, *, max_time_ms: Optional[int] = None
) -> AdminDatabaseInfo:
    """
    Get the full information on a given database, through a request to the DevOps API.
    This is an awaitable method suitable for use within an asyncio event loop.

    Args:
        id: the ID of the target database, e. g.
            "01234567-89ab-cdef-0123-456789abcdef".
        max_time_ms: a timeout, in milliseconds, for the API request.

    Returns:
        An AdminDatabaseInfo object.

    Example:
        >>> async def check_if_db_active(db_id: str) -> bool:
        ...     db_info = await my_astra_db_admin.async_database_info(db_id)
        ...     return db_info.status == "ACTIVE"
        ...
        >>> asyncio.run(check_if_db_active("01234567-..."))
        True
    """

    logger.info(f"getting database info for '{id}', async")
    gd_response = await self._astra_db_ops.async_get_database(
        database=id,
        timeout_info=base_timeout_info(max_time_ms),
    )
    logger.info(f"finished getting database info for '{id}', async")
    if not isinstance(gd_response, dict):
        raise DevOpsAPIException(
            "Faulty response from get-database DevOps API command.",
        )
    else:
        return _recast_as_admin_database_info(
            gd_response,
            environment=self.environment,
        )

async def async_drop_database(self, id: str, *, wait_until_active: bool = True, max_time_ms: Optional[int] = None) ‑> Dict[str, Any]

Drop a database, i.e. delete it completely and permanently with all its data. Async version of the method, for use in an asyncio context.

Args

id

The ID of the database to drop, e. g. "01234567-89ab-cdef-0123-456789abcdef".

wait_until_active

if True (default), the method returns only after the database has actually been deleted (generally a few minutes). If False, it will return right after issuing the drop request to the DevOps API, and it will be responsibility of the caller to check the database status/availability after that, if desired.

max_time_ms

a timeout, in milliseconds, for the whole requested operation to complete. Note that a timeout is no guarantee that the deletion request has not reached the API server.

Returns

A dictionary of the form {"ok": 1} in case of success. Otherwise, an exception is raised.

Example

>>> asyncio.run(
...     my_astra_db_admin.async_drop_database("01234567-...")
... )
{'ok': 1}

Expand source code

@ops_recast_method_async
async def async_drop_database(
    self,
    id: str,
    *,
    wait_until_active: bool = True,
    max_time_ms: Optional[int] = None,
) -> Dict[str, Any]:
    """
    Drop a database, i.e. delete it completely and permanently with all its data.
    Async version of the method, for use in an asyncio context.

    Args:
        id: The ID of the database to drop, e. g.
            "01234567-89ab-cdef-0123-456789abcdef".
        wait_until_active: if True (default), the method returns only after
            the database has actually been deleted (generally a few minutes).
            If False, it will return right after issuing the
            drop request to the DevOps API, and it will be responsibility
            of the caller to check the database status/availability
            after that, if desired.
        max_time_ms: a timeout, in milliseconds, for the whole requested
            operation to complete.
            Note that a timeout is no guarantee that the deletion request
            has not reached the API server.

    Returns:
        A dictionary of the form {"ok": 1} in case of success.
        Otherwise, an exception is raised.

    Example:
        >>> asyncio.run(
        ...     my_astra_db_admin.async_drop_database("01234567-...")
        ... )
        {'ok': 1}
    """

    timeout_manager = MultiCallTimeoutManager(
        overall_max_time_ms=max_time_ms, exception_type="devops_api"
    )
    logger.info(f"dropping database '{id}', async")
    te_response = await self._astra_db_ops.async_terminate_database(
        database=id,
        timeout_info=base_timeout_info(max_time_ms),
    )
    logger.info(f"devops api returned from dropping database '{id}', async")
    if te_response == id:
        if wait_until_active:
            last_status_seen: Optional[str] = STATUS_TERMINATING
            _db_name: Optional[str] = None
            while last_status_seen == STATUS_TERMINATING:
                logger.info(f"sleeping to poll for status of '{id}', async")
                await asyncio.sleep(DATABASE_POLL_SLEEP_TIME)
                #
                detected_databases = [
                    a_db_info
                    for a_db_info in await self.async_list_databases(
                        max_time_ms=timeout_manager.remaining_timeout_ms(),
                    )
                    if a_db_info.id == id
                ]
                if detected_databases:
                    last_status_seen = detected_databases[0].status
                    _db_name = detected_databases[0].info.name
                else:
                    last_status_seen = None
            if last_status_seen is not None:
                _name_desc = f" ({_db_name})" if _db_name else ""
                raise DevOpsAPIException(
                    f"Database {id}{_name_desc} entered unexpected status {last_status_seen} after PENDING"
                )
        logger.info(f"finished dropping database '{id}', async")
        return {"ok": 1}
    else:
        raise DevOpsAPIException(
            f"Could not issue a successful terminate-database DevOps API request for {id}."
        )

async def async_list_databases(self, *, max_time_ms: Optional[int] = None) ‑> CommandCursor[AdminDatabaseInfo]

Get the list of databases, as obtained with a request to the DevOps API. Async version of the method, for use in an asyncio context.

Args

max_time_ms

a timeout, in milliseconds, for the API request.

Returns

A CommandCursor to iterate over the detected databases, represented as AdminDatabaseInfo objects. Note that the return type is not an awaitable, rather a regular iterable, e.g. for use in ordinary "for" loops.

Example

>>> async def check_if_db_exists(db_id: str) -> bool:
...     db_cursor = await my_astra_db_admin.async_list_databases()
...     db_list = list(dd_cursor)
...     return db_id in db_list
...
>>> asyncio.run(check_if_db_exists("xyz"))
True
>>> asyncio.run(check_if_db_exists("01234567-..."))
False

Expand source code

@ops_recast_method_async
async def async_list_databases(
    self,
    *,
    max_time_ms: Optional[int] = None,
) -> CommandCursor[AdminDatabaseInfo]:
    """
    Get the list of databases, as obtained with a request to the DevOps API.
    Async version of the method, for use in an asyncio context.

    Args:
        max_time_ms: a timeout, in milliseconds, for the API request.

    Returns:
        A CommandCursor to iterate over the detected databases,
        represented as AdminDatabaseInfo objects.
        Note that the return type is not an awaitable, rather
        a regular iterable, e.g. for use in ordinary "for" loops.

    Example:
        >>> async def check_if_db_exists(db_id: str) -> bool:
        ...     db_cursor = await my_astra_db_admin.async_list_databases()
        ...     db_list = list(dd_cursor)
        ...     return db_id in db_list
        ...
        >>> asyncio.run(check_if_db_exists("xyz"))
        True
        >>> asyncio.run(check_if_db_exists("01234567-..."))
        False
    """

    logger.info("getting databases, async")
    gd_list_response = await self._astra_db_ops.async_get_databases(
        timeout_info=base_timeout_info(max_time_ms)
    )
    logger.info("finished getting databases, async")
    if not isinstance(gd_list_response, list):
        raise DevOpsAPIException(
            "Faulty response from get-databases DevOps API command.",
        )
    else:
        # we know this is a list of dicts which need a little adjusting
        return CommandCursor(
            address=self._astra_db_ops.base_url,
            items=[
                _recast_as_admin_database_info(
                    db_dict,
                    environment=self.environment,
                )
                for db_dict in gd_list_response
            ],
        )

def create_database(self, name: str, *, cloud_provider: str, region: str, namespace: Optional[str] = None, wait_until_active: bool = True, max_time_ms: Optional[int] = None) ‑> AstraDBDatabaseAdmin

Create a database as requested, optionally waiting for it to be ready.

Args

name

the desired name for the database.

cloud_provider

one of 'aws', 'gcp' or 'azure'.

region

any of the available cloud regions.

namespace

name for the one namespace the database starts with. If omitted, DevOps API will use its default.

wait_until_active

if True (default), the method returns only after the newly-created database is in ACTIVE state (a few minutes, usually). If False, it will return right after issuing the creation request to the DevOps API, and it will be responsibility of the caller to check the database status before working with it.

max_time_ms

a timeout, in milliseconds, for the whole requested operation to complete. Note that a timeout is no guarantee that the creation request has not reached the API server.

Returns

An AstraDBDatabaseAdmin instance.

Example

>>> my_new_db_admin = my_astra_db_admin.create_database(
...     "new_database",
...     cloud_provider="aws",
...     region="ap-south-1",
... )
>>> my_new_db = my_new_db_admin.get_database()
>>> my_coll = my_new_db.create_collection("movies", dimension=512)
>>> my_coll.insert_one({"title": "The Title"}, vector=...)

Expand source code

@ops_recast_method_sync
def create_database(
    self,
    name: str,
    *,
    cloud_provider: str,
    region: str,
    namespace: Optional[str] = None,
    wait_until_active: bool = True,
    max_time_ms: Optional[int] = None,
) -> AstraDBDatabaseAdmin:
    """
    Create a database as requested, optionally waiting for it to be ready.

    Args:
        name: the desired name for the database.
        cloud_provider: one of 'aws', 'gcp' or 'azure'.
        region: any of the available cloud regions.
        namespace: name for the one namespace the database starts with.
            If omitted, DevOps API will use its default.
        wait_until_active: if True (default), the method returns only after
            the newly-created database is in ACTIVE state (a few minutes,
            usually). If False, it will return right after issuing the
            creation request to the DevOps API, and it will be responsibility
            of the caller to check the database status before working with it.
        max_time_ms: a timeout, in milliseconds, for the whole requested
            operation to complete.
            Note that a timeout is no guarantee that the creation request
            has not reached the API server.

    Returns:
        An AstraDBDatabaseAdmin instance.

    Example:
        >>> my_new_db_admin = my_astra_db_admin.create_database(
        ...     "new_database",
        ...     cloud_provider="aws",
        ...     region="ap-south-1",
        ... )
        >>> my_new_db = my_new_db_admin.get_database()
        >>> my_coll = my_new_db.create_collection("movies", dimension=512)
        >>> my_coll.insert_one({"title": "The Title"}, vector=...)
    """

    database_definition = {
        k: v
        for k, v in {
            "name": name,
            "tier": "serverless",
            "cloudProvider": cloud_provider,
            "region": region,
            "capacityUnits": 1,
            "dbType": "vector",
            "keyspace": namespace,
        }.items()
        if v is not None
    }
    timeout_manager = MultiCallTimeoutManager(
        overall_max_time_ms=max_time_ms, exception_type="devops_api"
    )
    logger.info(f"creating database {name}/({cloud_provider}, {region})")
    cd_response = self._astra_db_ops.create_database(
        database_definition=database_definition,
        timeout_info=base_timeout_info(max_time_ms),
    )
    logger.info(
        "devops api returned from creating database "
        f"{name}/({cloud_provider}, {region})"
    )
    if cd_response is not None and "id" in cd_response:
        new_database_id = cd_response["id"]
        if wait_until_active:
            last_status_seen = STATUS_PENDING
            while last_status_seen in {STATUS_PENDING, STATUS_INITIALIZING}:
                logger.info(f"sleeping to poll for status of '{new_database_id}'")
                time.sleep(DATABASE_POLL_SLEEP_TIME)
                last_db_info = self.database_info(
                    id=new_database_id,
                    max_time_ms=timeout_manager.remaining_timeout_ms(),
                )
                last_status_seen = last_db_info.status
            if last_status_seen != STATUS_ACTIVE:
                raise DevOpsAPIException(
                    f"Database {name} entered unexpected status {last_status_seen} after PENDING"
                )
        # return the database instance
        logger.info(
            f"finished creating database '{new_database_id}' = "
            f"{name}/({cloud_provider}, {region})"
        )
        return AstraDBDatabaseAdmin.from_astra_db_admin(
            id=new_database_id,
            astra_db_admin=self,
        )
    else:
        raise DevOpsAPIException("Could not create the database.")

def database_info(self, id: str, *, max_time_ms: Optional[int] = None) ‑> AdminDatabaseInfo

Get the full information on a given database, through a request to the DevOps API.

Args

id

the ID of the target database, e. g. "01234567-89ab-cdef-0123-456789abcdef".

max_time_ms

a timeout, in milliseconds, for the API request.

Returns

An AdminDatabaseInfo object.

Example

>>> details_of_my_db = my_astra_db_admin.database_info("01234567-...")
>>> details_of_my_db.id
'01234567-...'
>>> details_of_my_db.status
'ACTIVE'
>>> details_of_my_db.info.region
'eu-west-1'

Expand source code

@ops_recast_method_sync
def database_info(
    self, id: str, *, max_time_ms: Optional[int] = None
) -> AdminDatabaseInfo:
    """
    Get the full information on a given database, through a request to the DevOps API.

    Args:
        id: the ID of the target database, e. g.
            "01234567-89ab-cdef-0123-456789abcdef".
        max_time_ms: a timeout, in milliseconds, for the API request.

    Returns:
        An AdminDatabaseInfo object.

    Example:
        >>> details_of_my_db = my_astra_db_admin.database_info("01234567-...")
        >>> details_of_my_db.id
        '01234567-...'
        >>> details_of_my_db.status
        'ACTIVE'
        >>> details_of_my_db.info.region
        'eu-west-1'
    """

    logger.info(f"getting database info for '{id}'")
    gd_response = self._astra_db_ops.get_database(
        database=id,
        timeout_info=base_timeout_info(max_time_ms),
    )
    logger.info(f"finished getting database info for '{id}'")
    if not isinstance(gd_response, dict):
        raise DevOpsAPIException(
            "Faulty response from get-database DevOps API command.",
        )
    else:
        return _recast_as_admin_database_info(
            gd_response,
            environment=self.environment,
        )

def drop_database(self, id: str, *, wait_until_active: bool = True, max_time_ms: Optional[int] = None) ‑> Dict[str, Any]

Drop a database, i.e. delete it completely and permanently with all its data.

Args

id

The ID of the database to drop, e. g. "01234567-89ab-cdef-0123-456789abcdef".

wait_until_active

if True (default), the method returns only after the database has actually been deleted (generally a few minutes). If False, it will return right after issuing the drop request to the DevOps API, and it will be responsibility of the caller to check the database status/availability after that, if desired.

max_time_ms

a timeout, in milliseconds, for the whole requested operation to complete. Note that a timeout is no guarantee that the deletion request has not reached the API server.

Returns

A dictionary of the form {"ok": 1} in case of success. Otherwise, an exception is raised.

Example

>>> database_list_pre = my_astra_db_admin.list_databases()
>>> len(database_list_pre)
3
>>> my_astra_db_admin.drop_database("01234567-...")
{'ok': 1}
>>> database_list_post = my_astra_db_admin.list_databases()
>>> len(database_list_post)
2

Expand source code

@ops_recast_method_sync
def drop_database(
    self,
    id: str,
    *,
    wait_until_active: bool = True,
    max_time_ms: Optional[int] = None,
) -> Dict[str, Any]:
    """
    Drop a database, i.e. delete it completely and permanently with all its data.

    Args:
        id: The ID of the database to drop, e. g.
            "01234567-89ab-cdef-0123-456789abcdef".
        wait_until_active: if True (default), the method returns only after
            the database has actually been deleted (generally a few minutes).
            If False, it will return right after issuing the
            drop request to the DevOps API, and it will be responsibility
            of the caller to check the database status/availability
            after that, if desired.
        max_time_ms: a timeout, in milliseconds, for the whole requested
            operation to complete.
            Note that a timeout is no guarantee that the deletion request
            has not reached the API server.

    Returns:
        A dictionary of the form {"ok": 1} in case of success.
        Otherwise, an exception is raised.

    Example:
        >>> database_list_pre = my_astra_db_admin.list_databases()
        >>> len(database_list_pre)
        3
        >>> my_astra_db_admin.drop_database("01234567-...")
        {'ok': 1}
        >>> database_list_post = my_astra_db_admin.list_databases()
        >>> len(database_list_post)
        2
    """

    timeout_manager = MultiCallTimeoutManager(
        overall_max_time_ms=max_time_ms, exception_type="devops_api"
    )
    logger.info(f"dropping database '{id}'")
    te_response = self._astra_db_ops.terminate_database(
        database=id,
        timeout_info=base_timeout_info(max_time_ms),
    )
    logger.info(f"devops api returned from dropping database '{id}'")
    if te_response == id:
        if wait_until_active:
            last_status_seen: Optional[str] = STATUS_TERMINATING
            _db_name: Optional[str] = None
            while last_status_seen == STATUS_TERMINATING:
                logger.info(f"sleeping to poll for status of '{id}'")
                time.sleep(DATABASE_POLL_SLEEP_TIME)
                #
                detected_databases = [
                    a_db_info
                    for a_db_info in self.list_databases(
                        max_time_ms=timeout_manager.remaining_timeout_ms(),
                    )
                    if a_db_info.id == id
                ]
                if detected_databases:
                    last_status_seen = detected_databases[0].status
                    _db_name = detected_databases[0].info.name
                else:
                    last_status_seen = None
            if last_status_seen is not None:
                _name_desc = f" ({_db_name})" if _db_name else ""
                raise DevOpsAPIException(
                    f"Database {id}{_name_desc} entered unexpected status {last_status_seen} after PENDING"
                )
        logger.info(f"finished dropping database '{id}'")
        return {"ok": 1}
    else:
        raise DevOpsAPIException(
            f"Could not issue a successful terminate-database DevOps API request for {id}."
        )

def get_async_database(self, id: str, *, token: Optional[str] = None, namespace: Optional[str] = None, region: Optional[str] = None, api_path: Optional[str] = None, api_version: Optional[str] = None) ‑> AsyncDatabase

Create an AsyncDatabase instance for a specific database, to be used when doing data-level work (such as creating/managing collections).

This method has identical behavior and signature as the sync counterpart get_database: please see that one for more details.

Expand source code

def get_async_database(
    self,
    id: str,
    *,
    token: Optional[str] = None,
    namespace: Optional[str] = None,
    region: Optional[str] = None,
    api_path: Optional[str] = None,
    api_version: Optional[str] = None,
) -> AsyncDatabase:
    """
    Create an AsyncDatabase instance for a specific database, to be used
    when doing data-level work (such as creating/managing collections).

    This method has identical behavior and signature as the sync
    counterpart `get_database`: please see that one for more details.
    """

    return self.get_database(
        id=id,
        token=token,
        namespace=namespace,
        region=region,
        api_path=api_path,
        api_version=api_version,
    ).to_async()

def get_database(self, id: str, *, token: Optional[str] = None, namespace: Optional[str] = None, region: Optional[str] = None, api_path: Optional[str] = None, api_version: Optional[str] = None, max_time_ms: Optional[int] = None) ‑> Database

Create a Database instance for a specific database, to be used when doing data-level work (such as creating/managing collections).

Args

id

e. g. "01234567-89ab-cdef-0123-456789abcdef".

token

if supplied, is passed to the Database instead of the one set for this object.

namespace

used to specify a certain namespace the resulting Database will primarily work on. If not specified, similar as for region, an additional DevOps API call reveals the default namespace for the target database.

region

the region to use for connecting to the database. The database must be located in that region. Note that if this parameter is not passed, an additional DevOps API request is made to determine the default region and use it subsequently. If both namespace and region are missing, a single DevOps API request is made.

api_path

path to append to the API Endpoint. In typical usage, this should be left to its default of "/api/json".

api_version

version specifier to append to the API path. In typical usage, this should be left to its default of "v1".

max_time_ms

a timeout, in milliseconds, for the DevOps API HTTP request should it be necessary (see the region argument).

Returns

A Database object ready to be used.

Example

>>> my_db = my_astra_db_admin.get_database(
...     "01234567-...",
...     region="us-east1",
... )
>>> coll = my_db.create_collection("movies", dimension=512)
>>> my_coll.insert_one({"title": "The Title"}, vector=...)

Note

This method does not perform any admin-level operation through the DevOps API. For actual creation of a database, see the create_database method of class AstraDBAdmin.

Expand source code

def get_database(
    self,
    id: str,
    *,
    token: Optional[str] = None,
    namespace: Optional[str] = None,
    region: Optional[str] = None,
    api_path: Optional[str] = None,
    api_version: Optional[str] = None,
    max_time_ms: Optional[int] = None,
) -> Database:
    """
    Create a Database instance for a specific database, to be used
    when doing data-level work (such as creating/managing collections).

    Args:
        id: e. g. "01234567-89ab-cdef-0123-456789abcdef".
        token: if supplied, is passed to the Database instead of
            the one set for this object.
        namespace: used to specify a certain namespace the resulting
            Database will primarily work on. If not specified, similar
            as for `region`, an additional DevOps API call reveals
            the default namespace for the target database.
        region: the region to use for connecting to the database. The
            database must be located in that region.
            Note that if this parameter is not passed, an additional
            DevOps API request is made to determine the default region
            and use it subsequently.
            If both `namespace` and `region` are missing, a single
            DevOps API request is made.
        api_path: path to append to the API Endpoint. In typical usage, this
            should be left to its default of "/api/json".
        api_version: version specifier to append to the API path. In typical
            usage, this should be left to its default of "v1".
        max_time_ms: a timeout, in milliseconds, for the DevOps API
            HTTP request should it be necessary (see the `region` argument).

    Returns:
        A Database object ready to be used.

    Example:
        >>> my_db = my_astra_db_admin.get_database(
        ...     "01234567-...",
        ...     region="us-east1",
        ... )
        >>> coll = my_db.create_collection("movies", dimension=512)
        >>> my_coll.insert_one({"title": "The Title"}, vector=...)

    Note:
        This method does not perform any admin-level operation through
        the DevOps API. For actual creation of a database, see the
        `create_database` method of class AstraDBAdmin.
    """

    # lazy importing here to avoid circular dependency
    from astrapy import Database

    # need to inspect for values?
    this_db_info: Optional[AdminDatabaseInfo] = None
    # handle overrides
    _token = token or self.token
    if namespace:
        _namespace = namespace
    else:
        if this_db_info is None:
            this_db_info = self.database_info(id, max_time_ms=max_time_ms)
        _namespace = this_db_info.info.namespace
    if region:
        _region = region
    else:
        if this_db_info is None:
            this_db_info = self.database_info(id, max_time_ms=max_time_ms)
        _region = this_db_info.info.region

    _api_endpoint = build_api_endpoint(
        environment=self.environment,
        database_id=id,
        region=_region,
    )
    return Database(
        api_endpoint=_api_endpoint,
        token=_token,
        namespace=_namespace,
        caller_name=self._caller_name,
        caller_version=self._caller_version,
        api_path=api_path,
        api_version=api_version,
    )

def get_database_admin(self, id: str) ‑> AstraDBDatabaseAdmin

Create an AstraDBDatabaseAdmin object for admin work within a certain database.

Args

id

the ID of the target database, e. g. "01234567-89ab-cdef-0123-456789abcdef".

Returns

An AstraDBDatabaseAdmin instance representing the requested database.

Example

>>> my_db_admin = my_astra_db_admin.get_database_admin("01234567-...")
>>> my_db_admin.list_namespaces()
['default_keyspace']
>>> my_db_admin.create_namespace("that_other_one")
{'ok': 1}
>>> my_db_admin.list_namespaces()
['default_keyspace', 'that_other_one']

Note

This method does not perform any admin-level operation through the DevOps API. For actual creation of a database, see the create_database method.

Expand source code

def get_database_admin(self, id: str) -> AstraDBDatabaseAdmin:
    """
    Create an AstraDBDatabaseAdmin object for admin work within a certain database.

    Args:
        id: the ID of the target database, e. g. "01234567-89ab-cdef-0123-456789abcdef".

    Returns:
        An AstraDBDatabaseAdmin instance representing the requested database.

    Example:
        >>> my_db_admin = my_astra_db_admin.get_database_admin("01234567-...")
        >>> my_db_admin.list_namespaces()
        ['default_keyspace']
        >>> my_db_admin.create_namespace("that_other_one")
        {'ok': 1}
        >>> my_db_admin.list_namespaces()
        ['default_keyspace', 'that_other_one']

    Note:
        This method does not perform any admin-level operation through
        the DevOps API. For actual creation of a database, see the
        `create_database` method.
    """

    return AstraDBDatabaseAdmin.from_astra_db_admin(
        id=id,
        astra_db_admin=self,
    )

def list_databases(self, *, max_time_ms: Optional[int] = None) ‑> CommandCursor[AdminDatabaseInfo]

Get the list of databases, as obtained with a request to the DevOps API.

Args

max_time_ms

a timeout, in milliseconds, for the API request.

Returns

A CommandCursor to iterate over the detected databases, represented as AdminDatabaseInfo objects.

Example

>>> database_cursor = my_astra_db_admin.list_databases()
>>> database_list = list(database_cursor)
>>> len(database_list)
3
>>> database_list[2].id
'01234567-...'
>>> database_list[2].status
'ACTIVE'
>>> database_list[2].info.region
'eu-west-1'

Expand source code

@ops_recast_method_sync
def list_databases(
    self,
    *,
    max_time_ms: Optional[int] = None,
) -> CommandCursor[AdminDatabaseInfo]:
    """
    Get the list of databases, as obtained with a request to the DevOps API.

    Args:
        max_time_ms: a timeout, in milliseconds, for the API request.

    Returns:
        A CommandCursor to iterate over the detected databases,
        represented as AdminDatabaseInfo objects.

    Example:
        >>> database_cursor = my_astra_db_admin.list_databases()
        >>> database_list = list(database_cursor)
        >>> len(database_list)
        3
        >>> database_list[2].id
        '01234567-...'
        >>> database_list[2].status
        'ACTIVE'
        >>> database_list[2].info.region
        'eu-west-1'
    """

    logger.info("getting databases")
    gd_list_response = self._astra_db_ops.get_databases(
        timeout_info=base_timeout_info(max_time_ms)
    )
    logger.info("finished getting databases")
    if not isinstance(gd_list_response, list):
        raise DevOpsAPIException(
            "Faulty response from get-databases DevOps API command.",
        )
    else:
        # we know this is a list of dicts which need a little adjusting
        return CommandCursor(
            address=self._astra_db_ops.base_url,
            items=[
                _recast_as_admin_database_info(
                    db_dict,
                    environment=self.environment,
                )
                for db_dict in gd_list_response
            ],
        )

def set_caller(self, caller_name: Optional[str] = None, caller_version: Optional[str] = None) ‑> None

Set a new identity for the application/framework on behalf of which the DevOps API calls will be performed (the "caller").

New objects spawned from this client afterwards will inherit the new settings.

Args

caller_name

name of the application, or framework, on behalf of which the DevOps API calls are performed. This ends up in the request user-agent.

caller_version

version of the caller.

Example

>>> my_astra_db_admin.set_caller(
...     caller_name="the_caller",
...     caller_version="0.1.0",
... )

Expand source code

def set_caller(
    self,
    caller_name: Optional[str] = None,
    caller_version: Optional[str] = None,
) -> None:
    """
    Set a new identity for the application/framework on behalf of which
    the DevOps API calls will be performed (the "caller").

    New objects spawned from this client afterwards will inherit the new settings.

    Args:
        caller_name: name of the application, or framework, on behalf of which
            the DevOps API calls are performed. This ends up in the request user-agent.
        caller_version: version of the caller.

    Example:
        >>> my_astra_db_admin.set_caller(
        ...     caller_name="the_caller",
        ...     caller_version="0.1.0",
        ... )
    """

    logger.info(f"setting caller to {caller_name}/{caller_version}")
    self._caller_name = caller_name
    self._caller_version = caller_version
    self._astra_db_ops.set_caller(caller_name, caller_version)

def with_options(self, *, token: Optional[str] = None, caller_name: Optional[str] = None, caller_version: Optional[str] = None) ‑> AstraDBAdmin

Create a clone of this AstraDBAdmin with some changed attributes.

Args

token

an Access Token to the database. Example: "AstraCS:xyz...".

caller_name

name of the application, or framework, on behalf of which the Data API and DevOps API calls are performed. This ends up in the request user-agent.

caller_version

version of the caller.

Returns

a new AstraDBAdmin instance.

Example

>>> another_astra_db_admin = my_astra_db_admin.with_options(
...     caller_name="caller_identity",
...     caller_version="1.2.0",
... )

Expand source code

def with_options(
    self,
    *,
    token: Optional[str] = None,
    caller_name: Optional[str] = None,
    caller_version: Optional[str] = None,
) -> AstraDBAdmin:
    """
    Create a clone of this AstraDBAdmin with some changed attributes.

    Args:
        token: an Access Token to the database. Example: `"AstraCS:xyz..."`.
        caller_name: name of the application, or framework, on behalf of which
            the Data API and DevOps API calls are performed. This ends up in
            the request user-agent.
        caller_version: version of the caller.

    Returns:
        a new AstraDBAdmin instance.

    Example:
        >>> another_astra_db_admin = my_astra_db_admin.with_options(
        ...     caller_name="caller_identity",
        ...     caller_version="1.2.0",
        ... )
    """

    return self._copy(
        token=token,
        caller_name=caller_name,
        caller_version=caller_version,
    )

>>> from astrapy import DataAPIClient
>>> my_client = DataAPIClient("AstraCS:...")
>>> admin_for_my_db = my_client.get_admin().get_database_admin("01234567-...")
>>> admin_for_my_db.list_namespaces()
['default_keyspace', 'staging_namespace']
>>> admin_for_my_db.info().status
'ACTIVE'

class AstraDBDatabaseAdmin(DatabaseAdmin):
    """
    An "admin" object, able to perform administrative tasks at the namespaces level
    (i.e. within a certani database), such as creating/listing/dropping namespaces.

    This is one layer below the AstraDBAdmin concept, in that it is tied to
    a single database and enables admin work within it. As such, it is generally
    created by a method call on an AstraDBAdmin.

    Args:
        id: e. g. "01234567-89ab-cdef-0123-456789abcdef".
        token: an access token with enough permission to perform admin tasks.
        environment: a label, whose value is one of Environment.PROD (default),
            Environment.DEV or Environment.TEST.
        caller_name: name of the application, or framework, on behalf of which
            the DevOps API calls are performed. This ends up in the request user-agent.
        caller_version: version of the caller.
        dev_ops_url: in case of custom deployments, this can be used to specify
            the URL to the DevOps API, such as "https://api.astra.datastax.com".
            Generally it can be omitted. The environment (prod/dev/...) is
            determined from the API Endpoint.
        dev_ops_api_version: this can specify a custom version of the DevOps API
            (such as "v2"). Generally not needed.

    Example:
        >>> from astrapy import DataAPIClient
        >>> my_client = DataAPIClient("AstraCS:...")
        >>> admin_for_my_db = my_client.get_admin().get_database_admin("01234567-...")
        >>> admin_for_my_db.list_namespaces()
        ['default_keyspace', 'staging_namespace']
        >>> admin_for_my_db.info().status
        'ACTIVE'

    Note:
        creating an instance of AstraDBDatabaseAdmin does not trigger actual creation
        of the database itself, which should exist beforehand. To create databases,
        see the AstraDBAdmin class.
    """

    def __init__(
        self,
        id: str,
        *,
        token: str,
        environment: Optional[str] = None,
        caller_name: Optional[str] = None,
        caller_version: Optional[str] = None,
        dev_ops_url: Optional[str] = None,
        dev_ops_api_version: Optional[str] = None,
    ) -> None:
        self.id = id
        self.token = token
        self.environment = environment or Environment.PROD
        self._astra_db_admin = AstraDBAdmin(
            token=self.token,
            environment=self.environment,
            caller_name=caller_name,
            caller_version=caller_version,
            dev_ops_url=dev_ops_url,
            dev_ops_api_version=dev_ops_api_version,
        )

    def __repr__(self) -> str:
        env_desc: str
        if self.environment == Environment.PROD:
            env_desc = ""
        else:
            env_desc = f', environment="{self.environment}"'
        return (
            f'{self.__class__.__name__}(id="{self.id}", '
            f'"{self.token[:12]}..."{env_desc})'
        )

    def __eq__(self, other: Any) -> bool:
        if isinstance(other, AstraDBDatabaseAdmin):
            return all(
                [
                    self.id == other.id,
                    self.token == other.token,
                    self.environment == other.environment,
                    self._astra_db_admin == other._astra_db_admin,
                ]
            )
        else:
            return False

    def _copy(
        self,
        id: Optional[str] = None,
        token: Optional[str] = None,
        environment: Optional[str] = None,
        caller_name: Optional[str] = None,
        caller_version: Optional[str] = None,
        dev_ops_url: Optional[str] = None,
        dev_ops_api_version: Optional[str] = None,
    ) -> AstraDBDatabaseAdmin:
        return AstraDBDatabaseAdmin(
            id=id or self.id,
            token=token or self.token,
            environment=environment or self.environment,
            caller_name=caller_name or self._astra_db_admin._caller_name,
            caller_version=caller_version or self._astra_db_admin._caller_version,
            dev_ops_url=dev_ops_url or self._astra_db_admin._dev_ops_url,
            dev_ops_api_version=dev_ops_api_version
            or self._astra_db_admin._dev_ops_api_version,
        )

    def with_options(
        self,
        *,
        id: Optional[str] = None,
        token: Optional[str] = None,
        caller_name: Optional[str] = None,
        caller_version: Optional[str] = None,
    ) -> AstraDBDatabaseAdmin:
        """
        Create a clone of this AstraDBDatabaseAdmin with some changed attributes.

        Args:
            id: e. g. "01234567-89ab-cdef-0123-456789abcdef".
            token: an Access Token to the database. Example: `"AstraCS:xyz..."`.
            caller_name: name of the application, or framework, on behalf of which
                the Data API and DevOps API calls are performed. This ends up in
                the request user-agent.
            caller_version: version of the caller.

        Returns:
            a new AstraDBDatabaseAdmin instance.

        Example:
            >>> admin_for_my_other_db = admin_for_my_db.with_options(
            ...     id="abababab-0101-2323-4545-6789abcdef01",
            ... )
        """

        return self._copy(
            id=id,
            token=token,
            caller_name=caller_name,
            caller_version=caller_version,
        )

    def set_caller(
        self,
        caller_name: Optional[str] = None,
        caller_version: Optional[str] = None,
    ) -> None:
        """
        Set a new identity for the application/framework on behalf of which
        the DevOps API calls will be performed (the "caller").

        New objects spawned from this client afterwards will inherit the new settings.

        Args:
            caller_name: name of the application, or framework, on behalf of which
                the DevOps API calls are performed. This ends up in the request user-agent.
            caller_version: version of the caller.

        Example:
            >>> admin_for_my_db.set_caller(
            ...     caller_name="the_caller",
            ...     caller_version="0.1.0",
            ... )
        """

        logger.info(f"setting caller to {caller_name}/{caller_version}")
        self._astra_db_admin.set_caller(caller_name, caller_version)

    @staticmethod
    def from_astra_db_admin(
        id: str, *, astra_db_admin: AstraDBAdmin
    ) -> AstraDBDatabaseAdmin:
        """
        Create an AstraDBDatabaseAdmin from an AstraDBAdmin and a database ID.

        Args:
            id: e. g. "01234567-89ab-cdef-0123-456789abcdef".
            astra_db_admin: an AstraDBAdmin object that has visibility over
                the target database.

        Returns:
            An AstraDBDatabaseAdmin object, for admin work within the database.

        Example:
            >>> from astrapy import DataAPIClient, AstraDBDatabaseAdmin
            >>> admin_for_my_db = AstraDBDatabaseAdmin.from_astra_db_admin(
            ...     id="01234567-...",
            ...     astra_db_admin=DataAPIClient("AstraCS:...").get_admin(),
            ... )
            >>> admin_for_my_db.list_namespaces()
            ['default_keyspace', 'staging_namespace']
            >>> admin_for_my_db.info().status
            'ACTIVE'

        Note:
            Creating an instance of AstraDBDatabaseAdmin does not trigger actual creation
            of the database itself, which should exist beforehand. To create databases,
            see the AstraDBAdmin class.
        """

        return AstraDBDatabaseAdmin(
            id=id,
            token=astra_db_admin.token,
            environment=astra_db_admin.environment,
            caller_name=astra_db_admin._caller_name,
            caller_version=astra_db_admin._caller_version,
            dev_ops_url=astra_db_admin._dev_ops_url,
            dev_ops_api_version=astra_db_admin._dev_ops_api_version,
        )

    @staticmethod
    def from_api_endpoint(
        api_endpoint: str,
        *,
        token: str,
        caller_name: Optional[str] = None,
        caller_version: Optional[str] = None,
        dev_ops_url: Optional[str] = None,
        dev_ops_api_version: Optional[str] = None,
    ) -> AstraDBDatabaseAdmin:
        """
        Create an AstraDBDatabaseAdmin from an API Endpoint and optionally a token.

        Args:
            api_endpoint: a full API endpoint for the Data Api.
            token: an access token with enough permissions to do admin work.
            caller_name: name of the application, or framework, on behalf of which
                the DevOps API calls are performed. This ends up in the request user-agent.
            caller_version: version of the caller.
            dev_ops_url: in case of custom deployments, this can be used to specify
                the URL to the DevOps API, such as "https://api.astra.datastax.com".
                Generally it can be omitted. The environment (prod/dev/...) is
                determined from the API Endpoint.
            dev_ops_api_version: this can specify a custom version of the DevOps API
                (such as "v2"). Generally not needed.

        Returns:
            An AstraDBDatabaseAdmin object, for admin work within the database.

        Example:
            >>> from astrapy import AstraDBDatabaseAdmin
            >>> admin_for_my_db = AstraDBDatabaseAdmin.from_api_endpoint(
            ...     api_endpoint="https://01234567-....apps.astra.datastax.com",
            ...     token="AstraCS:...",
            ... )
            >>> admin_for_my_db.list_namespaces()
            ['default_keyspace', 'another_namespace']
            >>> admin_for_my_db.info().status
            'ACTIVE'

        Note:
            Creating an instance of AstraDBDatabaseAdmin does not trigger actual creation
            of the database itself, which should exist beforehand. To create databases,
            see the AstraDBAdmin class.
        """

        parsed_api_endpoint = parse_api_endpoint(api_endpoint)
        if parsed_api_endpoint:
            return AstraDBDatabaseAdmin(
                id=parsed_api_endpoint.database_id,
                token=token,
                environment=parsed_api_endpoint.environment,
                caller_name=caller_name,
                caller_version=caller_version,
                dev_ops_url=dev_ops_url,
                dev_ops_api_version=dev_ops_api_version,
            )
        else:
            raise ValueError("Cannot parse the provided API endpoint.")

    def info(self, *, max_time_ms: Optional[int] = None) -> AdminDatabaseInfo:
        """
        Query the DevOps API for the full info on this database.

        Args:
            max_time_ms: a timeout, in milliseconds, for the DevOps API request.

        Returns:
            An AdminDatabaseInfo object.

        Example:
            >>> my_db_info = admin_for_my_db.info()
            >>> my_db_info.status
            'ACTIVE'
            >>> my_db_info.info.region
            'us-east1'
        """

        logger.info(f"getting info ('{self.id}')")
        req_response = self._astra_db_admin.database_info(
            id=self.id,
            max_time_ms=max_time_ms,
        )
        logger.info(f"finished getting info ('{self.id}')")
        return req_response  # type: ignore[no-any-return]

    async def async_info(
        self, *, max_time_ms: Optional[int] = None
    ) -> AdminDatabaseInfo:
        """
        Query the DevOps API for the full info on this database.
        Async version of the method, for use in an asyncio context.

        Args:
            max_time_ms: a timeout, in milliseconds, for the DevOps API request.

        Returns:
            An AdminDatabaseInfo object.

        Example:
            >>> async def wait_until_active(db_admin: AstraDBDatabaseAdmin) -> None:
            ...     while True:
            ...         info = await db_admin.async_info()
            ...         if info.status == "ACTIVE":
            ...             return
            ...
            >>> asyncio.run(wait_until_active(admin_for_my_db))
        """

        logger.info(f"getting info ('{self.id}'), async")
        req_response = await self._astra_db_admin.async_database_info(
            id=self.id,
            max_time_ms=max_time_ms,
        )
        logger.info(f"finished getting info ('{self.id}'), async")
        return req_response  # type: ignore[no-any-return]

    def list_namespaces(self, *, max_time_ms: Optional[int] = None) -> List[str]:
        """
        Query the DevOps API for a list of the namespaces in the database.

        Args:
            max_time_ms: a timeout, in milliseconds, for the DevOps API request.

        Returns:
            A list of the namespaces, each a string, in no particular order.

        Example:
            >>> admin_for_my_db.list_namespaces()
            ['default_keyspace', 'staging_namespace']
        """

        logger.info(f"getting namespaces ('{self.id}')")
        info = self.info(max_time_ms=max_time_ms)
        logger.info(f"finished getting namespaces ('{self.id}')")
        if info.raw_info is None:
            raise DevOpsAPIException("Could not get the namespace list.")
        else:
            return info.raw_info["info"]["keyspaces"]  # type: ignore[no-any-return]

    async def async_list_namespaces(
        self, *, max_time_ms: Optional[int] = None
    ) -> List[str]:
        """
        Query the DevOps API for a list of the namespaces in the database.
        Async version of the method, for use in an asyncio context.

        Args:
            max_time_ms: a timeout, in milliseconds, for the DevOps API request.

        Returns:
            A list of the namespaces, each a string, in no particular order.

        Example:
            >>> async def check_if_ns_exists(
            ...     db_admin: AstraDBDatabaseAdmin, namespace: str
            ... ) -> bool:
            ...     ns_list = await db_admin.async_list_namespaces()
            ...     return namespace in ns_list
            ...
            >>> asyncio.run(check_if_ns_exists(admin_for_my_db, "dragons"))
            False
            >>> asyncio.run(check_if_db_exists(admin_for_my_db, "app_namespace"))
            True
        """

        logger.info(f"getting namespaces ('{self.id}'), async")
        info = await self.async_info(max_time_ms=max_time_ms)
        logger.info(f"finished getting namespaces ('{self.id}'), async")
        if info.raw_info is None:
            raise DevOpsAPIException("Could not get the namespace list.")
        else:
            return info.raw_info["info"]["keyspaces"]  # type: ignore[no-any-return]

    @ops_recast_method_sync
    def create_namespace(
        self,
        name: str,
        *,
        wait_until_active: bool = True,
        max_time_ms: Optional[int] = None,
    ) -> Dict[str, Any]:
        """
        Create a namespace in this database as requested,
        optionally waiting for it to be ready.

        Args:
            name: the namespace name. If supplying a namespace that exists
                already, the method call proceeds as usual, no errors are
                raised, and the whole invocation is a no-op.
            wait_until_active: if True (default), the method returns only after
                the target database is in ACTIVE state again (a few
                seconds, usually). If False, it will return right after issuing the
                creation request to the DevOps API, and it will be responsibility
                of the caller to check the database status/namespace availability
                before working with it.
            max_time_ms: a timeout, in milliseconds, for the whole requested
                operation to complete.
                Note that a timeout is no guarantee that the creation request
                has not reached the API server.

        Returns:
            A dictionary of the form {"ok": 1} in case of success.
            Otherwise, an exception is raised.

        Example:
            >>> my_db_admin.list_namespaces()
            ['default_keyspace']
            >>> my_db_admin.create_namespace("that_other_one")
            {'ok': 1}
            >>> my_db_admin.list_namespaces()
            ['default_keyspace', 'that_other_one']
        """

        timeout_manager = MultiCallTimeoutManager(
            overall_max_time_ms=max_time_ms, exception_type="devops_api"
        )
        logger.info(f"creating namespace '{name}' on '{self.id}'")
        cn_response = self._astra_db_admin._astra_db_ops.create_keyspace(
            database=self.id,
            keyspace=name,
            timeout_info=base_timeout_info(max_time_ms),
        )
        logger.info(
            f"devops api returned from creating namespace '{name}' on '{self.id}'"
        )
        if cn_response is not None and name == cn_response.get("name"):
            if wait_until_active:
                last_status_seen = STATUS_MAINTENANCE
                while last_status_seen == STATUS_MAINTENANCE:
                    logger.info(f"sleeping to poll for status of '{self.id}'")
                    time.sleep(DATABASE_POLL_NAMESPACE_SLEEP_TIME)
                    last_status_seen = self.info(
                        max_time_ms=timeout_manager.remaining_timeout_ms(),
                    ).status
                if last_status_seen != STATUS_ACTIVE:
                    raise DevOpsAPIException(
                        f"Database entered unexpected status {last_status_seen} after MAINTENANCE."
                    )
                # is the namespace found?
                if name not in self.list_namespaces():
                    raise DevOpsAPIException("Could not create the namespace.")
            logger.info(f"finished creating namespace '{name}' on '{self.id}'")
            return {"ok": 1}
        else:
            raise DevOpsAPIException(
                f"Could not issue a successful create-namespace DevOps API request for {name}."
            )

    # the 'override' is because the error-recast decorator washes out the signature
    @ops_recast_method_async
    async def async_create_namespace(  # type: ignore[override]
        self,
        name: str,
        *,
        wait_until_active: bool = True,
        max_time_ms: Optional[int] = None,
    ) -> Dict[str, Any]:
        """
        Create a namespace in this database as requested,
        optionally waiting for it to be ready.
        Async version of the method, for use in an asyncio context.

        Args:
            name: the namespace name. If supplying a namespace that exists
                already, the method call proceeds as usual, no errors are
                raised, and the whole invocation is a no-op.
            wait_until_active: if True (default), the method returns only after
                the target database is in ACTIVE state again (a few
                seconds, usually). If False, it will return right after issuing the
                creation request to the DevOps API, and it will be responsibility
                of the caller to check the database status/namespace availability
                before working with it.
            max_time_ms: a timeout, in milliseconds, for the whole requested
                operation to complete.
                Note that a timeout is no guarantee that the creation request
                has not reached the API server.

        Returns:
            A dictionary of the form {"ok": 1} in case of success.
            Otherwise, an exception is raised.

        Example:
            >>> asyncio.run(
            ...     my_db_admin.async_create_namespace("app_namespace")
            ... )
            {'ok': 1}
        """

        timeout_manager = MultiCallTimeoutManager(
            overall_max_time_ms=max_time_ms, exception_type="devops_api"
        )
        logger.info(f"creating namespace '{name}' on '{self.id}', async")
        cn_response = await self._astra_db_admin._astra_db_ops.async_create_keyspace(
            database=self.id,
            keyspace=name,
            timeout_info=base_timeout_info(max_time_ms),
        )
        logger.info(
            f"devops api returned from creating namespace "
            f"'{name}' on '{self.id}', async"
        )
        if cn_response is not None and name == cn_response.get("name"):
            if wait_until_active:
                last_status_seen = STATUS_MAINTENANCE
                while last_status_seen == STATUS_MAINTENANCE:
                    logger.info(f"sleeping to poll for status of '{self.id}', async")
                    await asyncio.sleep(DATABASE_POLL_NAMESPACE_SLEEP_TIME)
                    last_db_info = await self.async_info(
                        max_time_ms=timeout_manager.remaining_timeout_ms(),
                    )
                    last_status_seen = last_db_info.status
                if last_status_seen != STATUS_ACTIVE:
                    raise DevOpsAPIException(
                        f"Database entered unexpected status {last_status_seen} after MAINTENANCE."
                    )
                # is the namespace found?
                if name not in await self.async_list_namespaces():
                    raise DevOpsAPIException("Could not create the namespace.")
            logger.info(f"finished creating namespace '{name}' on '{self.id}', async")
            return {"ok": 1}
        else:
            raise DevOpsAPIException(
                f"Could not issue a successful create-namespace DevOps API request for {name}."
            )

    @ops_recast_method_sync
    def drop_namespace(
        self,
        name: str,
        *,
        wait_until_active: bool = True,
        max_time_ms: Optional[int] = None,
    ) -> Dict[str, Any]:
        """
        Delete a namespace from the database, optionally waiting for it
        to become active again.

        Args:
            name: the namespace to delete. If it does not exist in this database,
                an error is raised.
            wait_until_active: if True (default), the method returns only after
                the target database is in ACTIVE state again (a few
                seconds, usually). If False, it will return right after issuing the
                deletion request to the DevOps API, and it will be responsibility
                of the caller to check the database status/namespace availability
                before working with it.
            max_time_ms: a timeout, in milliseconds, for the whole requested
                operation to complete.
                Note that a timeout is no guarantee that the deletion request
                has not reached the API server.

        Returns:
            A dictionary of the form {"ok": 1} in case of success.
            Otherwise, an exception is raised.

        Example:
            >>> my_db_admin.list_namespaces()
            ['default_keyspace', 'that_other_one']
            >>> my_db_admin.drop_namespace("that_other_one")
            {'ok': 1}
            >>> my_db_admin.list_namespaces()
            ['default_keyspace']
        """

        timeout_manager = MultiCallTimeoutManager(
            overall_max_time_ms=max_time_ms, exception_type="devops_api"
        )
        logger.info(f"dropping namespace '{name}' on '{self.id}'")
        dk_response = self._astra_db_admin._astra_db_ops.delete_keyspace(
            database=self.id,
            keyspace=name,
            timeout_info=base_timeout_info(max_time_ms),
        )
        logger.info(
            f"devops api returned from dropping namespace '{name}' on '{self.id}'"
        )
        if dk_response == name:
            if wait_until_active:
                last_status_seen = STATUS_MAINTENANCE
                while last_status_seen == STATUS_MAINTENANCE:
                    logger.info(f"sleeping to poll for status of '{self.id}'")
                    time.sleep(DATABASE_POLL_NAMESPACE_SLEEP_TIME)
                    last_status_seen = self.info(
                        max_time_ms=timeout_manager.remaining_timeout_ms(),
                    ).status
                if last_status_seen != STATUS_ACTIVE:
                    raise DevOpsAPIException(
                        f"Database entered unexpected status {last_status_seen} after MAINTENANCE."
                    )
                # is the namespace found?
                if name in self.list_namespaces():
                    raise DevOpsAPIException("Could not drop the namespace.")
            logger.info(f"finished dropping namespace '{name}' on '{self.id}'")
            return {"ok": 1}
        else:
            raise DevOpsAPIException(
                f"Could not issue a successful delete-namespace DevOps API request for {name}."
            )

    # the 'override' is because the error-recast decorator washes out the signature
    @ops_recast_method_async
    async def async_drop_namespace(  # type: ignore[override]
        self,
        name: str,
        *,
        wait_until_active: bool = True,
        max_time_ms: Optional[int] = None,
    ) -> Dict[str, Any]:
        """
        Delete a namespace from the database, optionally waiting for it
        to become active again.
        Async version of the method, for use in an asyncio context.

        Args:
            name: the namespace to delete. If it does not exist in this database,
                an error is raised.
            wait_until_active: if True (default), the method returns only after
                the target database is in ACTIVE state again (a few
                seconds, usually). If False, it will return right after issuing the
                deletion request to the DevOps API, and it will be responsibility
                of the caller to check the database status/namespace availability
                before working with it.
            max_time_ms: a timeout, in milliseconds, for the whole requested
                operation to complete.
                Note that a timeout is no guarantee that the deletion request
                has not reached the API server.

        Returns:
            A dictionary of the form {"ok": 1} in case of success.
            Otherwise, an exception is raised.

        Example:
            >>> asyncio.run(
            ...     my_db_admin.async_drop_namespace("app_namespace")
            ... )
            {'ok': 1}
        """

        timeout_manager = MultiCallTimeoutManager(
            overall_max_time_ms=max_time_ms, exception_type="devops_api"
        )
        logger.info(f"dropping namespace '{name}' on '{self.id}', async")
        dk_response = await self._astra_db_admin._astra_db_ops.async_delete_keyspace(
            database=self.id,
            keyspace=name,
            timeout_info=base_timeout_info(max_time_ms),
        )
        logger.info(
            f"devops api returned from dropping namespace "
            f"'{name}' on '{self.id}', async"
        )
        if dk_response == name:
            if wait_until_active:
                last_status_seen = STATUS_MAINTENANCE
                while last_status_seen == STATUS_MAINTENANCE:
                    logger.info(f"sleeping to poll for status of '{self.id}', async")
                    await asyncio.sleep(DATABASE_POLL_NAMESPACE_SLEEP_TIME)
                    last_db_info = await self.async_info(
                        max_time_ms=timeout_manager.remaining_timeout_ms(),
                    )
                    last_status_seen = last_db_info.status
                if last_status_seen != STATUS_ACTIVE:
                    raise DevOpsAPIException(
                        f"Database entered unexpected status {last_status_seen} after MAINTENANCE."
                    )
                # is the namespace found?
                if name in await self.async_list_namespaces():
                    raise DevOpsAPIException("Could not drop the namespace.")
            logger.info(f"finished dropping namespace '{name}' on '{self.id}', async")
            return {"ok": 1}
        else:
            raise DevOpsAPIException(
                f"Could not issue a successful delete-namespace DevOps API request for {name}."
            )

    def drop(
        self,
        *,
        wait_until_active: bool = True,
        max_time_ms: Optional[int] = None,
    ) -> Dict[str, Any]:
        """
        Drop this database, i.e. delete it completely and permanently with all its data.

        This method wraps the `drop_database` method of the AstraDBAdmin class,
        where more information may be found.

        Args:
            wait_until_active: if True (default), the method returns only after
                the database has actually been deleted (generally a few minutes).
                If False, it will return right after issuing the
                drop request to the DevOps API, and it will be responsibility
                of the caller to check the database status/availability
                after that, if desired.
            max_time_ms: a timeout, in milliseconds, for the whole requested
                operation to complete.
                Note that a timeout is no guarantee that the deletion request
                has not reached the API server.

        Returns:
            A dictionary of the form {"ok": 1} in case of success.
            Otherwise, an exception is raised.

        Example:
            >>> my_db_admin.list_namespaces()
            ['default_keyspace', 'that_other_one']
            >>> my_db_admin.drop()
            {'ok': 1}
            >>> my_db_admin.list_namespaces()  # raises a 404 Not Found http error

        Note:
            Once the method succeeds, methods on this object -- such as `info()`,
            or `list_namespaces()` -- can still be invoked: however, this hardly
            makes sense as the underlying actual database is no more.
            It is responsibility of the developer to design a correct flow
            which avoids using a deceased database any further.
        """

        logger.info(f"dropping this database ('{self.id}')")
        return self._astra_db_admin.drop_database(  # type: ignore[no-any-return]
            id=self.id,
            wait_until_active=wait_until_active,
            max_time_ms=max_time_ms,
        )
        logger.info(f"finished dropping this database ('{self.id}')")

    async def async_drop(
        self,
        *,
        wait_until_active: bool = True,
        max_time_ms: Optional[int] = None,
    ) -> Dict[str, Any]:
        """
        Drop this database, i.e. delete it completely and permanently with all its data.
        Async version of the method, for use in an asyncio context.

        This method wraps the `drop_database` method of the AstraDBAdmin class,
        where more information may be found.

        Args:
            wait_until_active: if True (default), the method returns only after
                the database has actually been deleted (generally a few minutes).
                If False, it will return right after issuing the
                drop request to the DevOps API, and it will be responsibility
                of the caller to check the database status/availability
                after that, if desired.
            max_time_ms: a timeout, in milliseconds, for the whole requested
                operation to complete.
                Note that a timeout is no guarantee that the deletion request
                has not reached the API server.

        Returns:
            A dictionary of the form {"ok": 1} in case of success.
            Otherwise, an exception is raised.

        Example:
            >>> asyncio.run(my_db_admin.async_drop())
            {'ok': 1}

        Note:
            Once the method succeeds, methods on this object -- such as `info()`,
            or `list_namespaces()` -- can still be invoked: however, this hardly
            makes sense as the underlying actual database is no more.
            It is responsibility of the developer to design a correct flow
            which avoids using a deceased database any further.
        """

        logger.info(f"dropping this database ('{self.id}'), async")
        return await self._astra_db_admin.async_drop_database(  # type: ignore[no-any-return]
            id=self.id,
            wait_until_active=wait_until_active,
            max_time_ms=max_time_ms,
        )
        logger.info(f"finished dropping this database ('{self.id}'), async")

    def get_database(
        self,
        *,
        token: Optional[str] = None,
        namespace: Optional[str] = None,
        region: Optional[str] = None,
        api_path: Optional[str] = None,
        api_version: Optional[str] = None,
        max_time_ms: Optional[int] = None,
    ) -> Database:
        """
        Create a Database instance out of this class for working with the data in it.

        Args:
            token: if supplied, is passed to the Database instead of
                the one set for this object. Useful if one wants to work in
                a least-privilege manner, limiting the permissions for non-admin work.
            namespace: an optional namespace to set in the resulting Database.
                The same default logic as for `AstraDBAdmin.get_database` applies.
            region: an optional region for connecting to the database Data API endpoint.
                The same default logic as for `AstraDBAdmin.get_database` applies.
            api_path: path to append to the API Endpoint. In typical usage, this
                should be left to its default of "/api/json".
            api_version: version specifier to append to the API path. In typical
                usage, this should be left to its default of "v1".

        Returns:
            A Database object, ready to be used for working with data and collections.

        Example:
            >>> my_db = my_db_admin.get_database()
            >>> my_db.list_collection_names()
            ['movies', 'another_collection']

        Note:
            creating an instance of Database does not trigger actual creation
            of the database itself, which should exist beforehand. To create databases,
            see the AstraDBAdmin class.
        """

        return self._astra_db_admin.get_database(
            id=self.id,
            token=token,
            namespace=namespace,
            region=region,
            api_path=api_path,
            api_version=api_version,
            max_time_ms=max_time_ms,
        )

    def get_async_database(
        self,
        *,
        token: Optional[str] = None,
        namespace: Optional[str] = None,
        region: Optional[str] = None,
        api_path: Optional[str] = None,
        api_version: Optional[str] = None,
        max_time_ms: Optional[int] = None,
    ) -> AsyncDatabase:
        """
        Create an AsyncDatabase instance out of this class for working
        with the data in it.

        This method has identical behavior and signature as the sync
        counterpart `get_database`: please see that one for more details.
        """

        return self.get_database(
            token=token,
            namespace=namespace,
            region=region,
            api_path=api_path,
            api_version=api_version,
            max_time_ms=max_time_ms,
        ).to_async()

Ancestors

• DatabaseAdmin
• abc.ABC

Static methods

def from_api_endpoint(api_endpoint: str, *, token: str, caller_name: Optional[str] = None, caller_version: Optional[str] = None, dev_ops_url: Optional[str] = None, dev_ops_api_version: Optional[str] = None) ‑> AstraDBDatabaseAdmin

Create an AstraDBDatabaseAdmin from an API Endpoint and optionally a token.

Args

api_endpoint

a full API endpoint for the Data Api.

token

an access token with enough permissions to do admin work.

caller_name

name of the application, or framework, on behalf of which the DevOps API calls are performed. This ends up in the request user-agent.

caller_version

version of the caller.

dev_ops_url

in case of custom deployments, this can be used to specify the URL to the DevOps API, such as "https://api.astra.datastax.com". Generally it can be omitted. The environment (prod/dev/…) is determined from the API Endpoint.

dev_ops_api_version

this can specify a custom version of the DevOps API (such as "v2"). Generally not needed.

Returns

An AstraDBDatabaseAdmin object, for admin work within the database.

Example

>>> from astrapy import AstraDBDatabaseAdmin
>>> admin_for_my_db = AstraDBDatabaseAdmin.from_api_endpoint(
...     api_endpoint="https://01234567-....apps.astra.datastax.com",
...     token="AstraCS:...",
... )
>>> admin_for_my_db.list_namespaces()
['default_keyspace', 'another_namespace']
>>> admin_for_my_db.info().status
'ACTIVE'

Note

Creating an instance of AstraDBDatabaseAdmin does not trigger actual creation of the database itself, which should exist beforehand. To create databases, see the AstraDBAdmin class.

Expand source code

@staticmethod
def from_api_endpoint(
    api_endpoint: str,
    *,
    token: str,
    caller_name: Optional[str] = None,
    caller_version: Optional[str] = None,
    dev_ops_url: Optional[str] = None,
    dev_ops_api_version: Optional[str] = None,
) -> AstraDBDatabaseAdmin:
    """
    Create an AstraDBDatabaseAdmin from an API Endpoint and optionally a token.

    Args:
        api_endpoint: a full API endpoint for the Data Api.
        token: an access token with enough permissions to do admin work.
        caller_name: name of the application, or framework, on behalf of which
            the DevOps API calls are performed. This ends up in the request user-agent.
        caller_version: version of the caller.
        dev_ops_url: in case of custom deployments, this can be used to specify
            the URL to the DevOps API, such as "https://api.astra.datastax.com".
            Generally it can be omitted. The environment (prod/dev/...) is
            determined from the API Endpoint.
        dev_ops_api_version: this can specify a custom version of the DevOps API
            (such as "v2"). Generally not needed.

    Returns:
        An AstraDBDatabaseAdmin object, for admin work within the database.

    Example:
        >>> from astrapy import AstraDBDatabaseAdmin
        >>> admin_for_my_db = AstraDBDatabaseAdmin.from_api_endpoint(
        ...     api_endpoint="https://01234567-....apps.astra.datastax.com",
        ...     token="AstraCS:...",
        ... )
        >>> admin_for_my_db.list_namespaces()
        ['default_keyspace', 'another_namespace']
        >>> admin_for_my_db.info().status
        'ACTIVE'

    Note:
        Creating an instance of AstraDBDatabaseAdmin does not trigger actual creation
        of the database itself, which should exist beforehand. To create databases,
        see the AstraDBAdmin class.
    """

    parsed_api_endpoint = parse_api_endpoint(api_endpoint)
    if parsed_api_endpoint:
        return AstraDBDatabaseAdmin(
            id=parsed_api_endpoint.database_id,
            token=token,
            environment=parsed_api_endpoint.environment,
            caller_name=caller_name,
            caller_version=caller_version,
            dev_ops_url=dev_ops_url,
            dev_ops_api_version=dev_ops_api_version,
        )
    else:
        raise ValueError("Cannot parse the provided API endpoint.")

def from_astra_db_admin(id: str, *, astra_db_admin: AstraDBAdmin) ‑> AstraDBDatabaseAdmin

Create an AstraDBDatabaseAdmin from an AstraDBAdmin and a database ID.

Args

id

e. g. "01234567-89ab-cdef-0123-456789abcdef".

astra_db_admin

an AstraDBAdmin object that has visibility over the target database.

Returns

An AstraDBDatabaseAdmin object, for admin work within the database.

Example

>>> from astrapy import DataAPIClient, AstraDBDatabaseAdmin
>>> admin_for_my_db = AstraDBDatabaseAdmin.from_astra_db_admin(
...     id="01234567-...",
...     astra_db_admin=DataAPIClient("AstraCS:...").get_admin(),
... )
>>> admin_for_my_db.list_namespaces()
['default_keyspace', 'staging_namespace']
>>> admin_for_my_db.info().status
'ACTIVE'

Note

Creating an instance of AstraDBDatabaseAdmin does not trigger actual creation of the database itself, which should exist beforehand. To create databases, see the AstraDBAdmin class.

Expand source code

@staticmethod
def from_astra_db_admin(
    id: str, *, astra_db_admin: AstraDBAdmin
) -> AstraDBDatabaseAdmin:
    """
    Create an AstraDBDatabaseAdmin from an AstraDBAdmin and a database ID.

    Args:
        id: e. g. "01234567-89ab-cdef-0123-456789abcdef".
        astra_db_admin: an AstraDBAdmin object that has visibility over
            the target database.

    Returns:
        An AstraDBDatabaseAdmin object, for admin work within the database.

    Example:
        >>> from astrapy import DataAPIClient, AstraDBDatabaseAdmin
        >>> admin_for_my_db = AstraDBDatabaseAdmin.from_astra_db_admin(
        ...     id="01234567-...",
        ...     astra_db_admin=DataAPIClient("AstraCS:...").get_admin(),
        ... )
        >>> admin_for_my_db.list_namespaces()
        ['default_keyspace', 'staging_namespace']
        >>> admin_for_my_db.info().status
        'ACTIVE'

    Note:
        Creating an instance of AstraDBDatabaseAdmin does not trigger actual creation
        of the database itself, which should exist beforehand. To create databases,
        see the AstraDBAdmin class.
    """

    return AstraDBDatabaseAdmin(
        id=id,
        token=astra_db_admin.token,
        environment=astra_db_admin.environment,
        caller_name=astra_db_admin._caller_name,
        caller_version=astra_db_admin._caller_version,
        dev_ops_url=astra_db_admin._dev_ops_url,
        dev_ops_api_version=astra_db_admin._dev_ops_api_version,
    )

Methods

async def async_create_namespace(self, name: str, *, wait_until_active: bool = True, max_time_ms: Optional[int] = None) ‑> Dict[str, Any]

Create a namespace in this database as requested, optionally waiting for it to be ready. Async version of the method, for use in an asyncio context.

Args

name

the namespace name. If supplying a namespace that exists already, the method call proceeds as usual, no errors are raised, and the whole invocation is a no-op.

wait_until_active

if True (default), the method returns only after the target database is in ACTIVE state again (a few seconds, usually). If False, it will return right after issuing the creation request to the DevOps API, and it will be responsibility of the caller to check the database status/namespace availability before working with it.

max_time_ms

a timeout, in milliseconds, for the whole requested operation to complete. Note that a timeout is no guarantee that the creation request has not reached the API server.

Returns

A dictionary of the form {"ok": 1} in case of success. Otherwise, an exception is raised.

Example

>>> asyncio.run(
...     my_db_admin.async_create_namespace("app_namespace")
... )
{'ok': 1}

Expand source code

@ops_recast_method_async
async def async_create_namespace(  # type: ignore[override]
    self,
    name: str,
    *,
    wait_until_active: bool = True,
    max_time_ms: Optional[int] = None,
) -> Dict[str, Any]:
    """
    Create a namespace in this database as requested,
    optionally waiting for it to be ready.
    Async version of the method, for use in an asyncio context.

    Args:
        name: the namespace name. If supplying a namespace that exists
            already, the method call proceeds as usual, no errors are
            raised, and the whole invocation is a no-op.
        wait_until_active: if True (default), the method returns only after
            the target database is in ACTIVE state again (a few
            seconds, usually). If False, it will return right after issuing the
            creation request to the DevOps API, and it will be responsibility
            of the caller to check the database status/namespace availability
            before working with it.
        max_time_ms: a timeout, in milliseconds, for the whole requested
            operation to complete.
            Note that a timeout is no guarantee that the creation request
            has not reached the API server.

    Returns:
        A dictionary of the form {"ok": 1} in case of success.
        Otherwise, an exception is raised.

    Example:
        >>> asyncio.run(
        ...     my_db_admin.async_create_namespace("app_namespace")
        ... )
        {'ok': 1}
    """

    timeout_manager = MultiCallTimeoutManager(
        overall_max_time_ms=max_time_ms, exception_type="devops_api"
    )
    logger.info(f"creating namespace '{name}' on '{self.id}', async")
    cn_response = await self._astra_db_admin._astra_db_ops.async_create_keyspace(
        database=self.id,
        keyspace=name,
        timeout_info=base_timeout_info(max_time_ms),
    )
    logger.info(
        f"devops api returned from creating namespace "
        f"'{name}' on '{self.id}', async"
    )
    if cn_response is not None and name == cn_response.get("name"):
        if wait_until_active:
            last_status_seen = STATUS_MAINTENANCE
            while last_status_seen == STATUS_MAINTENANCE:
                logger.info(f"sleeping to poll for status of '{self.id}', async")
                await asyncio.sleep(DATABASE_POLL_NAMESPACE_SLEEP_TIME)
                last_db_info = await self.async_info(
                    max_time_ms=timeout_manager.remaining_timeout_ms(),
                )
                last_status_seen = last_db_info.status
            if last_status_seen != STATUS_ACTIVE:
                raise DevOpsAPIException(
                    f"Database entered unexpected status {last_status_seen} after MAINTENANCE."
                )
            # is the namespace found?
            if name not in await self.async_list_namespaces():
                raise DevOpsAPIException("Could not create the namespace.")
        logger.info(f"finished creating namespace '{name}' on '{self.id}', async")
        return {"ok": 1}
    else:
        raise DevOpsAPIException(
            f"Could not issue a successful create-namespace DevOps API request for {name}."
        )

async def async_drop(self, *, wait_until_active: bool = True, max_time_ms: Optional[int] = None) ‑> Dict[str, Any]

Drop this database, i.e. delete it completely and permanently with all its data. Async version of the method, for use in an asyncio context.

This method wraps the drop_database method of the AstraDBAdmin class, where more information may be found.

Args

wait_until_active

if True (default), the method returns only after the database has actually been deleted (generally a few minutes). If False, it will return right after issuing the drop request to the DevOps API, and it will be responsibility of the caller to check the database status/availability after that, if desired.

max_time_ms

a timeout, in milliseconds, for the whole requested operation to complete. Note that a timeout is no guarantee that the deletion request has not reached the API server.

Returns

A dictionary of the form {"ok": 1} in case of success. Otherwise, an exception is raised.

Example

>>> asyncio.run(my_db_admin.async_drop())
{'ok': 1}

Note

Once the method succeeds, methods on this object – such as astrapy.info, or list_namespaces() – can still be invoked: however, this hardly makes sense as the underlying actual database is no more. It is responsibility of the developer to design a correct flow which avoids using a deceased database any further.

Expand source code

async def async_drop(
    self,
    *,
    wait_until_active: bool = True,
    max_time_ms: Optional[int] = None,
) -> Dict[str, Any]:
    """
    Drop this database, i.e. delete it completely and permanently with all its data.
    Async version of the method, for use in an asyncio context.

    This method wraps the `drop_database` method of the AstraDBAdmin class,
    where more information may be found.

    Args:
        wait_until_active: if True (default), the method returns only after
            the database has actually been deleted (generally a few minutes).
            If False, it will return right after issuing the
            drop request to the DevOps API, and it will be responsibility
            of the caller to check the database status/availability
            after that, if desired.
        max_time_ms: a timeout, in milliseconds, for the whole requested
            operation to complete.
            Note that a timeout is no guarantee that the deletion request
            has not reached the API server.

    Returns:
        A dictionary of the form {"ok": 1} in case of success.
        Otherwise, an exception is raised.

    Example:
        >>> asyncio.run(my_db_admin.async_drop())
        {'ok': 1}

    Note:
        Once the method succeeds, methods on this object -- such as `info()`,
        or `list_namespaces()` -- can still be invoked: however, this hardly
        makes sense as the underlying actual database is no more.
        It is responsibility of the developer to design a correct flow
        which avoids using a deceased database any further.
    """

    logger.info(f"dropping this database ('{self.id}'), async")
    return await self._astra_db_admin.async_drop_database(  # type: ignore[no-any-return]
        id=self.id,
        wait_until_active=wait_until_active,
        max_time_ms=max_time_ms,
    )
    logger.info(f"finished dropping this database ('{self.id}'), async")

async def async_drop_namespace(self, name: str, *, wait_until_active: bool = True, max_time_ms: Optional[int] = None) ‑> Dict[str, Any]

Delete a namespace from the database, optionally waiting for it to become active again. Async version of the method, for use in an asyncio context.

Args

name

the namespace to delete. If it does not exist in this database, an error is raised.

wait_until_active

if True (default), the method returns only after the target database is in ACTIVE state again (a few seconds, usually). If False, it will return right after issuing the deletion request to the DevOps API, and it will be responsibility of the caller to check the database status/namespace availability before working with it.

max_time_ms

a timeout, in milliseconds, for the whole requested operation to complete. Note that a timeout is no guarantee that the deletion request has not reached the API server.

Returns

A dictionary of the form {"ok": 1} in case of success. Otherwise, an exception is raised.

Example

>>> asyncio.run(
...     my_db_admin.async_drop_namespace("app_namespace")
... )
{'ok': 1}

Expand source code

@ops_recast_method_async
async def async_drop_namespace(  # type: ignore[override]
    self,
    name: str,
    *,
    wait_until_active: bool = True,
    max_time_ms: Optional[int] = None,
) -> Dict[str, Any]:
    """
    Delete a namespace from the database, optionally waiting for it
    to become active again.
    Async version of the method, for use in an asyncio context.

    Args:
        name: the namespace to delete. If it does not exist in this database,
            an error is raised.
        wait_until_active: if True (default), the method returns only after
            the target database is in ACTIVE state again (a few
            seconds, usually). If False, it will return right after issuing the
            deletion request to the DevOps API, and it will be responsibility
            of the caller to check the database status/namespace availability
            before working with it.
        max_time_ms: a timeout, in milliseconds, for the whole requested
            operation to complete.
            Note that a timeout is no guarantee that the deletion request
            has not reached the API server.

    Returns:
        A dictionary of the form {"ok": 1} in case of success.
        Otherwise, an exception is raised.

    Example:
        >>> asyncio.run(
        ...     my_db_admin.async_drop_namespace("app_namespace")
        ... )
        {'ok': 1}
    """

    timeout_manager = MultiCallTimeoutManager(
        overall_max_time_ms=max_time_ms, exception_type="devops_api"
    )
    logger.info(f"dropping namespace '{name}' on '{self.id}', async")
    dk_response = await self._astra_db_admin._astra_db_ops.async_delete_keyspace(
        database=self.id,
        keyspace=name,
        timeout_info=base_timeout_info(max_time_ms),
    )
    logger.info(
        f"devops api returned from dropping namespace "
        f"'{name}' on '{self.id}', async"
    )
    if dk_response == name:
        if wait_until_active:
            last_status_seen = STATUS_MAINTENANCE
            while last_status_seen == STATUS_MAINTENANCE:
                logger.info(f"sleeping to poll for status of '{self.id}', async")
                await asyncio.sleep(DATABASE_POLL_NAMESPACE_SLEEP_TIME)
                last_db_info = await self.async_info(
                    max_time_ms=timeout_manager.remaining_timeout_ms(),
                )
                last_status_seen = last_db_info.status
            if last_status_seen != STATUS_ACTIVE:
                raise DevOpsAPIException(
                    f"Database entered unexpected status {last_status_seen} after MAINTENANCE."
                )
            # is the namespace found?
            if name in await self.async_list_namespaces():
                raise DevOpsAPIException("Could not drop the namespace.")
        logger.info(f"finished dropping namespace '{name}' on '{self.id}', async")
        return {"ok": 1}
    else:
        raise DevOpsAPIException(
            f"Could not issue a successful delete-namespace DevOps API request for {name}."
        )

async def async_info(self, *, max_time_ms: Optional[int] = None) ‑> AdminDatabaseInfo

Query the DevOps API for the full info on this database. Async version of the method, for use in an asyncio context.

Args

max_time_ms

a timeout, in milliseconds, for the DevOps API request.

Returns

An AdminDatabaseInfo object.

Example

>>> async def wait_until_active(db_admin: AstraDBDatabaseAdmin) -> None:
...     while True:
...         info = await db_admin.async_info()
...         if info.status == "ACTIVE":
...             return
...
>>> asyncio.run(wait_until_active(admin_for_my_db))

Expand source code

async def async_info(
    self, *, max_time_ms: Optional[int] = None
) -> AdminDatabaseInfo:
    """
    Query the DevOps API for the full info on this database.
    Async version of the method, for use in an asyncio context.

    Args:
        max_time_ms: a timeout, in milliseconds, for the DevOps API request.

    Returns:
        An AdminDatabaseInfo object.

    Example:
        >>> async def wait_until_active(db_admin: AstraDBDatabaseAdmin) -> None:
        ...     while True:
        ...         info = await db_admin.async_info()
        ...         if info.status == "ACTIVE":
        ...             return
        ...
        >>> asyncio.run(wait_until_active(admin_for_my_db))
    """

    logger.info(f"getting info ('{self.id}'), async")
    req_response = await self._astra_db_admin.async_database_info(
        id=self.id,
        max_time_ms=max_time_ms,
    )
    logger.info(f"finished getting info ('{self.id}'), async")
    return req_response  # type: ignore[no-any-return]

async def async_list_namespaces(self, *, max_time_ms: Optional[int] = None) ‑> List[str]

Query the DevOps API for a list of the namespaces in the database. Async version of the method, for use in an asyncio context.

Args

max_time_ms

a timeout, in milliseconds, for the DevOps API request.

Returns

A list of the namespaces, each a string, in no particular order.

Example

>>> async def check_if_ns_exists(
...     db_admin: AstraDBDatabaseAdmin, namespace: str
... ) -> bool:
...     ns_list = await db_admin.async_list_namespaces()
...     return namespace in ns_list
...
>>> asyncio.run(check_if_ns_exists(admin_for_my_db, "dragons"))
False
>>> asyncio.run(check_if_db_exists(admin_for_my_db, "app_namespace"))
True

Expand source code

async def async_list_namespaces(
    self, *, max_time_ms: Optional[int] = None
) -> List[str]:
    """
    Query the DevOps API for a list of the namespaces in the database.
    Async version of the method, for use in an asyncio context.

    Args:
        max_time_ms: a timeout, in milliseconds, for the DevOps API request.

    Returns:
        A list of the namespaces, each a string, in no particular order.

    Example:
        >>> async def check_if_ns_exists(
        ...     db_admin: AstraDBDatabaseAdmin, namespace: str
        ... ) -> bool:
        ...     ns_list = await db_admin.async_list_namespaces()
        ...     return namespace in ns_list
        ...
        >>> asyncio.run(check_if_ns_exists(admin_for_my_db, "dragons"))
        False
        >>> asyncio.run(check_if_db_exists(admin_for_my_db, "app_namespace"))
        True
    """

    logger.info(f"getting namespaces ('{self.id}'), async")
    info = await self.async_info(max_time_ms=max_time_ms)
    logger.info(f"finished getting namespaces ('{self.id}'), async")
    if info.raw_info is None:
        raise DevOpsAPIException("Could not get the namespace list.")
    else:
        return info.raw_info["info"]["keyspaces"]  # type: ignore[no-any-return]

def create_namespace(self, name: str, *, wait_until_active: bool = True, max_time_ms: Optional[int] = None) ‑> Dict[str, Any]

Create a namespace in this database as requested, optionally waiting for it to be ready.

Args

name

the namespace name. If supplying a namespace that exists already, the method call proceeds as usual, no errors are raised, and the whole invocation is a no-op.

wait_until_active

if True (default), the method returns only after the target database is in ACTIVE state again (a few seconds, usually). If False, it will return right after issuing the creation request to the DevOps API, and it will be responsibility of the caller to check the database status/namespace availability before working with it.

max_time_ms

a timeout, in milliseconds, for the whole requested operation to complete. Note that a timeout is no guarantee that the creation request has not reached the API server.

Returns

A dictionary of the form {"ok": 1} in case of success. Otherwise, an exception is raised.

Example

>>> my_db_admin.list_namespaces()
['default_keyspace']
>>> my_db_admin.create_namespace("that_other_one")
{'ok': 1}
>>> my_db_admin.list_namespaces()
['default_keyspace', 'that_other_one']

Expand source code

@ops_recast_method_sync
def create_namespace(
    self,
    name: str,
    *,
    wait_until_active: bool = True,
    max_time_ms: Optional[int] = None,
) -> Dict[str, Any]:
    """
    Create a namespace in this database as requested,
    optionally waiting for it to be ready.

    Args:
        name: the namespace name. If supplying a namespace that exists
            already, the method call proceeds as usual, no errors are
            raised, and the whole invocation is a no-op.
        wait_until_active: if True (default), the method returns only after
            the target database is in ACTIVE state again (a few
            seconds, usually). If False, it will return right after issuing the
            creation request to the DevOps API, and it will be responsibility
            of the caller to check the database status/namespace availability
            before working with it.
        max_time_ms: a timeout, in milliseconds, for the whole requested
            operation to complete.
            Note that a timeout is no guarantee that the creation request
            has not reached the API server.

    Returns:
        A dictionary of the form {"ok": 1} in case of success.
        Otherwise, an exception is raised.

    Example:
        >>> my_db_admin.list_namespaces()
        ['default_keyspace']
        >>> my_db_admin.create_namespace("that_other_one")
        {'ok': 1}
        >>> my_db_admin.list_namespaces()
        ['default_keyspace', 'that_other_one']
    """

    timeout_manager = MultiCallTimeoutManager(
        overall_max_time_ms=max_time_ms, exception_type="devops_api"
    )
    logger.info(f"creating namespace '{name}' on '{self.id}'")
    cn_response = self._astra_db_admin._astra_db_ops.create_keyspace(
        database=self.id,
        keyspace=name,
        timeout_info=base_timeout_info(max_time_ms),
    )
    logger.info(
        f"devops api returned from creating namespace '{name}' on '{self.id}'"
    )
    if cn_response is not None and name == cn_response.get("name"):
        if wait_until_active:
            last_status_seen = STATUS_MAINTENANCE
            while last_status_seen == STATUS_MAINTENANCE:
                logger.info(f"sleeping to poll for status of '{self.id}'")
                time.sleep(DATABASE_POLL_NAMESPACE_SLEEP_TIME)
                last_status_seen = self.info(
                    max_time_ms=timeout_manager.remaining_timeout_ms(),
                ).status
            if last_status_seen != STATUS_ACTIVE:
                raise DevOpsAPIException(
                    f"Database entered unexpected status {last_status_seen} after MAINTENANCE."
                )
            # is the namespace found?
            if name not in self.list_namespaces():
                raise DevOpsAPIException("Could not create the namespace.")
        logger.info(f"finished creating namespace '{name}' on '{self.id}'")
        return {"ok": 1}
    else:
        raise DevOpsAPIException(
            f"Could not issue a successful create-namespace DevOps API request for {name}."
        )

def drop(self, *, wait_until_active: bool = True, max_time_ms: Optional[int] = None) ‑> Dict[str, Any]

Drop this database, i.e. delete it completely and permanently with all its data.

This method wraps the drop_database method of the AstraDBAdmin class, where more information may be found.

Args

wait_until_active

if True (default), the method returns only after the database has actually been deleted (generally a few minutes). If False, it will return right after issuing the drop request to the DevOps API, and it will be responsibility of the caller to check the database status/availability after that, if desired.

max_time_ms

a timeout, in milliseconds, for the whole requested operation to complete. Note that a timeout is no guarantee that the deletion request has not reached the API server.

Returns

A dictionary of the form {"ok": 1} in case of success. Otherwise, an exception is raised.

Example

>>> my_db_admin.list_namespaces()
['default_keyspace', 'that_other_one']
>>> my_db_admin.drop()
{'ok': 1}
>>> my_db_admin.list_namespaces()  # raises a 404 Not Found http error

Note

Once the method succeeds, methods on this object – such as astrapy.info, or list_namespaces() – can still be invoked: however, this hardly makes sense as the underlying actual database is no more. It is responsibility of the developer to design a correct flow which avoids using a deceased database any further.

Expand source code

def drop(
    self,
    *,
    wait_until_active: bool = True,
    max_time_ms: Optional[int] = None,
) -> Dict[str, Any]:
    """
    Drop this database, i.e. delete it completely and permanently with all its data.

    This method wraps the `drop_database` method of the AstraDBAdmin class,
    where more information may be found.

    Args:
        wait_until_active: if True (default), the method returns only after
            the database has actually been deleted (generally a few minutes).
            If False, it will return right after issuing the
            drop request to the DevOps API, and it will be responsibility
            of the caller to check the database status/availability
            after that, if desired.
        max_time_ms: a timeout, in milliseconds, for the whole requested
            operation to complete.
            Note that a timeout is no guarantee that the deletion request
            has not reached the API server.

    Returns:
        A dictionary of the form {"ok": 1} in case of success.
        Otherwise, an exception is raised.

    Example:
        >>> my_db_admin.list_namespaces()
        ['default_keyspace', 'that_other_one']
        >>> my_db_admin.drop()
        {'ok': 1}
        >>> my_db_admin.list_namespaces()  # raises a 404 Not Found http error

    Note:
        Once the method succeeds, methods on this object -- such as `info()`,
        or `list_namespaces()` -- can still be invoked: however, this hardly
        makes sense as the underlying actual database is no more.
        It is responsibility of the developer to design a correct flow
        which avoids using a deceased database any further.
    """

    logger.info(f"dropping this database ('{self.id}')")
    return self._astra_db_admin.drop_database(  # type: ignore[no-any-return]
        id=self.id,
        wait_until_active=wait_until_active,
        max_time_ms=max_time_ms,
    )
    logger.info(f"finished dropping this database ('{self.id}')")

def drop_namespace(self, name: str, *, wait_until_active: bool = True, max_time_ms: Optional[int] = None) ‑> Dict[str, Any]

Delete a namespace from the database, optionally waiting for it to become active again.

Args

name

the namespace to delete. If it does not exist in this database, an error is raised.

wait_until_active

if True (default), the method returns only after the target database is in ACTIVE state again (a few seconds, usually). If False, it will return right after issuing the deletion request to the DevOps API, and it will be responsibility of the caller to check the database status/namespace availability before working with it.

max_time_ms

a timeout, in milliseconds, for the whole requested operation to complete. Note that a timeout is no guarantee that the deletion request has not reached the API server.

Returns

A dictionary of the form {"ok": 1} in case of success. Otherwise, an exception is raised.

Example

>>> my_db_admin.list_namespaces()
['default_keyspace', 'that_other_one']
>>> my_db_admin.drop_namespace("that_other_one")
{'ok': 1}
>>> my_db_admin.list_namespaces()
['default_keyspace']

Expand source code

@ops_recast_method_sync
def drop_namespace(
    self,
    name: str,
    *,
    wait_until_active: bool = True,
    max_time_ms: Optional[int] = None,
) -> Dict[str, Any]:
    """
    Delete a namespace from the database, optionally waiting for it
    to become active again.

    Args:
        name: the namespace to delete. If it does not exist in this database,
            an error is raised.
        wait_until_active: if True (default), the method returns only after
            the target database is in ACTIVE state again (a few
            seconds, usually). If False, it will return right after issuing the
            deletion request to the DevOps API, and it will be responsibility
            of the caller to check the database status/namespace availability
            before working with it.
        max_time_ms: a timeout, in milliseconds, for the whole requested
            operation to complete.
            Note that a timeout is no guarantee that the deletion request
            has not reached the API server.

    Returns:
        A dictionary of the form {"ok": 1} in case of success.
        Otherwise, an exception is raised.

    Example:
        >>> my_db_admin.list_namespaces()
        ['default_keyspace', 'that_other_one']
        >>> my_db_admin.drop_namespace("that_other_one")
        {'ok': 1}
        >>> my_db_admin.list_namespaces()
        ['default_keyspace']
    """

    timeout_manager = MultiCallTimeoutManager(
        overall_max_time_ms=max_time_ms, exception_type="devops_api"
    )
    logger.info(f"dropping namespace '{name}' on '{self.id}'")
    dk_response = self._astra_db_admin._astra_db_ops.delete_keyspace(
        database=self.id,
        keyspace=name,
        timeout_info=base_timeout_info(max_time_ms),
    )
    logger.info(
        f"devops api returned from dropping namespace '{name}' on '{self.id}'"
    )
    if dk_response == name:
        if wait_until_active:
            last_status_seen = STATUS_MAINTENANCE
            while last_status_seen == STATUS_MAINTENANCE:
                logger.info(f"sleeping to poll for status of '{self.id}'")
                time.sleep(DATABASE_POLL_NAMESPACE_SLEEP_TIME)
                last_status_seen = self.info(
                    max_time_ms=timeout_manager.remaining_timeout_ms(),
                ).status
            if last_status_seen != STATUS_ACTIVE:
                raise DevOpsAPIException(
                    f"Database entered unexpected status {last_status_seen} after MAINTENANCE."
                )
            # is the namespace found?
            if name in self.list_namespaces():
                raise DevOpsAPIException("Could not drop the namespace.")
        logger.info(f"finished dropping namespace '{name}' on '{self.id}'")
        return {"ok": 1}
    else:
        raise DevOpsAPIException(
            f"Could not issue a successful delete-namespace DevOps API request for {name}."
        )

def get_async_database(self, *, token: Optional[str] = None, namespace: Optional[str] = None, region: Optional[str] = None, api_path: Optional[str] = None, api_version: Optional[str] = None, max_time_ms: Optional[int] = None) ‑> AsyncDatabase

Create an AsyncDatabase instance out of this class for working with the data in it.

This method has identical behavior and signature as the sync counterpart get_database: please see that one for more details.

Expand source code

def get_async_database(
    self,
    *,
    token: Optional[str] = None,
    namespace: Optional[str] = None,
    region: Optional[str] = None,
    api_path: Optional[str] = None,
    api_version: Optional[str] = None,
    max_time_ms: Optional[int] = None,
) -> AsyncDatabase:
    """
    Create an AsyncDatabase instance out of this class for working
    with the data in it.

    This method has identical behavior and signature as the sync
    counterpart `get_database`: please see that one for more details.
    """

    return self.get_database(
        token=token,
        namespace=namespace,
        region=region,
        api_path=api_path,
        api_version=api_version,
        max_time_ms=max_time_ms,
    ).to_async()

def get_database(self, *, token: Optional[str] = None, namespace: Optional[str] = None, region: Optional[str] = None, api_path: Optional[str] = None, api_version: Optional[str] = None, max_time_ms: Optional[int] = None) ‑> Database

Create a Database instance out of this class for working with the data in it.

Args

token

if supplied, is passed to the Database instead of the one set for this object. Useful if one wants to work in a least-privilege manner, limiting the permissions for non-admin work.

namespace

an optional namespace to set in the resulting Database. The same default logic as for AstraDBAdmin.get_database() applies.

region

an optional region for connecting to the database Data API endpoint. The same default logic as for AstraDBAdmin.get_database() applies.

api_path

path to append to the API Endpoint. In typical usage, this should be left to its default of "/api/json".

api_version

version specifier to append to the API path. In typical usage, this should be left to its default of "v1".

Returns

A Database object, ready to be used for working with data and collections.

Example

>>> my_db = my_db_admin.get_database()
>>> my_db.list_collection_names()
['movies', 'another_collection']

Note

creating an instance of Database does not trigger actual creation of the database itself, which should exist beforehand. To create databases, see the AstraDBAdmin class.

Expand source code

def get_database(
    self,
    *,
    token: Optional[str] = None,
    namespace: Optional[str] = None,
    region: Optional[str] = None,
    api_path: Optional[str] = None,
    api_version: Optional[str] = None,
    max_time_ms: Optional[int] = None,
) -> Database:
    """
    Create a Database instance out of this class for working with the data in it.

    Args:
        token: if supplied, is passed to the Database instead of
            the one set for this object. Useful if one wants to work in
            a least-privilege manner, limiting the permissions for non-admin work.
        namespace: an optional namespace to set in the resulting Database.
            The same default logic as for `AstraDBAdmin.get_database` applies.
        region: an optional region for connecting to the database Data API endpoint.
            The same default logic as for `AstraDBAdmin.get_database` applies.
        api_path: path to append to the API Endpoint. In typical usage, this
            should be left to its default of "/api/json".
        api_version: version specifier to append to the API path. In typical
            usage, this should be left to its default of "v1".

    Returns:
        A Database object, ready to be used for working with data and collections.

    Example:
        >>> my_db = my_db_admin.get_database()
        >>> my_db.list_collection_names()
        ['movies', 'another_collection']

    Note:
        creating an instance of Database does not trigger actual creation
        of the database itself, which should exist beforehand. To create databases,
        see the AstraDBAdmin class.
    """

    return self._astra_db_admin.get_database(
        id=self.id,
        token=token,
        namespace=namespace,
        region=region,
        api_path=api_path,
        api_version=api_version,
        max_time_ms=max_time_ms,
    )

def info(self, *, max_time_ms: Optional[int] = None) ‑> AdminDatabaseInfo

Query the DevOps API for the full info on this database.

Args

max_time_ms

a timeout, in milliseconds, for the DevOps API request.

Returns

An AdminDatabaseInfo object.

Example

>>> my_db_info = admin_for_my_db.info()
>>> my_db_info.status
'ACTIVE'
>>> my_db_info.info.region
'us-east1'

Expand source code

def info(self, *, max_time_ms: Optional[int] = None) -> AdminDatabaseInfo:
    """
    Query the DevOps API for the full info on this database.

    Args:
        max_time_ms: a timeout, in milliseconds, for the DevOps API request.

    Returns:
        An AdminDatabaseInfo object.

    Example:
        >>> my_db_info = admin_for_my_db.info()
        >>> my_db_info.status
        'ACTIVE'
        >>> my_db_info.info.region
        'us-east1'
    """

    logger.info(f"getting info ('{self.id}')")
    req_response = self._astra_db_admin.database_info(
        id=self.id,
        max_time_ms=max_time_ms,
    )
    logger.info(f"finished getting info ('{self.id}')")
    return req_response  # type: ignore[no-any-return]

def list_namespaces(self, *, max_time_ms: Optional[int] = None) ‑> List[str]

Query the DevOps API for a list of the namespaces in the database.

Args

max_time_ms

a timeout, in milliseconds, for the DevOps API request.

Returns

A list of the namespaces, each a string, in no particular order.

Example

>>> admin_for_my_db.list_namespaces()
['default_keyspace', 'staging_namespace']

Expand source code

def list_namespaces(self, *, max_time_ms: Optional[int] = None) -> List[str]:
    """
    Query the DevOps API for a list of the namespaces in the database.

    Args:
        max_time_ms: a timeout, in milliseconds, for the DevOps API request.

    Returns:
        A list of the namespaces, each a string, in no particular order.

    Example:
        >>> admin_for_my_db.list_namespaces()
        ['default_keyspace', 'staging_namespace']
    """

    logger.info(f"getting namespaces ('{self.id}')")
    info = self.info(max_time_ms=max_time_ms)
    logger.info(f"finished getting namespaces ('{self.id}')")
    if info.raw_info is None:
        raise DevOpsAPIException("Could not get the namespace list.")
    else:
        return info.raw_info["info"]["keyspaces"]  # type: ignore[no-any-return]

def set_caller(self, caller_name: Optional[str] = None, caller_version: Optional[str] = None) ‑> None

Set a new identity for the application/framework on behalf of which the DevOps API calls will be performed (the "caller").

New objects spawned from this client afterwards will inherit the new settings.

Args

caller_name

name of the application, or framework, on behalf of which the DevOps API calls are performed. This ends up in the request user-agent.

caller_version

version of the caller.

Example

>>> admin_for_my_db.set_caller(
...     caller_name="the_caller",
...     caller_version="0.1.0",
... )

Expand source code

def set_caller(
    self,
    caller_name: Optional[str] = None,
    caller_version: Optional[str] = None,
) -> None:
    """
    Set a new identity for the application/framework on behalf of which
    the DevOps API calls will be performed (the "caller").

    New objects spawned from this client afterwards will inherit the new settings.

    Args:
        caller_name: name of the application, or framework, on behalf of which
            the DevOps API calls are performed. This ends up in the request user-agent.
        caller_version: version of the caller.

    Example:
        >>> admin_for_my_db.set_caller(
        ...     caller_name="the_caller",
        ...     caller_version="0.1.0",
        ... )
    """

    logger.info(f"setting caller to {caller_name}/{caller_version}")
    self._astra_db_admin.set_caller(caller_name, caller_version)

def with_options(self, *, id: Optional[str] = None, token: Optional[str] = None, caller_name: Optional[str] = None, caller_version: Optional[str] = None) ‑> AstraDBDatabaseAdmin

Create a clone of this AstraDBDatabaseAdmin with some changed attributes.

Args

id

e. g. "01234567-89ab-cdef-0123-456789abcdef".

token

an Access Token to the database. Example: "AstraCS:xyz...".

caller_name

name of the application, or framework, on behalf of which the Data API and DevOps API calls are performed. This ends up in the request user-agent.

caller_version

version of the caller.

Returns

a new AstraDBDatabaseAdmin instance.

Example

>>> admin_for_my_other_db = admin_for_my_db.with_options(
...     id="abababab-0101-2323-4545-6789abcdef01",
... )

Expand source code

def with_options(
    self,
    *,
    id: Optional[str] = None,
    token: Optional[str] = None,
    caller_name: Optional[str] = None,
    caller_version: Optional[str] = None,
) -> AstraDBDatabaseAdmin:
    """
    Create a clone of this AstraDBDatabaseAdmin with some changed attributes.

    Args:
        id: e. g. "01234567-89ab-cdef-0123-456789abcdef".
        token: an Access Token to the database. Example: `"AstraCS:xyz..."`.
        caller_name: name of the application, or framework, on behalf of which
            the Data API and DevOps API calls are performed. This ends up in
            the request user-agent.
        caller_version: version of the caller.

    Returns:
        a new AstraDBDatabaseAdmin instance.

    Example:
        >>> admin_for_my_other_db = admin_for_my_db.with_options(
        ...     id="abababab-0101-2323-4545-6789abcdef01",
        ... )
    """

    return self._copy(
        id=id,
        token=token,
        caller_name=caller_name,
        caller_version=caller_version,
    )

>>> from astrapy import DataAPIClient, AsyncCollection
>>> my_client = astrapy.DataAPIClient("AstraCS:...")
>>> my_async_db = my_client.get_async_database_by_api_endpoint(
...    "https://01234567-....apps.astra.datastax.com"
... )
>>> my_async_coll_1 = AsyncCollection(database=my_async_db, name="my_collection")
>>> my_async coll_2 = asyncio.run(my_async_db.create_collection(
...     "my_v_collection",
...     dimension=3,
...     metric="cosine",
... ))
>>> my_async_coll_3a = asyncio.run(my_async_db.get_collection(
...     "my_already_existing_collection",
... ))
>>> my_async_coll_3b = my_async_db.my_already_existing_collection
>>> my_async_coll_3c = my_async_db["my_already_existing_collection"]

class AsyncCollection:
    """
    A Data API collection, the main object to interact with the Data API,
    especially for DDL operations.
    This class has a synchronous interface.

    A Collection is spawned from a Database object, from which it inherits
    the details on how to reach the API server (endpoint, authentication token).

    Args:
        database: a Database object, instantiated earlier. This represents
            the database the collection belongs to.
        name: the collection name. This parameter should match an existing
            collection on the database.
        namespace: this is the namespace to which the collection belongs.
            If not specified, the database's working namespace is used.
        caller_name: name of the application, or framework, on behalf of which
            the Data API calls are performed. This ends up in the request user-agent.
        caller_version: version of the caller.

    Examples:
        >>> from astrapy import DataAPIClient, AsyncCollection
        >>> my_client = astrapy.DataAPIClient("AstraCS:...")
        >>> my_async_db = my_client.get_async_database_by_api_endpoint(
        ...    "https://01234567-....apps.astra.datastax.com"
        ... )
        >>> my_async_coll_1 = AsyncCollection(database=my_async_db, name="my_collection")
        >>> my_async coll_2 = asyncio.run(my_async_db.create_collection(
        ...     "my_v_collection",
        ...     dimension=3,
        ...     metric="cosine",
        ... ))
        >>> my_async_coll_3a = asyncio.run(my_async_db.get_collection(
        ...     "my_already_existing_collection",
        ... ))
        >>> my_async_coll_3b = my_async_db.my_already_existing_collection
        >>> my_async_coll_3c = my_async_db["my_already_existing_collection"]

    Note:
        creating an instance of Collection does not trigger actual creation
        of the collection on the database. The latter should have been created
        beforehand, e.g. through the `create_collection` method of a Database.
    """

    def __init__(
        self,
        database: AsyncDatabase,
        name: str,
        *,
        namespace: Optional[str] = None,
        caller_name: Optional[str] = None,
        caller_version: Optional[str] = None,
    ) -> None:
        self._astra_db_collection: AsyncAstraDBCollection = AsyncAstraDBCollection(
            collection_name=name,
            astra_db=database._astra_db,
            namespace=namespace,
            caller_name=caller_name,
            caller_version=caller_version,
        )
        # this comes after the above, lets AstraDBCollection resolve namespace
        self._database = database._copy(
            namespace=self._astra_db_collection.astra_db.namespace
        )

    def __repr__(self) -> str:
        return (
            f'{self.__class__.__name__}(name="{self.name}", '
            f'namespace="{self.namespace}", database={self.database})'
        )

    def __eq__(self, other: Any) -> bool:
        if isinstance(other, AsyncCollection):
            return self._astra_db_collection == other._astra_db_collection
        else:
            return False

    def __call__(self, *pargs: Any, **kwargs: Any) -> None:
        raise TypeError(
            f"'{self.__class__.__name__}' object is not callable. If you "
            f"meant to call the '{self.name}' method on a "
            f"'{self.database.__class__.__name__}' object "
            "it is failing because no such method exists."
        )

    def _copy(
        self,
        *,
        database: Optional[AsyncDatabase] = None,
        name: Optional[str] = None,
        namespace: Optional[str] = None,
        caller_name: Optional[str] = None,
        caller_version: Optional[str] = None,
    ) -> AsyncCollection:
        return AsyncCollection(
            database=database or self.database._copy(),
            name=name or self.name,
            namespace=namespace or self.namespace,
            caller_name=caller_name or self._astra_db_collection.caller_name,
            caller_version=caller_version or self._astra_db_collection.caller_version,
        )

    def with_options(
        self,
        *,
        name: Optional[str] = None,
        caller_name: Optional[str] = None,
        caller_version: Optional[str] = None,
    ) -> AsyncCollection:
        """
        Create a clone of this collection with some changed attributes.

        Args:
            name: the name of the collection. This parameter is useful to
                quickly spawn AsyncCollection instances each pointing to a different
                collection existing in the same namespace.
            caller_name: name of the application, or framework, on behalf of which
                the Data API calls are performed. This ends up in the request user-agent.
            caller_version: version of the caller.

        Returns:
            a new AsyncCollection instance.

        Example:
            >>> my_other_async_coll = my_async_coll.with_options(
            ...     name="the_other_coll",
            ...     caller_name="caller_identity",
            ... )
        """

        return self._copy(
            name=name,
            caller_name=caller_name,
            caller_version=caller_version,
        )

    def to_sync(
        self,
        *,
        database: Optional[Database] = None,
        name: Optional[str] = None,
        namespace: Optional[str] = None,
        caller_name: Optional[str] = None,
        caller_version: Optional[str] = None,
    ) -> Collection:
        """
        Create a Collection from this one. Save for the arguments
        explicitly provided as overrides, everything else is kept identical
        to this collection in the copy (the database is converted into
        a sync object).

        Args:
            database: a Database object, instantiated earlier.
                This represents the database the new collection belongs to.
            name: the collection name. This parameter should match an existing
                collection on the database.
            namespace: this is the namespace to which the collection belongs.
                If not specified, the database's working namespace is used.
            caller_name: name of the application, or framework, on behalf of which
                the Data API calls are performed. This ends up in the request user-agent.
            caller_version: version of the caller.

        Returns:
            the new copy, a Collection instance.

        Example:
            >>> my_async_coll.to_sync().count_documents({}, upper_bound=100)
            77
        """

        return Collection(
            database=database or self.database.to_sync(),
            name=name or self.name,
            namespace=namespace or self.namespace,
            caller_name=caller_name or self._astra_db_collection.caller_name,
            caller_version=caller_version or self._astra_db_collection.caller_version,
        )

    def set_caller(
        self,
        caller_name: Optional[str] = None,
        caller_version: Optional[str] = None,
    ) -> None:
        """
        Set a new identity for the application/framework on behalf of which
        the Data API calls are performed (the "caller").

        Args:
            caller_name: name of the application, or framework, on behalf of which
                the Data API calls are performed. This ends up in the request user-agent.
            caller_version: version of the caller.

        Example:
            >>> my_coll.set_caller(caller_name="the_caller", caller_version="0.1.0")
        """

        logger.info(f"setting caller to {caller_name}/{caller_version}")
        self._astra_db_collection.set_caller(
            caller_name=caller_name,
            caller_version=caller_version,
        )

    async def options(self, *, max_time_ms: Optional[int] = None) -> CollectionOptions:
        """
        Get the collection options, i.e. its configuration as read from the database.

        The method issues a request to the Data API each time is invoked,
        without caching mechanisms: this ensures up-to-date information
        for usages such as real-time collection validation by the application.

        Args:
            max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

        Returns:
            a CollectionOptions instance describing the collection.
            (See also the database `list_collections` method.)

        Example:
            >>> asyncio.run(my_async_coll.options())
            CollectionOptions(vector=CollectionVectorOptions(dimension=3, metric='cosine'))
        """

        logger.info(f"getting collections in search of '{self.name}'")
        self_descriptors = [
            coll_desc
            async for coll_desc in self.database.list_collections(
                max_time_ms=max_time_ms
            )
            if coll_desc.name == self.name
        ]
        logger.info(f"finished getting collections in search of '{self.name}'")
        if self_descriptors:
            return self_descriptors[0].options  # type: ignore[no-any-return]
        else:
            raise CollectionNotFoundException(
                text=f"Collection {self.namespace}.{self.name} not found.",
                namespace=self.namespace,
                collection_name=self.name,
            )

    def info(self) -> CollectionInfo:
        """
        Information on the collection (name, location, database), in the
        form of a CollectionInfo object.

        Not to be confused with the collection `options` method (related
        to the collection internal configuration).

        Example:
            >>> my_async_coll.info().database_info.region
            'us-east1'
            >>> my_async_coll.info().full_name
            'default_keyspace.my_v_collection'

        Note:
            the returned CollectionInfo wraps, among other things,
            the database information: as such, calling this method
            triggers the same-named method of a Database object (which, in turn,
            performs a HTTP request to the DevOps API).
            See the documentation for `Database.info()` for more details.
        """

        return CollectionInfo(
            database_info=self.database.info(),
            namespace=self.namespace,
            name=self.name,
            full_name=self.full_name,
        )

    @property
    def database(self) -> AsyncDatabase:
        """
        a Database object, the database this collection belongs to.

        Example:
            >>> my_async_coll.database.name
            'quicktest'
        """

        return self._database

    @property
    def namespace(self) -> str:
        """
        The namespace this collection is in.

        Example:
            >>> my_async_coll.database.namespace
            'default_keyspace'
        """

        return self.database.namespace

    @property
    def name(self) -> str:
        """
        The name of this collection.

        Example:
            >>> my_async_coll.name
            'my_v_collection'
        """

        # type hint added as for some reason the typechecker gets lost
        return self._astra_db_collection.collection_name  # type: ignore[no-any-return, has-type]

    @property
    def full_name(self) -> str:
        """
        The fully-qualified collection name within the database,
        in the form "namespace.collection_name".

        Example:
            >>> my_async_coll.full_name
            'default_keyspace.my_v_collection'
        """

        return f"{self.namespace}.{self.name}"

    @recast_method_async
    async def insert_one(
        self,
        document: DocumentType,
        *,
        vector: Optional[VectorType] = None,
        vectorize: Optional[str] = None,
        max_time_ms: Optional[int] = None,
    ) -> InsertOneResult:
        """
        Insert a single document in the collection in an atomic operation.

        Args:
            document: the dictionary expressing the document to insert.
                The `_id` field of the document can be left out, in which
                case it will be created automatically.
            vector: a vector (a list of numbers appropriate for the collection)
                for the document. Passing this parameter is equivalent to
                providing the vector in the "$vector" field of the document itself,
                however the two are mutually exclusive.
            vectorize: a string to be made into a vector, if such a service
                is configured for the collection. Passing this parameter is
                equivalent to providing a `$vectorize` field in the document itself,
                however the two are mutually exclusive.
                Moreover, this parameter cannot coexist with `vector`.
                NOTE: This feature is under current development.
            max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

        Returns:
            an InsertOneResult object.

        Example:
            >>> async def write_and_count(acol: AsyncCollection) -> None:
            ...     count0 = await acol.count_documents({}, upper_bound=10)
            ...     print("count0", count0)
            ...     await acol.insert_one(
            ...         {
            ...             "age": 30,
            ...             "name": "Smith",
            ...             "food": ["pear", "peach"],
            ...             "likes_fruit": True,
            ...         },
            ...     )
            ...     await acol.insert_one({"_id": "user-123", "age": 50, "name": "Maccio"})
            ...     count1 = await acol.count_documents({}, upper_bound=10)
            ...     print("count1", count1)
            ...
            >>> asyncio.run(write_and_count(my_async_coll))
            count0 0
            count1 2

            >>> asyncio.run(my_async_coll.insert_one({"tag": v"}, vector=[10, 11]))
            InsertOneResult(...)

        Note:
            If an `_id` is explicitly provided, which corresponds to a document
            that exists already in the collection, an error is raised and
            the insertion fails.
        """

        _document = _collate_vector_to_document(document, vector, vectorize)
        logger.info(f"inserting one document in '{self.name}'")
        io_response = await self._astra_db_collection.insert_one(
            _document,
            timeout_info=base_timeout_info(max_time_ms),
        )
        logger.info(f"finished inserting one document in '{self.name}'")
        if "insertedIds" in io_response.get("status", {}):
            if io_response["status"]["insertedIds"]:
                inserted_id = io_response["status"]["insertedIds"][0]
                return InsertOneResult(
                    raw_results=[io_response],
                    inserted_id=inserted_id,
                )
            else:
                raise ValueError(
                    "Could not complete a insert_one operation. "
                    f"(gotten '${json.dumps(io_response)}')"
                )
        else:
            raise ValueError(
                "Could not complete a insert_one operation. "
                f"(gotten '${json.dumps(io_response)}')"
            )

    @recast_method_async
    async def insert_many(
        self,
        documents: Iterable[DocumentType],
        *,
        vectors: Optional[Iterable[Optional[VectorType]]] = None,
        vectorize: Optional[Iterable[Optional[str]]] = None,
        ordered: bool = True,
        chunk_size: Optional[int] = None,
        concurrency: Optional[int] = None,
        max_time_ms: Optional[int] = None,
    ) -> InsertManyResult:
        """
        Insert a list of documents into the collection.
        This is not an atomic operation.

        Args:
            documents: an iterable of dictionaries, each a document to insert.
                Documents may specify their `_id` field or leave it out, in which
                case it will be added automatically.
            vectors: an optional list of vectors (as many vectors as the provided
                documents) to associate to the documents when inserting.
                Each vector is added to the corresponding document prior to
                insertion on database. The list can be a mixture of None and vectors,
                in which case some documents will not have a vector, unless it is
                specified in their "$vector" field already.
                Passing vectors this way is indeed equivalent to the "$vector" field
                of the documents, however the two are mutually exclusive.
            vectorize: an optional list of strings to be made into as many vectors
                (one per document), if such a service is configured for the collection.
                Passing this parameter is equivalent to providing a `$vectorize`
                field in the documents themselves, however the two are mutually exclusive.
                For any given document, this parameter cannot coexist with the
                corresponding `vector` entry.
                NOTE: This feature is under current development.
            ordered: if True (default), the insertions are processed sequentially.
                If False, they can occur in arbitrary order and possibly concurrently.
            chunk_size: how many documents to include in a single API request.
                Exceeding the server maximum allowed value results in an error.
                Leave it unspecified (recommended) to use the system default.
            concurrency: maximum number of concurrent requests to the API at
                a given time. It cannot be more than one for ordered insertions.
            max_time_ms: a timeout, in milliseconds, for the operation.

        Returns:
            an InsertManyResult object.

        Examples:
            >>> async def write_and_count(acol: AsyncCollection) -> None:
            ...             count0 = await acol.count_documents({}, upper_bound=10)
            ...             print("count0", count0)
            ...             im_result1 = await acol.insert_many(
            ...                 [
            ...                     {"a": 10},
            ...                     {"a": 5},
            ...                     {"b": [True, False, False]},
            ...                 ],
            ...             )
            ...             print("inserted1", im_result1.inserted_ids)
            ...             count1 = await acol.count_documents({}, upper_bound=100)
            ...             print("count1", count1)
            ...             await acol.insert_many(
            ...                 [{"seq": i} for i in range(50)],
            ...                 ordered=False,
            ...                 concurrency=5,
            ...             )
            ...             count2 = await acol.count_documents({}, upper_bound=100)
            ...             print("count2", count2)
            ...
            >>> asyncio.run(write_and_count(my_async_coll))
            count0 0
            inserted1 ['e3c2a684-...', '1de4949f-...', '167dacc3-...']
            count1 3
            count2 53

            # The following are three equivalent statements:
            >>> asyncio.run(my_async_coll.insert_many(
            ...     [{"tag": "a"}, {"tag": "b"}],
            ...     vectors=[[1, 2], [3, 4]],
            ... ))
            InsertManyResult(...)
            >>> asyncio.run(my_async_coll.insert_many(
            ...     [{"tag": "a", "$vector": [1, 2]}, {"tag": "b"}],
            ...     vectors=[None, [3, 4]],
            ... ))
            InsertManyResult(...)
            >>> asyncio.run(my_async_coll.insert_many(
            ...     [
            ...         {"tag": "a", "$vector": [1, 2]},
            ...         {"tag": "b", "$vector": [3, 4]},
            ...     ]
            ... ))
            InsertManyResult(...)


        Note:
            Unordered insertions are executed with some degree of concurrency,
            so it is usually better to prefer this mode unless the order in the
            document sequence is important.

        Note:
            A failure mode for this command is related to certain faulty documents
            found among those to insert: a document may have the an `_id` already
            present on the collection, or its vector dimension may not
            match the collection setting.

            For an ordered insertion, the method will raise an exception at
            the first such faulty document -- nevertheless, all documents processed
            until then will end up being written to the database.

            For unordered insertions, if the error stems from faulty documents
            the insertion proceeds until exhausting the input documents: then,
            an exception is raised -- and all insertable documents will have been
            written to the database, including those "after" the troublesome ones.

            If, on the other hand, there are errors not related to individual
            documents (such as a network connectivity error), the whole
            `insert_many` operation will stop in mid-way, an exception will be raised,
            and only a certain amount of the input documents will
            have made their way to the database.
        """

        if concurrency is None:
            if ordered:
                _concurrency = 1
            else:
                _concurrency = DEFAULT_INSERT_MANY_CONCURRENCY
        else:
            _concurrency = concurrency
        if _concurrency > 1 and ordered:
            raise ValueError("Cannot run ordered insert_many concurrently.")
        if chunk_size is None:
            _chunk_size = MAX_INSERT_NUM_DOCUMENTS
        else:
            _chunk_size = chunk_size
        _documents = _collate_vectors_to_documents(documents, vectors, vectorize)
        logger.info(f"inserting {len(_documents)} documents in '{self.name}'")
        raw_results: List[Dict[str, Any]] = []
        timeout_manager = MultiCallTimeoutManager(overall_max_time_ms=max_time_ms)
        if ordered:
            options = {"ordered": True}
            inserted_ids: List[Any] = []
            for i in range(0, len(_documents), _chunk_size):
                logger.info(f"inserting a chunk of documents in '{self.name}'")
                chunk_response = await self._astra_db_collection.insert_many(
                    documents=_documents[i : i + _chunk_size],
                    options=options,
                    partial_failures_allowed=True,
                    timeout_info=timeout_manager.remaining_timeout_info(),
                )
                logger.info(f"finished inserting a chunk of documents in '{self.name}'")
                # accumulate the results in this call
                chunk_inserted_ids = (chunk_response.get("status") or {}).get(
                    "insertedIds", []
                )
                inserted_ids += chunk_inserted_ids
                raw_results += [chunk_response]
                # if errors, quit early
                if chunk_response.get("errors", []):
                    partial_result = InsertManyResult(
                        raw_results=raw_results,
                        inserted_ids=inserted_ids,
                    )
                    raise InsertManyException.from_response(
                        command=None,
                        raw_response=chunk_response,
                        partial_result=partial_result,
                    )

            # return
            full_result = InsertManyResult(
                raw_results=raw_results,
                inserted_ids=inserted_ids,
            )
            logger.info(
                f"finished inserting {len(_documents)} documents in '{self.name}'"
            )
            return full_result

        else:
            # unordered: concurrent or not, do all of them and parse the results
            options = {"ordered": False}

            sem = asyncio.Semaphore(_concurrency)

            async def concurrent_insert_chunk(
                document_chunk: List[DocumentType],
            ) -> Dict[str, Any]:
                async with sem:
                    logger.info(f"inserting a chunk of documents in '{self.name}'")
                    im_response = await self._astra_db_collection.insert_many(
                        document_chunk,
                        options=options,
                        partial_failures_allowed=True,
                        timeout_info=timeout_manager.remaining_timeout_info(),
                    )
                    logger.info(
                        f"finished inserting a chunk of documents in '{self.name}'"
                    )
                    return im_response

            if _concurrency > 1:
                tasks = [
                    asyncio.create_task(
                        concurrent_insert_chunk(_documents[i : i + _chunk_size])
                    )
                    for i in range(0, len(_documents), _chunk_size)
                ]
                raw_results = await asyncio.gather(*tasks)
            else:
                raw_results = [
                    await concurrent_insert_chunk(_documents[i : i + _chunk_size])
                    for i in range(0, len(_documents), _chunk_size)
                ]

            # recast raw_results
            inserted_ids = [
                inserted_id
                for chunk_response in raw_results
                for inserted_id in (chunk_response.get("status") or {}).get(
                    "insertedIds", []
                )
            ]

            # check-raise
            if any(
                [chunk_response.get("errors", []) for chunk_response in raw_results]
            ):
                partial_result = InsertManyResult(
                    raw_results=raw_results,
                    inserted_ids=inserted_ids,
                )
                raise InsertManyException.from_responses(
                    commands=[None for _ in raw_results],
                    raw_responses=raw_results,
                    partial_result=partial_result,
                )

            # return
            full_result = InsertManyResult(
                raw_results=raw_results,
                inserted_ids=inserted_ids,
            )
            logger.info(
                f"finished inserting {len(_documents)} documents in '{self.name}'"
            )
            return full_result

    def find(
        self,
        filter: Optional[FilterType] = None,
        *,
        projection: Optional[ProjectionType] = None,
        skip: Optional[int] = None,
        limit: Optional[int] = None,
        vector: Optional[VectorType] = None,
        vectorize: Optional[str] = None,
        include_similarity: Optional[bool] = None,
        sort: Optional[SortType] = None,
        max_time_ms: Optional[int] = None,
    ) -> AsyncCursor:
        """
        Find documents on the collection, matching a certain provided filter.

        The method returns a Cursor that can then be iterated over. Depending
        on the method call pattern, the iteration over all documents can reflect
        collection mutations occurred since the `find` method was called, or not.
        In cases where the cursor reflects mutations in real-time, it will iterate
        over cursors in an approximate way (i.e. exhibiting occasional skipped
        or duplicate documents). This happens when making use of the `sort`
        option in a non-vector-search manner.

        Args:
            filter: a predicate expressed as a dictionary according to the
                Data API filter syntax. Examples are:
                    {}
                    {"name": "John"}
                    {"price": {"$le": 100}}
                    {"$and": [{"name": "John"}, {"price": {"$le": 100}}]}
                See the Data API documentation for the full set of operators.
            projection: used to select a subset of fields in the documents being
                returned. The projection can be: an iterable over the field names
                to return; a dictionary {field_name: True} to positively select
                certain fields; or a dictionary {field_name: False} if one wants
                to discard some fields from the response.
                The default is to return the whole documents.
            skip: with this integer parameter, what would be the first `skip`
                documents returned by the query are discarded, and the results
                start from the (skip+1)-th document.
                This parameter can be used only in conjunction with an explicit
                `sort` criterion of the ascending/descending type (i.e. it cannot
                be used when not sorting, nor with vector-based ANN search).
            limit: this (integer) parameter sets a limit over how many documents
                are returned. Once `limit` is reached (or the cursor is exhausted
                for lack of matching documents), nothing more is returned.
            vector: a suitable vector, i.e. a list of float numbers of the appropriate
                dimensionality, to perform vector search (i.e. ANN,
                or "approximate nearest-neighbours" search).
                When running similarity search on a collection, no other sorting
                criteria can be specified. Moreover, there is an upper bound
                to the number of documents that can be returned. For details,
                see the Note about upper bounds and the Data API documentation.
            vectorize: a string to be made into a vector to perform vector search.
                This can be supplied in (exclusive) alternative to `vector`,
                provided such a service is configured for the collection,
                and achieves the same effect.
                NOTE: This feature is under current development.
            include_similarity: a boolean to request the numeric value of the
                similarity to be returned as an added "$similarity" key in each
                returned document. Can only be used for vector ANN search, i.e.
                when either `vector` is supplied or the `sort` parameter has the
                shape {"$vector": ...}.
            sort: with this dictionary parameter one can control the order
                the documents are returned. See the Note about sorting, as well as
                the one about upper bounds, for details.
            max_time_ms: a timeout, in milliseconds, for each single one
                of the underlying HTTP requests used to fetch documents as the
                cursor is iterated over.

        Returns:
            an AsyncCursor object representing iterations over the matching documents
            (see the AsyncCursor object for how to use it. The simplest thing is to
            run a for loop: `for document in collection.sort(...):`).

        Examples:
            >>> async def run_finds(acol: AsyncCollection) -> None:
            ...             filter = {"seq": {"$exists": True}}
            ...             print("find results 1:")
            ...             async for doc in acol.find(filter, projection={"seq": True}, limit=5):
            ...                 print(doc["seq"])
            ...             async_cursor1 = acol.find(
            ...                 {},
            ...                 limit=4,
            ...                 sort={"seq": astrapy.constants.SortDocuments.DESCENDING},
            ...             )
            ...             ids = [doc["_id"] async for doc in async_cursor1]
            ...             print("find results 2:", ids)
            ...             async_cursor2 = acol.find({}, limit=3)
            ...             seqs = await async_cursor2.distinct("seq")
            ...             print("distinct results 3:", seqs)
            ...
            >>> asyncio.run(run_finds(my_async_coll))
            find results 1:
            48
            35
            7
            11
            13
            find results 2: ['d656cd9d-...', '479c7ce8-...', '96dc87fd-...', '83f0a21f-...']
            distinct results 3: [48, 35, 7]

            >>> async def run_vector_finds(acol: AsyncCollection) -> None:
            ...     await acol.insert_many([
            ...         {"tag": "A", "$vector": [4, 5]},
            ...         {"tag": "B", "$vector": [3, 4]},
            ...         {"tag": "C", "$vector": [3, 2]},
            ...         {"tag": "D", "$vector": [4, 1]},
            ...         {"tag": "E", "$vector": [2, 5]},
            ...     ])
            ...     ann_tags = [
            ...         document["tag"]
            ...         async for document in acol.find(
            ...             {},
            ...             limit=3,
            ...             vector=[3, 3],
            ...         )
            ...     ]
            ...     return ann_tags
            ...
            >>> asyncio.run(run_vector_finds(my_async_coll))
            ['A', 'B', 'C']
            # (assuming the collection has metric VectorMetric.COSINE)

        Note:
            The following are example values for the `sort` parameter.
            When no particular order is required:
                sort={}
            When sorting by a certain value in ascending/descending order:
                sort={"field": SortDocuments.ASCENDING}
                sort={"field": SortDocuments.DESCENDING}
            When sorting first by "field" and then by "subfield"
            (while modern Python versions preserve the order of dictionaries,
            it is suggested for clarity to employ a `collections.OrderedDict`
            in these cases):
                sort={
                    "field": SortDocuments.ASCENDING,
                    "subfield": SortDocuments.ASCENDING,
                }
            When running a vector similarity (ANN) search:
                sort={"$vector": [0.4, 0.15, -0.5]}

        Note:
            Some combinations of arguments impose an implicit upper bound on the
            number of documents that are returned by the Data API. More specifically:
            (a) Vector ANN searches cannot return more than a number of documents
            that at the time of writing is set to 1000 items.
            (b) When using a sort criterion of the ascending/descending type,
            the Data API will return a smaller number of documents, set to 20
            at the time of writing, and stop there. The returned documents are
            the top results across the whole collection according to the requested
            criterion.
            These provisions should be kept in mind even when subsequently running
            a command such as `.distinct()` on a cursor.

        Note:
            When not specifying sorting criteria at all (by vector or otherwise),
            the cursor can scroll through an arbitrary number of documents as
            the Data API and the client periodically exchange new chunks of documents.
            It should be noted that the behavior of the cursor in the case documents
            have been added/removed after the `find` was started depends on database
            internals and it is not guaranteed, nor excluded, that such "real-time"
            changes in the data would be picked up by the cursor.
        """

        _sort = _collate_vector_to_sort(sort, vector, vectorize)
        if include_similarity is not None and not _is_vector_sort(_sort):
            raise ValueError(
                "Cannot use `include_similarity` when not searching through `vector`."
            )
        return (
            AsyncCursor(
                collection=self,
                filter=filter,
                projection=projection,
                max_time_ms=max_time_ms,
                overall_max_time_ms=None,
            )
            .skip(skip)
            .limit(limit)
            .sort(_sort)
            .include_similarity(include_similarity)
        )

    async def find_one(
        self,
        filter: Optional[FilterType] = None,
        *,
        projection: Optional[ProjectionType] = None,
        vector: Optional[VectorType] = None,
        vectorize: Optional[str] = None,
        include_similarity: Optional[bool] = None,
        sort: Optional[SortType] = None,
        max_time_ms: Optional[int] = None,
    ) -> Union[DocumentType, None]:
        """
        Run a search, returning the first document in the collection that matches
        provided filters, if any is found.

        Args:
            filter: a predicate expressed as a dictionary according to the
                Data API filter syntax. Examples are:
                    {}
                    {"name": "John"}
                    {"price": {"$le": 100}}
                    {"$and": [{"name": "John"}, {"price": {"$le": 100}}]}
                See the Data API documentation for the full set of operators.
            projection: used to select a subset of fields in the documents being
                returned. The projection can be: an iterable over the field names
                to return; a dictionary {field_name: True} to positively select
                certain fields; or a dictionary {field_name: False} if one wants
                to discard some fields from the response.
                The default is to return the whole documents.
            vector: a suitable vector, i.e. a list of float numbers of the appropriate
                dimensionality, to perform vector search (i.e. ANN,
                or "approximate nearest-neighbours" search), extracting the most
                similar document in the collection matching the filter.
                This parameter cannot be used together with `sort`.
                See the `find` method for more details on this parameter.
            vectorize: a string to be made into a vector to perform vector search.
                This can be supplied in (exclusive) alternative to `vector`,
                provided such a service is configured for the collection,
                and achieves the same effect.
                NOTE: This feature is under current development.
            include_similarity: a boolean to request the numeric value of the
                similarity to be returned as an added "$similarity" key in the
                returned document. Can only be used for vector ANN search, i.e.
                when either `vector` is supplied or the `sort` parameter has the
                shape {"$vector": ...}.
            sort: with this dictionary parameter one can control the order
                the documents are returned. See the Note about sorting for details.
            max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

        Returns:
            a dictionary expressing the required document, otherwise None.

        Example:
            >>> async def demo_find_one(acol: AsyncCollection) -> None:
            ....    print("Count:", await acol.count_documents({}, upper_bound=100))
            ...     result0 = await acol.find_one({})
            ...     print("result0", result0)
            ...     result1 = await acol.find_one({"seq": 10})
            ...     print("result1", result1)
            ...     result2 = await acol.find_one({"seq": 1011})
            ...     print("result2", result2)
            ...     result3 = await acol.find_one({}, projection={"seq": False})
            ...     print("result3", result3)
            ...     result4 = await acol.find_one(
            ...         {},
            ...         sort={"seq": astrapy.constants.SortDocuments.DESCENDING},
            ...     )
            ...     print("result4", result4)
            ...
            >>>
            >>> asyncio.run(demo_find_one(my_async_coll))
            Count: 50
            result0 {'_id': '479c7ce8-...', 'seq': 48}
            result1 {'_id': '93e992c4-...', 'seq': 10}
            result2 None
            result3 {'_id': '479c7ce8-...'}
            result4 {'_id': 'd656cd9d-...', 'seq': 49}

            >>> asyncio.run(my_async_coll.find_one({}, vector=[1, 0]))
            {'_id': '...', 'tag': 'D', '$vector': [4.0, 1.0]}

        Note:
            See the `find` method for more details on the accepted parameters
            (whereas `skip` and `limit` are not valid parameters for `find_one`).
        """

        fo_cursor = self.find(
            filter=filter,
            projection=projection,
            skip=None,
            limit=1,
            vector=vector,
            vectorize=vectorize,
            include_similarity=include_similarity,
            sort=sort,
            max_time_ms=max_time_ms,
        )
        try:
            document = await fo_cursor.__anext__()
            return document  # type: ignore[no-any-return]
        except StopAsyncIteration:
            return None

    async def distinct(
        self,
        key: str,
        *,
        filter: Optional[FilterType] = None,
        max_time_ms: Optional[int] = None,
    ) -> List[Any]:
        """
        Return a list of the unique values of `key` across the documents
        in the collection that match the provided filter.

        Args:
            key: the name of the field whose value is inspected across documents.
                Keys can use dot-notation to descend to deeper document levels.
                Example of acceptable `key` values:
                    "field"
                    "field.subfield"
                    "field.3"
                    "field.3.subfield"
                If lists are encountered and no numeric index is specified,
                all items in the list are visited.
            filter: a predicate expressed as a dictionary according to the
                Data API filter syntax. Examples are:
                    {}
                    {"name": "John"}
                    {"price": {"$le": 100}}
                    {"$and": [{"name": "John"}, {"price": {"$le": 100}}]}
                See the Data API documentation for the full set of operators.
            max_time_ms: a timeout, in milliseconds, for the operation.

        Returns:
            a list of all different values for `key` found across the documents
            that match the filter. The result list has no repeated items.

        Example:
            >>> async def run_distinct(acol: AsyncCollection) -> None:
            ...     await acol.insert_many(
            ...         [
            ...             {"name": "Marco", "food": ["apple", "orange"], "city": "Helsinki"},
            ...             {"name": "Emma", "food": {"likes_fruit": True, "allergies": []}},
            ...         ]
            ...     )
            ...     distinct0 = await acol.distinct("name")
            ...     print("distinct('name')", distinct0)
            ...     distinct1 = await acol.distinct("city")
            ...     print("distinct('city')", distinct1)
            ...     distinct2 = await acol.distinct("food")
            ...     print("distinct('food')", distinct2)
            ...     distinct3 = await acol.distinct("food.1")
            ...     print("distinct('food.1')", distinct3)
            ...     distinct4 = await acol.distinct("food.allergies")
            ...     print("distinct('food.allergies')", distinct4)
            ...     distinct5 = await acol.distinct("food.likes_fruit")
            ...     print("distinct('food.likes_fruit')", distinct5)
            ...
            >>> asyncio.run(run_distinct(my_async_coll))
            distinct('name') ['Emma', 'Marco']
            distinct('city') ['Helsinki']
            distinct('food') [{'likes_fruit': True, 'allergies': []}, 'apple', 'orange']
            distinct('food.1') ['orange']
            distinct('food.allergies') []
            distinct('food.likes_fruit') [True]

        Note:
            It must be kept in mind that `distinct` is a client-side operation,
            which effectively browses all required documents using the logic
            of the `find` method and collects the unique values found for `key`.
            As such, there may be performance, latency and ultimately
            billing implications if the amount of matching documents is large.

        Note:
            For details on the behaviour of "distinct" in conjunction with
            real-time changes in the collection contents, see the
            Note of the `find` command.
        """

        f_cursor = AsyncCursor(
            collection=self,
            filter=filter,
            projection={key: True},
            max_time_ms=None,
            overall_max_time_ms=max_time_ms,
        )
        return await f_cursor.distinct(key)  # type: ignore[no-any-return]

    @recast_method_async
    async def count_documents(
        self,
        filter: Dict[str, Any],
        *,
        upper_bound: int,
        max_time_ms: Optional[int] = None,
    ) -> int:
        """
        Count the documents in the collection matching the specified filter.

        Args:
            filter: a predicate expressed as a dictionary according to the
                Data API filter syntax. Examples are:
                    {}
                    {"name": "John"}
                    {"price": {"$le": 100}}
                    {"$and": [{"name": "John"}, {"price": {"$le": 100}}]}
                See the Data API documentation for the full set of operators.
            upper_bound: a required ceiling on the result of the count operation.
                If the actual number of documents exceeds this value,
                an exception will be raised.
                Furthermore, if the actual number of documents exceeds the maximum
                count that the Data API can reach (regardless of upper_bound),
                an exception will be raised.
            max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

        Returns:
            the exact count of matching documents.

        Example:
            >>> async def do_count_docs(acol: AsyncCollection) -> None:
            ...     await acol.insert_many([{"seq": i} for i in range(20)])
            ...     count0 = await acol.count_documents({}, upper_bound=100)
            ...     print("count0", count0)
            ...     count1 = await acol.count_documents({"seq":{"$gt": 15}}, upper_bound=100)
            ...     print("count1", count1)
            ...     count2 = await acol.count_documents({}, upper_bound=10)
            ...     print("count2", count2)
            ...
            >>> asyncio.run(do_count_docs(my_async_coll))
            count0 20
            count1 4
            Traceback (most recent call last):
                ... ...
            astrapy.exceptions.TooManyDocumentsToCountException

        Note:
            Count operations are expensive: for this reason, the best practice
            is to provide a reasonable `upper_bound` according to the caller
            expectations. Moreover, indiscriminate usage of count operations
            for sizeable amounts of documents (i.e. in the thousands and more)
            is discouraged in favor of alternative application-specific solutions.
            Keep in mind that the Data API has a hard upper limit on the amount
            of documents it will count, and that an exception will be thrown
            by this method if this limit is encountered.
        """

        logger.info("calling count_documents")
        cd_response = await self._astra_db_collection.count_documents(
            filter=filter,
            timeout_info=base_timeout_info(max_time_ms),
        )
        logger.info("finished calling count_documents")
        if "count" in cd_response.get("status", {}):
            count: int = cd_response["status"]["count"]
            if cd_response["status"].get("moreData", False):
                raise TooManyDocumentsToCountException(
                    text=f"Document count exceeds {count}, the maximum allowed by the server",
                    server_max_count_exceeded=True,
                )
            else:
                if count > upper_bound:
                    raise TooManyDocumentsToCountException(
                        text="Document count exceeds required upper bound",
                        server_max_count_exceeded=False,
                    )
                else:
                    return count
        else:
            raise DataAPIFaultyResponseException(
                text="Faulty response from count_documents API command.",
                raw_response=cd_response,
            )

    @recast_method_async
    async def find_one_and_replace(
        self,
        filter: Dict[str, Any],
        replacement: DocumentType,
        *,
        projection: Optional[ProjectionType] = None,
        vector: Optional[VectorType] = None,
        vectorize: Optional[str] = None,
        sort: Optional[SortType] = None,
        upsert: bool = False,
        return_document: str = ReturnDocument.BEFORE,
        max_time_ms: Optional[int] = None,
    ) -> Union[DocumentType, None]:
        """
        Find a document on the collection and replace it entirely with a new one,
        optionally inserting a new one if no match is found.

        Args:

            filter: a predicate expressed as a dictionary according to the
                Data API filter syntax. Examples are:
                    {}
                    {"name": "John"}
                    {"price": {"$le": 100}}
                    {"$and": [{"name": "John"}, {"price": {"$le": 100}}]}
                See the Data API documentation for the full set of operators.
            replacement: the new document to write into the collection.
            projection: used to select a subset of fields in the document being
                returned. The projection can be: an iterable over the field names
                to return; a dictionary {field_name: True} to positively select
                certain fields; or a dictionary {field_name: False} if one wants
                to discard some fields from the response.
                The default is to return the whole documents.
            vector: a suitable vector, i.e. a list of float numbers of the appropriate
                dimensionality, to use vector search (i.e. ANN,
                or "approximate nearest-neighbours" search), as the sorting criterion.
                In this way, the matched document (if any) will be the one
                that is most similar to the provided vector.
                This parameter cannot be used together with `sort`.
                See the `find` method for more details on this parameter.
            vectorize: a string to be made into a vector to perform vector search.
                This can be supplied in (exclusive) alternative to `vector`,
                provided such a service is configured for the collection,
                and achieves the same effect.
                NOTE: This feature is under current development.
            sort: with this dictionary parameter one can control the sorting
                order of the documents matching the filter, effectively
                determining what document will come first and hence be the
                replaced one. See the `find` method for more on sorting.
            upsert: this parameter controls the behavior in absence of matches.
                If True, `replacement` is inserted as a new document
                if no matches are found on the collection. If False,
                the operation silently does nothing in case of no matches.
            return_document: a flag controlling what document is returned:
                if set to `ReturnDocument.BEFORE`, or the string "before",
                the document found on database is returned; if set to
                `ReturnDocument.AFTER`, or the string "after", the new
                document is returned. The default is "before".
            max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

        Returns:
            A document, either the one before the replace operation or the
            one after that. Alternatively, the method returns None to represent
            that no matching document was found, or that no replacement
            was inserted (depending on the `return_document` parameter).

        Example:
            >>> async def do_find_one_and_replace(acol: AsyncCollection) -> None:
            ...             await acol.insert_one({"_id": "rule1", "text": "all animals are equal"})
            ...             result0 = await acol.find_one_and_replace(
            ...                 {"_id": "rule1"},
            ...                 {"text": "some animals are more equal!"},
            ...             )
            ...             print("result0", result0)
            ...             result1 = await acol.find_one_and_replace(
            ...                 {"text": "some animals are more equal!"},
            ...                 {"text": "and the pigs are the rulers"},
            ...                 return_document=astrapy.constants.ReturnDocument.AFTER,
            ...             )
            ...             print("result1", result1)
            ...             result2 = await acol.find_one_and_replace(
            ...                 {"_id": "rule2"},
            ...                 {"text": "F=ma^2"},
            ...                 return_document=astrapy.constants.ReturnDocument.AFTER,
            ...             )
            ...             print("result2", result2)
            ...             result3 = await acol.find_one_and_replace(
            ...                 {"_id": "rule2"},
            ...                 {"text": "F=ma"},
            ...                 upsert=True,
            ...                 return_document=astrapy.constants.ReturnDocument.AFTER,
            ...                 projection={"_id": False},
            ...             )
            ...             print("result3", result3)
            ...
            >>> asyncio.run(do_find_one_and_replace(my_async_coll))
            result0 {'_id': 'rule1', 'text': 'all animals are equal'}
            result1 {'_id': 'rule1', 'text': 'and the pigs are the rulers'}
            result2 None
            result3 {'text': 'F=ma'}
        """

        _sort = _collate_vector_to_sort(sort, vector, vectorize)
        options = {
            "returnDocument": return_document,
            "upsert": upsert,
        }
        logger.info(f"calling find_one_and_replace on '{self.name}'")
        fo_response = await self._astra_db_collection.find_one_and_replace(
            replacement=replacement,
            filter=filter,
            projection=normalize_optional_projection(projection),
            sort=_sort,
            options=options,
            timeout_info=base_timeout_info(max_time_ms),
        )
        logger.info(f"finished calling find_one_and_replace on '{self.name}'")
        if "document" in fo_response.get("data", {}):
            ret_document = fo_response.get("data", {}).get("document")
            if ret_document is None:
                return None
            else:
                return ret_document  # type: ignore[no-any-return]
        else:
            raise DataAPIFaultyResponseException(
                text="Faulty response from find_one_and_replace API command.",
                raw_response=fo_response,
            )

    @recast_method_async
    async def replace_one(
        self,
        filter: Dict[str, Any],
        replacement: DocumentType,
        *,
        vector: Optional[VectorType] = None,
        vectorize: Optional[str] = None,
        sort: Optional[SortType] = None,
        upsert: bool = False,
        max_time_ms: Optional[int] = None,
    ) -> UpdateResult:
        """
        Replace a single document on the collection with a new one,
        optionally inserting a new one if no match is found.

        Args:
            filter: a predicate expressed as a dictionary according to the
                Data API filter syntax. Examples are:
                    {}
                    {"name": "John"}
                    {"price": {"$le": 100}}
                    {"$and": [{"name": "John"}, {"price": {"$le": 100}}]}
                See the Data API documentation for the full set of operators.
            replacement: the new document to write into the collection.
            vector: a suitable vector, i.e. a list of float numbers of the appropriate
                dimensionality, to use vector search (i.e. ANN,
                or "approximate nearest-neighbours" search), as the sorting criterion.
                In this way, the matched document (if any) will be the one
                that is most similar to the provided vector.
                This parameter cannot be used together with `sort`.
                See the `find` method for more details on this parameter.
            vectorize: a string to be made into a vector to perform vector search.
                This can be supplied in (exclusive) alternative to `vector`,
                provided such a service is configured for the collection,
                and achieves the same effect.
                NOTE: This feature is under current development.
            sort: with this dictionary parameter one can control the sorting
                order of the documents matching the filter, effectively
                determining what document will come first and hence be the
                replaced one. See the `find` method for more on sorting.
            upsert: this parameter controls the behavior in absence of matches.
                If True, `replacement` is inserted as a new document
                if no matches are found on the collection. If False,
                the operation silently does nothing in case of no matches.
            max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

        Returns:
            an UpdateResult object summarizing the outcome of the replace operation.

        Example:
            >>> async def do_replace_one(acol: AsyncCollection) -> None:
            ...     await acol.insert_one({"Marco": "Polo"})
            ...     result0 = await acol.replace_one(
            ...         {"Marco": {"$exists": True}},
            ...         {"Buda": "Pest"},
            ...     )
            ...     print("result0.update_info", result0.update_info)
            ...     doc1 = await acol.find_one({"Buda": "Pest"})
            ...     print("doc1", doc1)
            ...     result1 = await acol.replace_one(
            ...         {"Mirco": {"$exists": True}},
            ...         {"Oh": "yeah?"},
            ...     )
            ...     print("result1.update_info", result1.update_info)
            ...     result2 = await acol.replace_one(
            ...         {"Mirco": {"$exists": True}},
            ...         {"Oh": "yeah?"},
            ...         upsert=True,
            ...     )
            ...     print("result2.update_info", result2.update_info)
            ...
            >>> asyncio.run(do_replace_one(my_async_coll))
            result0.update_info {'n': 1, 'updatedExisting': True, 'ok': 1.0, 'nModified': 1}
            doc1 {'_id': '6e669a5a-...', 'Buda': 'Pest'}
            result1.update_info {'n': 0, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0}
            result2.update_info {'n': 1, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0, 'upserted': '30e34e00-...'}
        """

        _sort = _collate_vector_to_sort(sort, vector, vectorize)
        options = {
            "upsert": upsert,
        }
        logger.info(f"calling find_one_and_replace on '{self.name}'")
        fo_response = await self._astra_db_collection.find_one_and_replace(
            replacement=replacement,
            filter=filter,
            sort=_sort,
            options=options,
            timeout_info=base_timeout_info(max_time_ms),
        )
        logger.info(f"finished calling find_one_and_replace on '{self.name}'")
        if "document" in fo_response.get("data", {}):
            fo_status = fo_response.get("status") or {}
            _update_info = _prepare_update_info([fo_status])
            return UpdateResult(
                raw_results=[fo_response],
                update_info=_update_info,
            )
        else:
            raise DataAPIFaultyResponseException(
                text="Faulty response from find_one_and_replace API command.",
                raw_response=fo_response,
            )

    @recast_method_async
    async def find_one_and_update(
        self,
        filter: Dict[str, Any],
        update: Dict[str, Any],
        *,
        projection: Optional[ProjectionType] = None,
        vector: Optional[VectorType] = None,
        vectorize: Optional[str] = None,
        sort: Optional[SortType] = None,
        upsert: bool = False,
        return_document: str = ReturnDocument.BEFORE,
        max_time_ms: Optional[int] = None,
    ) -> Union[DocumentType, None]:
        """
        Find a document on the collection and update it as requested,
        optionally inserting a new one if no match is found.

        Args:
            filter: a predicate expressed as a dictionary according to the
                Data API filter syntax. Examples are:
                    {}
                    {"name": "John"}
                    {"price": {"$le": 100}}
                    {"$and": [{"name": "John"}, {"price": {"$le": 100}}]}
                See the Data API documentation for the full set of operators.
            update: the update prescription to apply to the document, expressed
                as a dictionary as per Data API syntax. Examples are:
                    {"$set": {"field": "value}}
                    {"$inc": {"counter": 10}}
                    {"$unset": {"field": ""}}
                See the Data API documentation for the full syntax.
            projection: used to select a subset of fields in the document being
                returned. The projection can be: an iterable over the field names
                to return; a dictionary {field_name: True} to positively select
                certain fields; or a dictionary {field_name: False} if one wants
                to discard some fields from the response.
                The default is to return the whole documents.
            vector: a suitable vector, i.e. a list of float numbers of the appropriate
                dimensionality, to use vector search (i.e. ANN,
                or "approximate nearest-neighbours" search), as the sorting criterion.
                In this way, the matched document (if any) will be the one
                that is most similar to the provided vector.
                This parameter cannot be used together with `sort`.
                See the `find` method for more details on this parameter.
            vectorize: a string to be made into a vector to perform vector search.
                This can be supplied in (exclusive) alternative to `vector`,
                provided such a service is configured for the collection,
                and achieves the same effect.
                NOTE: This feature is under current development.
            sort: with this dictionary parameter one can control the sorting
                order of the documents matching the filter, effectively
                determining what document will come first and hence be the
                updated one. See the `find` method for more on sorting.
            upsert: this parameter controls the behavior in absence of matches.
                If True, a new document (resulting from applying the `update`
                to an empty document) is inserted if no matches are found on
                the collection. If False, the operation silently does nothing
                in case of no matches.
            return_document: a flag controlling what document is returned:
                if set to `ReturnDocument.BEFORE`, or the string "before",
                the document found on database is returned; if set to
                `ReturnDocument.AFTER`, or the string "after", the new
                document is returned. The default is "before".
            max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

        Returns:
            A document (or a projection thereof, as required), either the one
            before the replace operation or the one after that.
            Alternatively, the method returns None to represent
            that no matching document was found, or that no update
            was applied (depending on the `return_document` parameter).

        Example:
            >>> async def do_find_one_and_update(acol: AsyncCollection) -> None:
            ...     await acol.insert_one({"Marco": "Polo"})
            ...     result0 = await acol.find_one_and_update(
            ...         {"Marco": {"$exists": True}},
            ...         {"$set": {"title": "Mr."}},
            ...     )
            ...     print("result0", result0)
            ...     result1 = await acol.find_one_and_update(
            ...         {"title": "Mr."},
            ...         {"$inc": {"rank": 3}},
            ...         projection=["title", "rank"],
            ...         return_document=astrapy.constants.ReturnDocument.AFTER,
            ...     )
            ...     print("result1", result1)
            ...     result2 = await acol.find_one_and_update(
            ...         {"name": "Johnny"},
            ...         {"$set": {"rank": 0}},
            ...         return_document=astrapy.constants.ReturnDocument.AFTER,
            ...     )
            ...     print("result2", result2)
            ...     result3 = await acol.find_one_and_update(
            ...         {"name": "Johnny"},
            ...         {"$set": {"rank": 0}},
            ...         upsert=True,
            ...         return_document=astrapy.constants.ReturnDocument.AFTER,
            ...     )
            ...     print("result3", result3)
            ...
            >>> asyncio.run(do_find_one_and_update(my_async_coll))
            result0 {'_id': 'f7c936d3-b0a0-45eb-a676-e2829662a57c', 'Marco': 'Polo'}
            result1 {'_id': 'f7c936d3-b0a0-45eb-a676-e2829662a57c', 'title': 'Mr.', 'rank': 3}
            result2 None
            result3 {'_id': 'db3d678d-14d4-4caa-82d2-d5fb77dab7ec', 'name': 'Johnny', 'rank': 0}
        """

        _sort = _collate_vector_to_sort(sort, vector, vectorize)
        options = {
            "returnDocument": return_document,
            "upsert": upsert,
        }
        logger.info(f"calling find_one_and_update on '{self.name}'")
        fo_response = await self._astra_db_collection.find_one_and_update(
            update=update,
            filter=filter,
            projection=normalize_optional_projection(projection),
            sort=_sort,
            options=options,
            timeout_info=base_timeout_info(max_time_ms),
        )
        logger.info(f"finished calling find_one_and_update on '{self.name}'")
        if "document" in fo_response.get("data", {}):
            ret_document = fo_response.get("data", {}).get("document")
            if ret_document is None:
                return None
            else:
                return ret_document  # type: ignore[no-any-return]
        else:
            raise DataAPIFaultyResponseException(
                text="Faulty response from find_one_and_update API command.",
                raw_response=fo_response,
            )

    @recast_method_async
    async def update_one(
        self,
        filter: Dict[str, Any],
        update: Dict[str, Any],
        *,
        vector: Optional[VectorType] = None,
        vectorize: Optional[str] = None,
        sort: Optional[SortType] = None,
        upsert: bool = False,
        max_time_ms: Optional[int] = None,
    ) -> UpdateResult:
        """
        Update a single document on the collection as requested,
        optionally inserting a new one if no match is found.

        Args:
            filter: a predicate expressed as a dictionary according to the
                Data API filter syntax. Examples are:
                    {}
                    {"name": "John"}
                    {"price": {"$le": 100}}
                    {"$and": [{"name": "John"}, {"price": {"$le": 100}}]}
                See the Data API documentation for the full set of operators.
            update: the update prescription to apply to the document, expressed
                as a dictionary as per Data API syntax. Examples are:
                    {"$set": {"field": "value}}
                    {"$inc": {"counter": 10}}
                    {"$unset": {"field": ""}}
                See the Data API documentation for the full syntax.
            vector: a suitable vector, i.e. a list of float numbers of the appropriate
                dimensionality, to use vector search (i.e. ANN,
                or "approximate nearest-neighbours" search), as the sorting criterion.
                In this way, the matched document (if any) will be the one
                that is most similar to the provided vector.
                This parameter cannot be used together with `sort`.
                See the `find` method for more details on this parameter.
            vectorize: a string to be made into a vector to perform vector search.
                This can be supplied in (exclusive) alternative to `vector`,
                provided such a service is configured for the collection,
                and achieves the same effect.
                NOTE: This feature is under current development.
            sort: with this dictionary parameter one can control the sorting
                order of the documents matching the filter, effectively
                determining what document will come first and hence be the
                updated one. See the `find` method for more on sorting.
            upsert: this parameter controls the behavior in absence of matches.
                If True, a new document (resulting from applying the `update`
                to an empty document) is inserted if no matches are found on
                the collection. If False, the operation silently does nothing
                in case of no matches.
            max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

        Returns:
            an UpdateResult object summarizing the outcome of the update operation.

        Example:
            >>> async def do_update_one(acol: AsyncCollection) -> None:
            ...     await acol.insert_one({"Marco": "Polo"})
            ...     result0 = await acol.update_one(
            ...         {"Marco": {"$exists": True}},
            ...         {"$inc": {"rank": 3}},
            ...     )
            ...     print("result0.update_info", result0.update_info)
            ...     result1 = await acol.update_one(
            ...         {"Mirko": {"$exists": True}},
            ...         {"$inc": {"rank": 3}},
            ...     )
            ...     print("result1.update_info", result1.update_info)
            ...     result2 = await acol.update_one(
            ...         {"Mirko": {"$exists": True}},
            ...         {"$inc": {"rank": 3}},
            ...         upsert=True,
            ...     )
            ...     print("result2.update_info", result2.update_info)
            ...
            >>> asyncio.run(do_update_one(my_async_coll))
            result0.update_info {'n': 1, 'updatedExisting': True, 'ok': 1.0, 'nModified': 1})
            result1.update_info {'n': 0, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0})
            result2.update_info {'n': 1, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0, 'upserted': '75748092-...'}
        """

        _sort = _collate_vector_to_sort(sort, vector, vectorize)
        options = {
            "upsert": upsert,
        }
        logger.info(f"calling find_one_and_update on '{self.name}'")
        fo_response = await self._astra_db_collection.find_one_and_update(
            update=update,
            sort=_sort,
            filter=filter,
            options=options,
            timeout_info=base_timeout_info(max_time_ms),
        )
        logger.info(f"finished calling find_one_and_update on '{self.name}'")
        if "document" in fo_response.get("data", {}):
            fo_status = fo_response.get("status") or {}
            _update_info = _prepare_update_info([fo_status])
            return UpdateResult(
                raw_results=[fo_response],
                update_info=_update_info,
            )
        else:
            raise DataAPIFaultyResponseException(
                text="Faulty response from find_one_and_update API command.",
                raw_response=fo_response,
            )

    @recast_method_async
    async def update_many(
        self,
        filter: Dict[str, Any],
        update: Dict[str, Any],
        *,
        upsert: bool = False,
        max_time_ms: Optional[int] = None,
    ) -> UpdateResult:
        """
        Apply an update operations to all documents matching a condition,
        optionally inserting one documents in absence of matches.

        Args:
            filter: a predicate expressed as a dictionary according to the
                Data API filter syntax. Examples are:
                    {}
                    {"name": "John"}
                    {"price": {"$le": 100}}
                    {"$and": [{"name": "John"}, {"price": {"$le": 100}}]}
                See the Data API documentation for the full set of operators.
            update: the update prescription to apply to the documents, expressed
                as a dictionary as per Data API syntax. Examples are:
                    {"$set": {"field": "value}}
                    {"$inc": {"counter": 10}}
                    {"$unset": {"field": ""}}
                See the Data API documentation for the full syntax.
            upsert: this parameter controls the behavior in absence of matches.
                If True, a single new document (resulting from applying `update`
                to an empty document) is inserted if no matches are found on
                the collection. If False, the operation silently does nothing
                in case of no matches.
            max_time_ms: a timeout, in milliseconds, for the operation.

        Returns:
            an UpdateResult object summarizing the outcome of the update operation.

        Example:
            >>> async def do_update_many(acol: AsyncCollection) -> None:
            ...     await acol.insert_many([{"c": "red"}, {"c": "green"}, {"c": "blue"}])
            ...     result0 = await acol.update_many(
            ...         {"c": {"$ne": "green"}},
            ...         {"$set": {"nongreen": True}},
            ...     )
            ...     print("result0.update_info", result0.update_info)
            ...     result1 = await acol.update_many(
            ...         {"c": "orange"},
            ...         {"$set": {"is_also_fruit": True}},
            ...     )
            ...     print("result1.update_info", result1.update_info)
            ...     result2 = await acol.update_many(
            ...         {"c": "orange"},
            ...         {"$set": {"is_also_fruit": True}},
            ...         upsert=True,
            ...     )
            ...     print("result2.update_info", result2.update_info)
            ...
            >>> asyncio.run(do_update_many(my_async_coll))
            result0.update_info {'n': 2, 'updatedExisting': True, 'ok': 1.0, 'nModified': 2}
            result1.update_info {'n': 0, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0}
            result2.update_info {'n': 1, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0, 'upserted': '79ffd5a3-ab99-4dff-a2a5-4aaa0e59e854'}

        Note:
            Similarly to the case of `find` (see its docstring for more details),
            running this command while, at the same time, another process is
            inserting new documents which match the filter of the `update_many`
            can result in an unpredictable fraction of these documents being updated.
            In other words, it cannot be easily predicted whether a given
            newly-inserted document will be picked up by the update_many command or not.
        """

        base_options = {
            "upsert": upsert,
        }
        page_state_options: Dict[str, str] = {}
        um_responses: List[Dict[str, Any]] = []
        um_statuses: List[Dict[str, Any]] = []
        must_proceed = True
        timeout_manager = MultiCallTimeoutManager(overall_max_time_ms=max_time_ms)
        logger.info(f"starting update_many on '{self.name}'")
        while must_proceed:
            options = {**base_options, **page_state_options}
            logger.info(f"calling update_many on '{self.name}'")
            this_um_response = await self._astra_db_collection.update_many(
                update=update,
                filter=filter,
                options=options,
                timeout_info=timeout_manager.remaining_timeout_info(),
            )
            logger.info(f"finished calling update_many on '{self.name}'")
            this_um_status = this_um_response.get("status") or {}
            #
            # if errors, quit early
            if this_um_response.get("errors", []):
                partial_update_info = _prepare_update_info(um_statuses)
                partial_result = UpdateResult(
                    raw_results=um_responses,
                    update_info=partial_update_info,
                )
                all_um_responses = um_responses + [this_um_response]
                raise UpdateManyException.from_responses(
                    commands=[None for _ in all_um_responses],
                    raw_responses=all_um_responses,
                    partial_result=partial_result,
                )
            else:
                if "status" not in this_um_response:
                    raise DataAPIFaultyResponseException(
                        text="Faulty response from update_many API command.",
                        raw_response=this_um_response,
                    )
                um_responses.append(this_um_response)
                um_statuses.append(this_um_status)
                next_page_state = this_um_status.get("nextPageState")
                if next_page_state is not None:
                    must_proceed = True
                    page_state_options = {"pageState": next_page_state}
                else:
                    must_proceed = False
                    page_state_options = {}

        update_info = _prepare_update_info(um_statuses)
        logger.info(f"finished update_many on '{self.name}'")
        return UpdateResult(
            raw_results=um_responses,
            update_info=update_info,
        )

    @recast_method_async
    async def find_one_and_delete(
        self,
        filter: Dict[str, Any],
        *,
        projection: Optional[ProjectionType] = None,
        vector: Optional[VectorType] = None,
        vectorize: Optional[str] = None,
        sort: Optional[SortType] = None,
        max_time_ms: Optional[int] = None,
    ) -> Union[DocumentType, None]:
        """
        Find a document in the collection and delete it. The deleted document,
        however, is the return value of the method.

        Args:
            filter: a predicate expressed as a dictionary according to the
                Data API filter syntax. Examples are:
                    {}
                    {"name": "John"}
                    {"price": {"$le": 100}}
                    {"$and": [{"name": "John"}, {"price": {"$le": 100}}]}
                See the Data API documentation for the full set of operators.
            projection: used to select a subset of fields in the document being
                returned. The projection can be: an iterable over the field names
                to return; a dictionary {field_name: True} to positively select
                certain fields; or a dictionary {field_name: False} if one wants
                to discard some fields from the response.
                Note that the `_id` field will be returned with the document
                in any case, regardless of what the provided `projection` requires.
                The default is to return the whole documents.
            vector: a suitable vector, i.e. a list of float numbers of the appropriate
                dimensionality, to use vector search (i.e. ANN,
                or "approximate nearest-neighbours" search), as the sorting criterion.
                In this way, the matched document (if any) will be the one
                that is most similar to the provided vector.
                This parameter cannot be used together with `sort`.
                See the `find` method for more details on this parameter.
            vectorize: a string to be made into a vector to perform vector search.
                This can be supplied in (exclusive) alternative to `vector`,
                provided such a service is configured for the collection,
                and achieves the same effect.
                NOTE: This feature is under current development.
            sort: with this dictionary parameter one can control the sorting
                order of the documents matching the filter, effectively
                determining what document will come first and hence be the
                deleted one. See the `find` method for more on sorting.
            max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

        Returns:
            Either the document (or a projection thereof, as requested), or None
            if no matches were found in the first place.

        Example:
            >>> async def do_find_one_and_delete(acol: AsyncCollection) -> None:
            ...     await acol.insert_many(
            ...         [
            ...             {"species": "swan", "class": "Aves"},
            ...             {"species": "frog", "class": "Amphibia"},
            ...         ],
            ...     )
            ...     delete_result0 = await acol.find_one_and_delete(
            ...         {"species": {"$ne": "frog"}},
            ...         projection=["species"],
            ...     )
            ...     print("delete_result0", delete_result0)
            ...     delete_result1 = await acol.find_one_and_delete(
            ...         {"species": {"$ne": "frog"}},
            ...     )
            ...     print("delete_result1", delete_result1)
            ...
            >>> asyncio.run(do_find_one_and_delete(my_async_coll))
            delete_result0 {'_id': 'f335cd0f-...', 'species': 'swan'}
            delete_result1 None
        """

        _sort = _collate_vector_to_sort(sort, vector, vectorize)
        _projection = normalize_optional_projection(projection)
        logger.info(f"calling find_one_and_delete on '{self.name}'")
        fo_response = await self._astra_db_collection.find_one_and_delete(
            sort=_sort,
            filter=filter,
            projection=_projection,
            timeout_info=base_timeout_info(max_time_ms),
        )
        logger.info(f"finished calling find_one_and_delete on '{self.name}'")
        if "document" in fo_response.get("data", {}):
            document = fo_response["data"]["document"]
            return document  # type: ignore[no-any-return]
        else:
            deleted_count = fo_response.get("status", {}).get("deletedCount")
            if deleted_count == 0:
                return None
            else:
                raise DataAPIFaultyResponseException(
                    text="Faulty response from find_one_and_delete API command.",
                    raw_response=fo_response,
                )

    @recast_method_async
    async def delete_one(
        self,
        filter: Dict[str, Any],
        *,
        vector: Optional[VectorType] = None,
        vectorize: Optional[str] = None,
        sort: Optional[SortType] = None,
        max_time_ms: Optional[int] = None,
    ) -> DeleteResult:
        """
        Delete one document matching a provided filter.
        This method never deletes more than a single document, regardless
        of the number of matches to the provided filters.

        Args:
            filter: a predicate expressed as a dictionary according to the
                Data API filter syntax. Examples are:
                    {}
                    {"name": "John"}
                    {"price": {"$le": 100}}
                    {"$and": [{"name": "John"}, {"price": {"$le": 100}}]}
                See the Data API documentation for the full set of operators.
            vector: a suitable vector, i.e. a list of float numbers of the appropriate
                dimensionality, to use vector search (i.e. ANN,
                or "approximate nearest-neighbours" search), as the sorting criterion.
                In this way, the matched document (if any) will be the one
                that is most similar to the provided vector.
                This parameter cannot be used together with `sort`.
                See the `find` method for more details on this parameter.
            vectorize: a string to be made into a vector to perform vector search.
                This can be supplied in (exclusive) alternative to `vector`,
                provided such a service is configured for the collection,
                and achieves the same effect.
                NOTE: This feature is under current development.
            sort: with this dictionary parameter one can control the sorting
                order of the documents matching the filter, effectively
                determining what document will come first and hence be the
                deleted one. See the `find` method for more on sorting.
            max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

        Returns:
            a DeleteResult object summarizing the outcome of the delete operation.

        Example:
            >>> my_coll.insert_many([{"seq": 1}, {"seq": 0}, {"seq": 2}])
            InsertManyResult(...)
            >>> my_coll.delete_one({"seq": 1})
            DeleteResult(raw_results=..., deleted_count=1)
            >>> my_coll.distinct("seq")
            [0, 2]
            >>> my_coll.delete_one(
            ...     {"seq": {"$exists": True}},
            ...     sort={"seq": astrapy.constants.SortDocuments.DESCENDING},
            ... )
            DeleteResult(raw_results=..., deleted_count=1)
            >>> my_coll.distinct("seq")
            [0]
            >>> my_coll.delete_one({"seq": 2})
            DeleteResult(raw_results=..., deleted_count=0)
        """

        _sort = _collate_vector_to_sort(sort, vector, vectorize)
        logger.info(f"calling delete_one_by_predicate on '{self.name}'")
        do_response = await self._astra_db_collection.delete_one_by_predicate(
            filter=filter,
            timeout_info=base_timeout_info(max_time_ms),
            sort=_sort,
        )
        logger.info(f"finished calling delete_one_by_predicate on '{self.name}'")
        if "deletedCount" in do_response.get("status", {}):
            deleted_count = do_response["status"]["deletedCount"]
            if deleted_count == -1:
                return DeleteResult(
                    deleted_count=None,
                    raw_results=[do_response],
                )
            else:
                # expected a non-negative integer:
                return DeleteResult(
                    deleted_count=deleted_count,
                    raw_results=[do_response],
                )
        else:
            raise DataAPIFaultyResponseException(
                text="Faulty response from delete_one API command.",
                raw_response=do_response,
            )

    @recast_method_async
    async def delete_many(
        self,
        filter: Dict[str, Any],
        *,
        max_time_ms: Optional[int] = None,
    ) -> DeleteResult:
        """
        Delete all documents matching a provided filter.

        Args:
            filter: a predicate expressed as a dictionary according to the
                Data API filter syntax. Examples are:
                    {}
                    {"name": "John"}
                    {"price": {"$le": 100}}
                    {"$and": [{"name": "John"}, {"price": {"$le": 100}}]}
                See the Data API documentation for the full set of operators.
                The `delete_many` method does not accept an empty filter: see
                `delete_all` to completely erase all contents of a collection
            max_time_ms: a timeout, in milliseconds, for the operation.

        Returns:
            a DeleteResult object summarizing the outcome of the delete operation.

        Example:
            >>> async def do_delete_many(acol: AsyncCollection) -> None:
            ...     await acol.insert_many([{"seq": 1}, {"seq": 0}, {"seq": 2}])
            ...     delete_result0 = await acol.delete_many({"seq": {"$lte": 1}})
            ...     print("delete_result0.deleted_count", delete_result0.deleted_count)
            ...     distinct1 = await acol.distinct("seq")
            ...     print("distinct1", distinct1)
            ...     delete_result2 = await acol.delete_many({"seq": {"$lte": 1}})
            ...     print("delete_result2.deleted_count", delete_result2.deleted_count)
            ...
            >>> asyncio.run(do_delete_many(my_async_coll))
            delete_result0.deleted_count 2
            distinct1 [2]
            delete_result2.deleted_count 0

        Note:
            This operation is not atomic. Depending on the amount of matching
            documents, it can keep running (in a blocking way) for a macroscopic
            time. In that case, new documents that are meanwhile inserted
            (e.g. from another process/application) will be deleted during
            the execution of this method call until the collection is devoid
            of matches.
        """

        if not filter:
            raise ValueError(
                "The `filter` parameter to method `delete_many` cannot be "
                "empty. In order to completely clear the contents of a "
                "collection, please use the `delete_all` method."
            )

        dm_responses: List[Dict[str, Any]] = []
        deleted_count = 0
        must_proceed = True
        timeout_manager = MultiCallTimeoutManager(overall_max_time_ms=max_time_ms)
        logger.info(f"starting delete_many on '{self.name}'")
        while must_proceed:
            logger.info(f"calling delete_many on '{self.name}'")
            this_dm_response = await self._astra_db_collection.delete_many(
                filter=filter,
                skip_error_check=True,
                timeout_info=timeout_manager.remaining_timeout_info(),
            )
            logger.info(f"finished calling delete_many on '{self.name}'")
            # if errors, quit early
            if this_dm_response.get("errors", []):
                partial_result = DeleteResult(
                    deleted_count=deleted_count,
                    raw_results=dm_responses,
                )
                all_dm_responses = dm_responses + [this_dm_response]
                raise DeleteManyException.from_responses(
                    commands=[None for _ in all_dm_responses],
                    raw_responses=all_dm_responses,
                    partial_result=partial_result,
                )
            else:
                this_dc = this_dm_response.get("status", {}).get("deletedCount")
                if this_dc is None or this_dc < 0:
                    raise DataAPIFaultyResponseException(
                        text="Faulty response from delete_many API command.",
                        raw_response=this_dm_response,
                    )
                dm_responses.append(this_dm_response)
                deleted_count += this_dc
                must_proceed = this_dm_response.get("status", {}).get("moreData", False)

        logger.info(f"finished delete_many on '{self.name}'")
        return DeleteResult(
            deleted_count=deleted_count,
            raw_results=dm_responses,
        )

    @recast_method_async
    async def delete_all(self, *, max_time_ms: Optional[int] = None) -> Dict[str, Any]:
        """
        Delete all documents in a collection.

        Args:
            max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

        Returns:
            a dictionary of the form {"ok": 1} to signal successful deletion.

        Example:
            >>> async def do_delete_all(acol: AsyncCollection) -> None:
            ...     distinct0 = await acol.distinct("seq")
            ...     print("distinct0", distinct0)
            ...     count1 = await acol.count_documents({}, upper_bound=100)
            ...     print("count1", count1)
            ...     delete_result2 = await acol.delete_all()
            ...     print("delete_result2", delete_result2)
            ...     count3 = await acol.count_documents({}, upper_bound=100)
            ...     print("count3", count3)
            ...
            >>> asyncio.run(do_delete_all(my_async_coll))
            distinct0 [4, 2, 3, 0, 1]
            count1 5
            delete_result2 {'ok': 1}
            count3 0

        Note:
            Use with caution.

        Note:
            Once the method succeeds, methods on this object can still be invoked:
            however, this hardly makes sense as the underlying actual collection
            is no more.
            It is responsibility of the developer to design a correct flow
            which avoids using a deceased collection any further.
        """

        logger.info(f"calling unfiltered delete_many on '{self.name}'")
        dm_response = await self._astra_db_collection.delete_many(
            filter={}, timeout_info=base_timeout_info(max_time_ms)
        )
        logger.info(f"finished calling unfiltered delete_many on '{self.name}'")
        deleted_count = dm_response["status"]["deletedCount"]
        if deleted_count == -1:
            return {"ok": 1}
        else:
            raise DataAPIFaultyResponseException(
                text="Faulty response from delete_many API command.",
                raw_response=dm_response,
            )

    async def bulk_write(
        self,
        requests: Iterable[AsyncBaseOperation],
        *,
        ordered: bool = True,
        concurrency: Optional[int] = None,
        max_time_ms: Optional[int] = None,
    ) -> BulkWriteResult:
        """
        Execute an arbitrary amount of operations such as inserts, updates, deletes
        either sequentially or concurrently.

        This method does not execute atomically, i.e. individual operations are
        each performed in the same way as the corresponding collection method,
        and each one is a different and unrelated database mutation.

        Args:
            requests: an iterable over concrete subclasses of `BaseOperation`,
                such as `AsyncInsertMany` or `AsyncReplaceOne`. Each such object
                represents an operation ready to be executed on a collection,
                and is instantiated by passing the same parameters as one
                would the corresponding collection method.
            ordered: whether to launch the `requests` one after the other or
                in arbitrary order, possibly in a concurrent fashion. For
                performance reasons, `ordered=False` should be preferred
                when compatible with the needs of the application flow.
            concurrency: maximum number of concurrent operations executing at
                a given time. It cannot be more than one for ordered bulk writes.
            max_time_ms: a timeout, in milliseconds, for the whole bulk write.
                Remember that, if the method call times out, then there's no
                guarantee about what portion of the bulk write has been received
                and successfully executed by the Data API.

        Returns:
            A single BulkWriteResult summarizing the whole list of requested
            operations. The keys in the map attributes of BulkWriteResult
            (when present) are the integer indices of the corresponding operation
            in the `requests` iterable.

        Example:
            >>> from astrapy.operations import AsyncInsertMany, AsyncReplaceOne, AsyncOperation
            >>> from astrapy.results import BulkWriteResult
            >>>
            >>> async def do_bulk_write(
            ...     acol: AsyncCollection,
            ...     async_operations: List[AsyncOperation],
            ... ) -> BulkWriteResult:
            ...     bw_result = await acol.bulk_write(async_operations)
            ...     count0 = await acol.count_documents({}, upper_bound=100)
            ...     print("count0", count0)
            ...     distinct0 = await acol.distinct("replaced")
            ...     print("distinct0", distinct0)
            ...     return bw_result
            ...
            >>> op1 = AsyncInsertMany([{"a": 1}, {"a": 2}])
            >>> op2 = AsyncReplaceOne(
            ...     {"z": 9},
            ...     replacement={"z": 9, "replaced": True},
            ...     upsert=True,
            ... )
            >>> result = asyncio.run(do_bulk_write(my_async_coll, [op1, op2]))
            count0 3
            distinct0 [True]
            >>> print("result", result)
            result BulkWriteResult(bulk_api_results={0: ..., 1: ...}, deleted_count=0, inserted_count=3, matched_count=0, modified_count=0, upserted_count=1, upserted_ids={1: 'ccd0a800-...'})
        """

        # lazy importing here against circular-import error
        from astrapy.operations import reduce_bulk_write_results

        if concurrency is None:
            if ordered:
                _concurrency = 1
            else:
                _concurrency = DEFAULT_BULK_WRITE_CONCURRENCY
        else:
            _concurrency = concurrency
        if _concurrency > 1 and ordered:
            raise ValueError("Cannot run ordered bulk_write concurrently.")
        timeout_manager = MultiCallTimeoutManager(overall_max_time_ms=max_time_ms)
        logger.info(f"startng a bulk write on '{self.name}'")
        if ordered:
            bulk_write_results: List[BulkWriteResult] = []
            for operation_i, operation in enumerate(requests):
                try:
                    this_bw_result = await operation.execute(
                        self,
                        index_in_bulk_write=operation_i,
                        bulk_write_timeout_ms=timeout_manager.remaining_timeout_ms(),
                    )
                    bulk_write_results.append(this_bw_result)
                except CumulativeOperationException as exc:
                    partial_result = exc.partial_result
                    partial_bw_result = reduce_bulk_write_results(
                        bulk_write_results
                        + [
                            partial_result.to_bulk_write_result(
                                index_in_bulk_write=operation_i
                            )
                        ]
                    )
                    dar_exception = exc.data_api_response_exception()
                    raise BulkWriteException(
                        text=dar_exception.text,
                        error_descriptors=dar_exception.error_descriptors,
                        detailed_error_descriptors=dar_exception.detailed_error_descriptors,
                        partial_result=partial_bw_result,
                        exceptions=[dar_exception],
                    )
                except DataAPIResponseException as exc:
                    # the cumulative exceptions, with their
                    # partially-done-info, are handled above:
                    # here it's just one-shot d.a.r. exceptions
                    partial_bw_result = reduce_bulk_write_results(bulk_write_results)
                    dar_exception = exc.data_api_response_exception()
                    raise BulkWriteException(
                        text=dar_exception.text,
                        error_descriptors=dar_exception.error_descriptors,
                        detailed_error_descriptors=dar_exception.detailed_error_descriptors,
                        partial_result=partial_bw_result,
                        exceptions=[dar_exception],
                    )
            full_bw_result = reduce_bulk_write_results(bulk_write_results)
            logger.info(f"finished a bulk write on '{self.name}'")
            return full_bw_result
        else:

            sem = asyncio.Semaphore(_concurrency)

            async def _concurrent_execute_as_either(
                operation: AsyncBaseOperation, operation_i: int
            ) -> Tuple[Optional[BulkWriteResult], Optional[DataAPIResponseException]]:
                async with sem:
                    try:
                        ex_result = await operation.execute(
                            self,
                            index_in_bulk_write=operation_i,
                            bulk_write_timeout_ms=timeout_manager.remaining_timeout_ms(),
                        )
                        return (ex_result, None)
                    except DataAPIResponseException as exc:
                        return (None, exc)

            tasks = [
                asyncio.create_task(
                    _concurrent_execute_as_either(operation, operation_i)
                )
                for operation_i, operation in enumerate(requests)
            ]
            bulk_write_either_results = await asyncio.gather(*tasks)
            # regroup
            bulk_write_successes = [bwr for bwr, _ in bulk_write_either_results if bwr]
            bulk_write_failures = [bwf for _, bwf in bulk_write_either_results if bwf]
            if bulk_write_failures:
                # extract and cumulate
                partial_results_from_failures = [
                    failure.partial_result.to_bulk_write_result(
                        index_in_bulk_write=operation_i
                    )
                    for failure in bulk_write_failures
                    if isinstance(failure, CumulativeOperationException)
                ]
                partial_bw_result = reduce_bulk_write_results(
                    bulk_write_successes + partial_results_from_failures
                )
                # raise and recast the first exception
                all_dar_exceptions = [
                    bw_failure.data_api_response_exception()
                    for bw_failure in bulk_write_failures
                ]
                dar_exception = all_dar_exceptions[0]
                raise BulkWriteException(
                    text=dar_exception.text,
                    error_descriptors=dar_exception.error_descriptors,
                    detailed_error_descriptors=dar_exception.detailed_error_descriptors,
                    partial_result=partial_bw_result,
                    exceptions=all_dar_exceptions,
                )
            else:
                logger.info(f"finished a bulk write on '{self.name}'")
                return reduce_bulk_write_results(bulk_write_successes)

    async def drop(self, *, max_time_ms: Optional[int] = None) -> Dict[str, Any]:
        """
        Drop the collection, i.e. delete it from the database along with
        all the documents it contains.

        Args:
            max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.
                Remember there is not guarantee that a request that has
                timed out us not in fact honored.

        Returns:
            a dictionary of the form {"ok": 1} to signal successful deletion.

        Example:
            >>> async def drop_and_check(acol: AsyncCollection) -> None:
            ...     doc0 = await acol.find_one({})
            ...     print("doc0", doc0)
            ...     drop_result = await acol.drop()
            ...     print("drop_result", drop_result)
            ...     doc1 = await acol.find_one({})
            ...
            >>> asyncio.run(drop_and_check(my_async_coll))
            doc0 {'_id': '...', 'z': -10}
            drop_result {'ok': 1}
            Traceback (most recent call last):
                ... ...
            astrapy.exceptions.DataAPIResponseException: Collection does not exist, collection name: my_collection

        Note:
            Use with caution.

        Note:
            Once the method succeeds, methods on this object can still be invoked:
            however, this hardly makes sense as the underlying actual collection
            is no more.
            It is responsibility of the developer to design a correct flow
            which avoids using a deceased collection any further.
        """

        logger.info(f"dropping collection '{self.name}' (self)")
        drop_result = await self.database.drop_collection(self, max_time_ms=max_time_ms)
        logger.info(f"finished dropping collection '{self.name}' (self)")
        return drop_result  # type: ignore[no-any-return]

    async def command(
        self,
        body: Dict[str, Any],
        *,
        max_time_ms: Optional[int] = None,
    ) -> Dict[str, Any]:
        """
        Send a POST request to the Data API for this collection with
        an arbitrary, caller-provided payload.

        Args:
            body: a JSON-serializable dictionary, the payload of the request.
            max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

        Returns:
            a dictionary with the response of the HTTP request.

        Example:
            >>> asyncio.await(my_async_coll.command({"countDocuments": {}}))
            {'status': {'count': 123}}
        """

        logger.info(f"calling command on '{self.name}'")
        command_result = await self.database.command(
            body=body,
            namespace=self.namespace,
            collection_name=self.name,
            max_time_ms=max_time_ms,
        )
        logger.info(f"finished calling command on '{self.name}'")
        return command_result  # type: ignore[no-any-return]

Instance variables

var database : AsyncDatabase

a Database object, the database this collection belongs to.

Example

>>> my_async_coll.database.name
'quicktest'

Expand source code

@property
def database(self) -> AsyncDatabase:
    """
    a Database object, the database this collection belongs to.

    Example:
        >>> my_async_coll.database.name
        'quicktest'
    """

    return self._database

var full_name : str

The fully-qualified collection name within the database, in the form "namespace.collection_name".

Example

>>> my_async_coll.full_name
'default_keyspace.my_v_collection'

Expand source code

@property
def full_name(self) -> str:
    """
    The fully-qualified collection name within the database,
    in the form "namespace.collection_name".

    Example:
        >>> my_async_coll.full_name
        'default_keyspace.my_v_collection'
    """

    return f"{self.namespace}.{self.name}"

var name : str

The name of this collection.

Example

>>> my_async_coll.name
'my_v_collection'

Expand source code

@property
def name(self) -> str:
    """
    The name of this collection.

    Example:
        >>> my_async_coll.name
        'my_v_collection'
    """

    # type hint added as for some reason the typechecker gets lost
    return self._astra_db_collection.collection_name  # type: ignore[no-any-return, has-type]

var namespace : str

The namespace this collection is in.

Example

>>> my_async_coll.database.namespace
'default_keyspace'

Expand source code

@property
def namespace(self) -> str:
    """
    The namespace this collection is in.

    Example:
        >>> my_async_coll.database.namespace
        'default_keyspace'
    """

    return self.database.namespace

Methods

async def bulk_write(self, requests: Iterable[AsyncBaseOperation], *, ordered: bool = True, concurrency: Optional[int] = None, max_time_ms: Optional[int] = None) ‑> BulkWriteResult

Execute an arbitrary amount of operations such as inserts, updates, deletes either sequentially or concurrently.

This method does not execute atomically, i.e. individual operations are each performed in the same way as the corresponding collection method, and each one is a different and unrelated database mutation.

Args

requests

an iterable over concrete subclasses of BaseOperation, such as AsyncInsertMany or AsyncReplaceOne. Each such object represents an operation ready to be executed on a collection, and is instantiated by passing the same parameters as one would the corresponding collection method.

ordered

whether to launch the requests one after the other or in arbitrary order, possibly in a concurrent fashion. For performance reasons, ordered=False should be preferred when compatible with the needs of the application flow.

concurrency

maximum number of concurrent operations executing at a given time. It cannot be more than one for ordered bulk writes.

max_time_ms

a timeout, in milliseconds, for the whole bulk write. Remember that, if the method call times out, then there's no guarantee about what portion of the bulk write has been received and successfully executed by the Data API.

Returns

A single BulkWriteResult summarizing the whole list of requested operations. The keys in the map attributes of BulkWriteResult (when present) are the integer indices of the corresponding operation in the requests iterable.

Example

>>> from astrapy.operations import AsyncInsertMany, AsyncReplaceOne, AsyncOperation
>>> from astrapy.results import BulkWriteResult
>>>
>>> async def do_bulk_write(
...     acol: AsyncCollection,
...     async_operations: List[AsyncOperation],
... ) -> BulkWriteResult:
...     bw_result = await acol.bulk_write(async_operations)
...     count0 = await acol.count_documents({}, upper_bound=100)
...     print("count0", count0)
...     distinct0 = await acol.distinct("replaced")
...     print("distinct0", distinct0)
...     return bw_result
...
>>> op1 = AsyncInsertMany([{"a": 1}, {"a": 2}])
>>> op2 = AsyncReplaceOne(
...     {"z": 9},
...     replacement={"z": 9, "replaced": True},
...     upsert=True,
... )
>>> result = asyncio.run(do_bulk_write(my_async_coll, [op1, op2]))
count0 3
distinct0 [True]
>>> print("result", result)
result BulkWriteResult(bulk_api_results={0: ..., 1: ...}, deleted_count=0, inserted_count=3, matched_count=0, modified_count=0, upserted_count=1, upserted_ids={1: 'ccd0a800-...'})

Expand source code

async def bulk_write(
    self,
    requests: Iterable[AsyncBaseOperation],
    *,
    ordered: bool = True,
    concurrency: Optional[int] = None,
    max_time_ms: Optional[int] = None,
) -> BulkWriteResult:
    """
    Execute an arbitrary amount of operations such as inserts, updates, deletes
    either sequentially or concurrently.

    This method does not execute atomically, i.e. individual operations are
    each performed in the same way as the corresponding collection method,
    and each one is a different and unrelated database mutation.

    Args:
        requests: an iterable over concrete subclasses of `BaseOperation`,
            such as `AsyncInsertMany` or `AsyncReplaceOne`. Each such object
            represents an operation ready to be executed on a collection,
            and is instantiated by passing the same parameters as one
            would the corresponding collection method.
        ordered: whether to launch the `requests` one after the other or
            in arbitrary order, possibly in a concurrent fashion. For
            performance reasons, `ordered=False` should be preferred
            when compatible with the needs of the application flow.
        concurrency: maximum number of concurrent operations executing at
            a given time. It cannot be more than one for ordered bulk writes.
        max_time_ms: a timeout, in milliseconds, for the whole bulk write.
            Remember that, if the method call times out, then there's no
            guarantee about what portion of the bulk write has been received
            and successfully executed by the Data API.

    Returns:
        A single BulkWriteResult summarizing the whole list of requested
        operations. The keys in the map attributes of BulkWriteResult
        (when present) are the integer indices of the corresponding operation
        in the `requests` iterable.

    Example:
        >>> from astrapy.operations import AsyncInsertMany, AsyncReplaceOne, AsyncOperation
        >>> from astrapy.results import BulkWriteResult
        >>>
        >>> async def do_bulk_write(
        ...     acol: AsyncCollection,
        ...     async_operations: List[AsyncOperation],
        ... ) -> BulkWriteResult:
        ...     bw_result = await acol.bulk_write(async_operations)
        ...     count0 = await acol.count_documents({}, upper_bound=100)
        ...     print("count0", count0)
        ...     distinct0 = await acol.distinct("replaced")
        ...     print("distinct0", distinct0)
        ...     return bw_result
        ...
        >>> op1 = AsyncInsertMany([{"a": 1}, {"a": 2}])
        >>> op2 = AsyncReplaceOne(
        ...     {"z": 9},
        ...     replacement={"z": 9, "replaced": True},
        ...     upsert=True,
        ... )
        >>> result = asyncio.run(do_bulk_write(my_async_coll, [op1, op2]))
        count0 3
        distinct0 [True]
        >>> print("result", result)
        result BulkWriteResult(bulk_api_results={0: ..., 1: ...}, deleted_count=0, inserted_count=3, matched_count=0, modified_count=0, upserted_count=1, upserted_ids={1: 'ccd0a800-...'})
    """

    # lazy importing here against circular-import error
    from astrapy.operations import reduce_bulk_write_results

    if concurrency is None:
        if ordered:
            _concurrency = 1
        else:
            _concurrency = DEFAULT_BULK_WRITE_CONCURRENCY
    else:
        _concurrency = concurrency
    if _concurrency > 1 and ordered:
        raise ValueError("Cannot run ordered bulk_write concurrently.")
    timeout_manager = MultiCallTimeoutManager(overall_max_time_ms=max_time_ms)
    logger.info(f"startng a bulk write on '{self.name}'")
    if ordered:
        bulk_write_results: List[BulkWriteResult] = []
        for operation_i, operation in enumerate(requests):
            try:
                this_bw_result = await operation.execute(
                    self,
                    index_in_bulk_write=operation_i,
                    bulk_write_timeout_ms=timeout_manager.remaining_timeout_ms(),
                )
                bulk_write_results.append(this_bw_result)
            except CumulativeOperationException as exc:
                partial_result = exc.partial_result
                partial_bw_result = reduce_bulk_write_results(
                    bulk_write_results
                    + [
                        partial_result.to_bulk_write_result(
                            index_in_bulk_write=operation_i
                        )
                    ]
                )
                dar_exception = exc.data_api_response_exception()
                raise BulkWriteException(
                    text=dar_exception.text,
                    error_descriptors=dar_exception.error_descriptors,
                    detailed_error_descriptors=dar_exception.detailed_error_descriptors,
                    partial_result=partial_bw_result,
                    exceptions=[dar_exception],
                )
            except DataAPIResponseException as exc:
                # the cumulative exceptions, with their
                # partially-done-info, are handled above:
                # here it's just one-shot d.a.r. exceptions
                partial_bw_result = reduce_bulk_write_results(bulk_write_results)
                dar_exception = exc.data_api_response_exception()
                raise BulkWriteException(
                    text=dar_exception.text,
                    error_descriptors=dar_exception.error_descriptors,
                    detailed_error_descriptors=dar_exception.detailed_error_descriptors,
                    partial_result=partial_bw_result,
                    exceptions=[dar_exception],
                )
        full_bw_result = reduce_bulk_write_results(bulk_write_results)
        logger.info(f"finished a bulk write on '{self.name}'")
        return full_bw_result
    else:

        sem = asyncio.Semaphore(_concurrency)

        async def _concurrent_execute_as_either(
            operation: AsyncBaseOperation, operation_i: int
        ) -> Tuple[Optional[BulkWriteResult], Optional[DataAPIResponseException]]:
            async with sem:
                try:
                    ex_result = await operation.execute(
                        self,
                        index_in_bulk_write=operation_i,
                        bulk_write_timeout_ms=timeout_manager.remaining_timeout_ms(),
                    )
                    return (ex_result, None)
                except DataAPIResponseException as exc:
                    return (None, exc)

        tasks = [
            asyncio.create_task(
                _concurrent_execute_as_either(operation, operation_i)
            )
            for operation_i, operation in enumerate(requests)
        ]
        bulk_write_either_results = await asyncio.gather(*tasks)
        # regroup
        bulk_write_successes = [bwr for bwr, _ in bulk_write_either_results if bwr]
        bulk_write_failures = [bwf for _, bwf in bulk_write_either_results if bwf]
        if bulk_write_failures:
            # extract and cumulate
            partial_results_from_failures = [
                failure.partial_result.to_bulk_write_result(
                    index_in_bulk_write=operation_i
                )
                for failure in bulk_write_failures
                if isinstance(failure, CumulativeOperationException)
            ]
            partial_bw_result = reduce_bulk_write_results(
                bulk_write_successes + partial_results_from_failures
            )
            # raise and recast the first exception
            all_dar_exceptions = [
                bw_failure.data_api_response_exception()
                for bw_failure in bulk_write_failures
            ]
            dar_exception = all_dar_exceptions[0]
            raise BulkWriteException(
                text=dar_exception.text,
                error_descriptors=dar_exception.error_descriptors,
                detailed_error_descriptors=dar_exception.detailed_error_descriptors,
                partial_result=partial_bw_result,
                exceptions=all_dar_exceptions,
            )
        else:
            logger.info(f"finished a bulk write on '{self.name}'")
            return reduce_bulk_write_results(bulk_write_successes)

async def command(self, body: Dict[str, Any], *, max_time_ms: Optional[int] = None) ‑> Dict[str, Any]

Send a POST request to the Data API for this collection with an arbitrary, caller-provided payload.

Args

body

a JSON-serializable dictionary, the payload of the request.

max_time_ms

a timeout, in milliseconds, for the underlying HTTP request.

Returns

a dictionary with the response of the HTTP request.

Example

>>> asyncio.await(my_async_coll.command({"countDocuments": {}}))
{'status': {'count': 123}}

Expand source code

async def command(
    self,
    body: Dict[str, Any],
    *,
    max_time_ms: Optional[int] = None,
) -> Dict[str, Any]:
    """
    Send a POST request to the Data API for this collection with
    an arbitrary, caller-provided payload.

    Args:
        body: a JSON-serializable dictionary, the payload of the request.
        max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

    Returns:
        a dictionary with the response of the HTTP request.

    Example:
        >>> asyncio.await(my_async_coll.command({"countDocuments": {}}))
        {'status': {'count': 123}}
    """

    logger.info(f"calling command on '{self.name}'")
    command_result = await self.database.command(
        body=body,
        namespace=self.namespace,
        collection_name=self.name,
        max_time_ms=max_time_ms,
    )
    logger.info(f"finished calling command on '{self.name}'")
    return command_result  # type: ignore[no-any-return]

async def count_documents(self, filter: Dict[str, Any], *, upper_bound: int, max_time_ms: Optional[int] = None) ‑> int

Count the documents in the collection matching the specified filter.

Args

filter

a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$le": 100}} {"$and": [{"name": "John"}, {"price": {"$le": 100}}]} See the Data API documentation for the full set of operators.

upper_bound

a required ceiling on the result of the count operation. If the actual number of documents exceeds this value, an exception will be raised. Furthermore, if the actual number of documents exceeds the maximum count that the Data API can reach (regardless of upper_bound), an exception will be raised.

max_time_ms

a timeout, in milliseconds, for the underlying HTTP request.

Returns

the exact count of matching documents.

Example

>>> async def do_count_docs(acol: AsyncCollection) -> None:
...     await acol.insert_many([{"seq": i} for i in range(20)])
...     count0 = await acol.count_documents({}, upper_bound=100)
...     print("count0", count0)
...     count1 = await acol.count_documents({"seq":{"$gt": 15}}, upper_bound=100)
...     print("count1", count1)
...     count2 = await acol.count_documents({}, upper_bound=10)
...     print("count2", count2)
...
>>> asyncio.run(do_count_docs(my_async_coll))
count0 20
count1 4
Traceback (most recent call last):
    ... ...
astrapy.exceptions.TooManyDocumentsToCountException

Note

Count operations are expensive: for this reason, the best practice is to provide a reasonable upper_bound according to the caller expectations. Moreover, indiscriminate usage of count operations for sizeable amounts of documents (i.e. in the thousands and more) is discouraged in favor of alternative application-specific solutions. Keep in mind that the Data API has a hard upper limit on the amount of documents it will count, and that an exception will be thrown by this method if this limit is encountered.

Expand source code

@recast_method_async
async def count_documents(
    self,
    filter: Dict[str, Any],
    *,
    upper_bound: int,
    max_time_ms: Optional[int] = None,
) -> int:
    """
    Count the documents in the collection matching the specified filter.

    Args:
        filter: a predicate expressed as a dictionary according to the
            Data API filter syntax. Examples are:
                {}
                {"name": "John"}
                {"price": {"$le": 100}}
                {"$and": [{"name": "John"}, {"price": {"$le": 100}}]}
            See the Data API documentation for the full set of operators.
        upper_bound: a required ceiling on the result of the count operation.
            If the actual number of documents exceeds this value,
            an exception will be raised.
            Furthermore, if the actual number of documents exceeds the maximum
            count that the Data API can reach (regardless of upper_bound),
            an exception will be raised.
        max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

    Returns:
        the exact count of matching documents.

    Example:
        >>> async def do_count_docs(acol: AsyncCollection) -> None:
        ...     await acol.insert_many([{"seq": i} for i in range(20)])
        ...     count0 = await acol.count_documents({}, upper_bound=100)
        ...     print("count0", count0)
        ...     count1 = await acol.count_documents({"seq":{"$gt": 15}}, upper_bound=100)
        ...     print("count1", count1)
        ...     count2 = await acol.count_documents({}, upper_bound=10)
        ...     print("count2", count2)
        ...
        >>> asyncio.run(do_count_docs(my_async_coll))
        count0 20
        count1 4
        Traceback (most recent call last):
            ... ...
        astrapy.exceptions.TooManyDocumentsToCountException

    Note:
        Count operations are expensive: for this reason, the best practice
        is to provide a reasonable `upper_bound` according to the caller
        expectations. Moreover, indiscriminate usage of count operations
        for sizeable amounts of documents (i.e. in the thousands and more)
        is discouraged in favor of alternative application-specific solutions.
        Keep in mind that the Data API has a hard upper limit on the amount
        of documents it will count, and that an exception will be thrown
        by this method if this limit is encountered.
    """

    logger.info("calling count_documents")
    cd_response = await self._astra_db_collection.count_documents(
        filter=filter,
        timeout_info=base_timeout_info(max_time_ms),
    )
    logger.info("finished calling count_documents")
    if "count" in cd_response.get("status", {}):
        count: int = cd_response["status"]["count"]
        if cd_response["status"].get("moreData", False):
            raise TooManyDocumentsToCountException(
                text=f"Document count exceeds {count}, the maximum allowed by the server",
                server_max_count_exceeded=True,
            )
        else:
            if count > upper_bound:
                raise TooManyDocumentsToCountException(
                    text="Document count exceeds required upper bound",
                    server_max_count_exceeded=False,
                )
            else:
                return count
    else:
        raise DataAPIFaultyResponseException(
            text="Faulty response from count_documents API command.",
            raw_response=cd_response,
        )

async def delete_all(self, *, max_time_ms: Optional[int] = None) ‑> Dict[str, Any]

Delete all documents in a collection.

Args

max_time_ms

a timeout, in milliseconds, for the underlying HTTP request.

Returns

a dictionary of the form {"ok": 1} to signal successful deletion.

Example

>>> async def do_delete_all(acol: AsyncCollection) -> None:
...     distinct0 = await acol.distinct("seq")
...     print("distinct0", distinct0)
...     count1 = await acol.count_documents({}, upper_bound=100)
...     print("count1", count1)
...     delete_result2 = await acol.delete_all()
...     print("delete_result2", delete_result2)
...     count3 = await acol.count_documents({}, upper_bound=100)
...     print("count3", count3)
...
>>> asyncio.run(do_delete_all(my_async_coll))
distinct0 [4, 2, 3, 0, 1]
count1 5
delete_result2 {'ok': 1}
count3 0

Note

Use with caution.

Note

Once the method succeeds, methods on this object can still be invoked: however, this hardly makes sense as the underlying actual collection is no more. It is responsibility of the developer to design a correct flow which avoids using a deceased collection any further.

Expand source code

@recast_method_async
async def delete_all(self, *, max_time_ms: Optional[int] = None) -> Dict[str, Any]:
    """
    Delete all documents in a collection.

    Args:
        max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

    Returns:
        a dictionary of the form {"ok": 1} to signal successful deletion.

    Example:
        >>> async def do_delete_all(acol: AsyncCollection) -> None:
        ...     distinct0 = await acol.distinct("seq")
        ...     print("distinct0", distinct0)
        ...     count1 = await acol.count_documents({}, upper_bound=100)
        ...     print("count1", count1)
        ...     delete_result2 = await acol.delete_all()
        ...     print("delete_result2", delete_result2)
        ...     count3 = await acol.count_documents({}, upper_bound=100)
        ...     print("count3", count3)
        ...
        >>> asyncio.run(do_delete_all(my_async_coll))
        distinct0 [4, 2, 3, 0, 1]
        count1 5
        delete_result2 {'ok': 1}
        count3 0

    Note:
        Use with caution.

    Note:
        Once the method succeeds, methods on this object can still be invoked:
        however, this hardly makes sense as the underlying actual collection
        is no more.
        It is responsibility of the developer to design a correct flow
        which avoids using a deceased collection any further.
    """

    logger.info(f"calling unfiltered delete_many on '{self.name}'")
    dm_response = await self._astra_db_collection.delete_many(
        filter={}, timeout_info=base_timeout_info(max_time_ms)
    )
    logger.info(f"finished calling unfiltered delete_many on '{self.name}'")
    deleted_count = dm_response["status"]["deletedCount"]
    if deleted_count == -1:
        return {"ok": 1}
    else:
        raise DataAPIFaultyResponseException(
            text="Faulty response from delete_many API command.",
            raw_response=dm_response,
        )

async def delete_many(self, filter: Dict[str, Any], *, max_time_ms: Optional[int] = None) ‑> DeleteResult

Delete all documents matching a provided filter.

Args

filter

a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$le": 100}} {"$and": [{"name": "John"}, {"price": {"$le": 100}}]} See the Data API documentation for the full set of operators. The delete_many method does not accept an empty filter: see delete_all to completely erase all contents of a collection

max_time_ms

a timeout, in milliseconds, for the operation.

Returns

a DeleteResult object summarizing the outcome of the delete operation.

Example

>>> async def do_delete_many(acol: AsyncCollection) -> None:
...     await acol.insert_many([{"seq": 1}, {"seq": 0}, {"seq": 2}])
...     delete_result0 = await acol.delete_many({"seq": {"$lte": 1}})
...     print("delete_result0.deleted_count", delete_result0.deleted_count)
...     distinct1 = await acol.distinct("seq")
...     print("distinct1", distinct1)
...     delete_result2 = await acol.delete_many({"seq": {"$lte": 1}})
...     print("delete_result2.deleted_count", delete_result2.deleted_count)
...
>>> asyncio.run(do_delete_many(my_async_coll))
delete_result0.deleted_count 2
distinct1 [2]
delete_result2.deleted_count 0

Note

This operation is not atomic. Depending on the amount of matching documents, it can keep running (in a blocking way) for a macroscopic time. In that case, new documents that are meanwhile inserted (e.g. from another process/application) will be deleted during the execution of this method call until the collection is devoid of matches.

Expand source code

@recast_method_async
async def delete_many(
    self,
    filter: Dict[str, Any],
    *,
    max_time_ms: Optional[int] = None,
) -> DeleteResult:
    """
    Delete all documents matching a provided filter.

    Args:
        filter: a predicate expressed as a dictionary according to the
            Data API filter syntax. Examples are:
                {}
                {"name": "John"}
                {"price": {"$le": 100}}
                {"$and": [{"name": "John"}, {"price": {"$le": 100}}]}
            See the Data API documentation for the full set of operators.
            The `delete_many` method does not accept an empty filter: see
            `delete_all` to completely erase all contents of a collection
        max_time_ms: a timeout, in milliseconds, for the operation.

    Returns:
        a DeleteResult object summarizing the outcome of the delete operation.

    Example:
        >>> async def do_delete_many(acol: AsyncCollection) -> None:
        ...     await acol.insert_many([{"seq": 1}, {"seq": 0}, {"seq": 2}])
        ...     delete_result0 = await acol.delete_many({"seq": {"$lte": 1}})
        ...     print("delete_result0.deleted_count", delete_result0.deleted_count)
        ...     distinct1 = await acol.distinct("seq")
        ...     print("distinct1", distinct1)
        ...     delete_result2 = await acol.delete_many({"seq": {"$lte": 1}})
        ...     print("delete_result2.deleted_count", delete_result2.deleted_count)
        ...
        >>> asyncio.run(do_delete_many(my_async_coll))
        delete_result0.deleted_count 2
        distinct1 [2]
        delete_result2.deleted_count 0

    Note:
        This operation is not atomic. Depending on the amount of matching
        documents, it can keep running (in a blocking way) for a macroscopic
        time. In that case, new documents that are meanwhile inserted
        (e.g. from another process/application) will be deleted during
        the execution of this method call until the collection is devoid
        of matches.
    """

    if not filter:
        raise ValueError(
            "The `filter` parameter to method `delete_many` cannot be "
            "empty. In order to completely clear the contents of a "
            "collection, please use the `delete_all` method."
        )

    dm_responses: List[Dict[str, Any]] = []
    deleted_count = 0
    must_proceed = True
    timeout_manager = MultiCallTimeoutManager(overall_max_time_ms=max_time_ms)
    logger.info(f"starting delete_many on '{self.name}'")
    while must_proceed:
        logger.info(f"calling delete_many on '{self.name}'")
        this_dm_response = await self._astra_db_collection.delete_many(
            filter=filter,
            skip_error_check=True,
            timeout_info=timeout_manager.remaining_timeout_info(),
        )
        logger.info(f"finished calling delete_many on '{self.name}'")
        # if errors, quit early
        if this_dm_response.get("errors", []):
            partial_result = DeleteResult(
                deleted_count=deleted_count,
                raw_results=dm_responses,
            )
            all_dm_responses = dm_responses + [this_dm_response]
            raise DeleteManyException.from_responses(
                commands=[None for _ in all_dm_responses],
                raw_responses=all_dm_responses,
                partial_result=partial_result,
            )
        else:
            this_dc = this_dm_response.get("status", {}).get("deletedCount")
            if this_dc is None or this_dc < 0:
                raise DataAPIFaultyResponseException(
                    text="Faulty response from delete_many API command.",
                    raw_response=this_dm_response,
                )
            dm_responses.append(this_dm_response)
            deleted_count += this_dc
            must_proceed = this_dm_response.get("status", {}).get("moreData", False)

    logger.info(f"finished delete_many on '{self.name}'")
    return DeleteResult(
        deleted_count=deleted_count,
        raw_results=dm_responses,
    )

async def delete_one(self, filter: Dict[str, Any], *, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, sort: Optional[SortType] = None, max_time_ms: Optional[int] = None) ‑> DeleteResult

Delete one document matching a provided filter. This method never deletes more than a single document, regardless of the number of matches to the provided filters.

Args

filter

a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$le": 100}} {"$and": [{"name": "John"}, {"price": {"$le": 100}}]} See the Data API documentation for the full set of operators.

vector

a suitable vector, i.e. a list of float numbers of the appropriate dimensionality, to use vector search (i.e. ANN, or "approximate nearest-neighbours" search), as the sorting criterion. In this way, the matched document (if any) will be the one that is most similar to the provided vector. This parameter cannot be used together with sort. See the find method for more details on this parameter.

vectorize

a string to be made into a vector to perform vector search. This can be supplied in (exclusive) alternative to vector, provided such a service is configured for the collection, and achieves the same effect. NOTE: This feature is under current development.

sort

with this dictionary parameter one can control the sorting order of the documents matching the filter, effectively determining what document will come first and hence be the deleted one. See the find method for more on sorting.

max_time_ms

a timeout, in milliseconds, for the underlying HTTP request.

Returns

a DeleteResult object summarizing the outcome of the delete operation.

Example

>>> my_coll.insert_many([{"seq": 1}, {"seq": 0}, {"seq": 2}])
InsertManyResult(...)
>>> my_coll.delete_one({"seq": 1})
DeleteResult(raw_results=..., deleted_count=1)
>>> my_coll.distinct("seq")
[0, 2]
>>> my_coll.delete_one(
...     {"seq": {"$exists": True}},
...     sort={"seq": astrapy.constants.SortDocuments.DESCENDING},
... )
DeleteResult(raw_results=..., deleted_count=1)
>>> my_coll.distinct("seq")
[0]
>>> my_coll.delete_one({"seq": 2})
DeleteResult(raw_results=..., deleted_count=0)

Expand source code

@recast_method_async
async def delete_one(
    self,
    filter: Dict[str, Any],
    *,
    vector: Optional[VectorType] = None,
    vectorize: Optional[str] = None,
    sort: Optional[SortType] = None,
    max_time_ms: Optional[int] = None,
) -> DeleteResult:
    """
    Delete one document matching a provided filter.
    This method never deletes more than a single document, regardless
    of the number of matches to the provided filters.

    Args:
        filter: a predicate expressed as a dictionary according to the
            Data API filter syntax. Examples are:
                {}
                {"name": "John"}
                {"price": {"$le": 100}}
                {"$and": [{"name": "John"}, {"price": {"$le": 100}}]}
            See the Data API documentation for the full set of operators.
        vector: a suitable vector, i.e. a list of float numbers of the appropriate
            dimensionality, to use vector search (i.e. ANN,
            or "approximate nearest-neighbours" search), as the sorting criterion.
            In this way, the matched document (if any) will be the one
            that is most similar to the provided vector.
            This parameter cannot be used together with `sort`.
            See the `find` method for more details on this parameter.
        vectorize: a string to be made into a vector to perform vector search.
            This can be supplied in (exclusive) alternative to `vector`,
            provided such a service is configured for the collection,
            and achieves the same effect.
            NOTE: This feature is under current development.
        sort: with this dictionary parameter one can control the sorting
            order of the documents matching the filter, effectively
            determining what document will come first and hence be the
            deleted one. See the `find` method for more on sorting.
        max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

    Returns:
        a DeleteResult object summarizing the outcome of the delete operation.

    Example:
        >>> my_coll.insert_many([{"seq": 1}, {"seq": 0}, {"seq": 2}])
        InsertManyResult(...)
        >>> my_coll.delete_one({"seq": 1})
        DeleteResult(raw_results=..., deleted_count=1)
        >>> my_coll.distinct("seq")
        [0, 2]
        >>> my_coll.delete_one(
        ...     {"seq": {"$exists": True}},
        ...     sort={"seq": astrapy.constants.SortDocuments.DESCENDING},
        ... )
        DeleteResult(raw_results=..., deleted_count=1)
        >>> my_coll.distinct("seq")
        [0]
        >>> my_coll.delete_one({"seq": 2})
        DeleteResult(raw_results=..., deleted_count=0)
    """

    _sort = _collate_vector_to_sort(sort, vector, vectorize)
    logger.info(f"calling delete_one_by_predicate on '{self.name}'")
    do_response = await self._astra_db_collection.delete_one_by_predicate(
        filter=filter,
        timeout_info=base_timeout_info(max_time_ms),
        sort=_sort,
    )
    logger.info(f"finished calling delete_one_by_predicate on '{self.name}'")
    if "deletedCount" in do_response.get("status", {}):
        deleted_count = do_response["status"]["deletedCount"]
        if deleted_count == -1:
            return DeleteResult(
                deleted_count=None,
                raw_results=[do_response],
            )
        else:
            # expected a non-negative integer:
            return DeleteResult(
                deleted_count=deleted_count,
                raw_results=[do_response],
            )
    else:
        raise DataAPIFaultyResponseException(
            text="Faulty response from delete_one API command.",
            raw_response=do_response,
        )

async def distinct(self, key: str, *, filter: Optional[FilterType] = None, max_time_ms: Optional[int] = None) ‑> List[Any]

Return a list of the unique values of key across the documents in the collection that match the provided filter.

Args

key

the name of the field whose value is inspected across documents. Keys can use dot-notation to descend to deeper document levels. Example of acceptable key values: "field" "field.subfield" "field.3" "field.3.subfield" If lists are encountered and no numeric index is specified, all items in the list are visited.

filter

a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$le": 100}} {"$and": [{"name": "John"}, {"price": {"$le": 100}}]} See the Data API documentation for the full set of operators.

max_time_ms

a timeout, in milliseconds, for the operation.

Returns

a list of all different values for key found across the documents that match the filter. The result list has no repeated items.

Example

>>> async def run_distinct(acol: AsyncCollection) -> None:
...     await acol.insert_many(
...         [
...             {"name": "Marco", "food": ["apple", "orange"], "city": "Helsinki"},
...             {"name": "Emma", "food": {"likes_fruit": True, "allergies": []}},
...         ]
...     )
...     distinct0 = await acol.distinct("name")
...     print("distinct('name')", distinct0)
...     distinct1 = await acol.distinct("city")
...     print("distinct('city')", distinct1)
...     distinct2 = await acol.distinct("food")
...     print("distinct('food')", distinct2)
...     distinct3 = await acol.distinct("food.1")
...     print("distinct('food.1')", distinct3)
...     distinct4 = await acol.distinct("food.allergies")
...     print("distinct('food.allergies')", distinct4)
...     distinct5 = await acol.distinct("food.likes_fruit")
...     print("distinct('food.likes_fruit')", distinct5)
...
>>> asyncio.run(run_distinct(my_async_coll))
distinct('name') ['Emma', 'Marco']
distinct('city') ['Helsinki']
distinct('food') [{'likes_fruit': True, 'allergies': []}, 'apple', 'orange']
distinct('food.1') ['orange']
distinct('food.allergies') []
distinct('food.likes_fruit') [True]

Note

It must be kept in mind that distinct is a client-side operation, which effectively browses all required documents using the logic of the find method and collects the unique values found for key. As such, there may be performance, latency and ultimately billing implications if the amount of matching documents is large.

Note

For details on the behaviour of "distinct" in conjunction with real-time changes in the collection contents, see the Note of the find command.

Expand source code

async def distinct(
    self,
    key: str,
    *,
    filter: Optional[FilterType] = None,
    max_time_ms: Optional[int] = None,
) -> List[Any]:
    """
    Return a list of the unique values of `key` across the documents
    in the collection that match the provided filter.

    Args:
        key: the name of the field whose value is inspected across documents.
            Keys can use dot-notation to descend to deeper document levels.
            Example of acceptable `key` values:
                "field"
                "field.subfield"
                "field.3"
                "field.3.subfield"
            If lists are encountered and no numeric index is specified,
            all items in the list are visited.
        filter: a predicate expressed as a dictionary according to the
            Data API filter syntax. Examples are:
                {}
                {"name": "John"}
                {"price": {"$le": 100}}
                {"$and": [{"name": "John"}, {"price": {"$le": 100}}]}
            See the Data API documentation for the full set of operators.
        max_time_ms: a timeout, in milliseconds, for the operation.

    Returns:
        a list of all different values for `key` found across the documents
        that match the filter. The result list has no repeated items.

    Example:
        >>> async def run_distinct(acol: AsyncCollection) -> None:
        ...     await acol.insert_many(
        ...         [
        ...             {"name": "Marco", "food": ["apple", "orange"], "city": "Helsinki"},
        ...             {"name": "Emma", "food": {"likes_fruit": True, "allergies": []}},
        ...         ]
        ...     )
        ...     distinct0 = await acol.distinct("name")
        ...     print("distinct('name')", distinct0)
        ...     distinct1 = await acol.distinct("city")
        ...     print("distinct('city')", distinct1)
        ...     distinct2 = await acol.distinct("food")
        ...     print("distinct('food')", distinct2)
        ...     distinct3 = await acol.distinct("food.1")
        ...     print("distinct('food.1')", distinct3)
        ...     distinct4 = await acol.distinct("food.allergies")
        ...     print("distinct('food.allergies')", distinct4)
        ...     distinct5 = await acol.distinct("food.likes_fruit")
        ...     print("distinct('food.likes_fruit')", distinct5)
        ...
        >>> asyncio.run(run_distinct(my_async_coll))
        distinct('name') ['Emma', 'Marco']
        distinct('city') ['Helsinki']
        distinct('food') [{'likes_fruit': True, 'allergies': []}, 'apple', 'orange']
        distinct('food.1') ['orange']
        distinct('food.allergies') []
        distinct('food.likes_fruit') [True]

    Note:
        It must be kept in mind that `distinct` is a client-side operation,
        which effectively browses all required documents using the logic
        of the `find` method and collects the unique values found for `key`.
        As such, there may be performance, latency and ultimately
        billing implications if the amount of matching documents is large.

    Note:
        For details on the behaviour of "distinct" in conjunction with
        real-time changes in the collection contents, see the
        Note of the `find` command.
    """

    f_cursor = AsyncCursor(
        collection=self,
        filter=filter,
        projection={key: True},
        max_time_ms=None,
        overall_max_time_ms=max_time_ms,
    )
    return await f_cursor.distinct(key)  # type: ignore[no-any-return]

async def drop(self, *, max_time_ms: Optional[int] = None) ‑> Dict[str, Any]

Drop the collection, i.e. delete it from the database along with all the documents it contains.

Args

max_time_ms

a timeout, in milliseconds, for the underlying HTTP request. Remember there is not guarantee that a request that has timed out us not in fact honored.

Returns

a dictionary of the form {"ok": 1} to signal successful deletion.

Example

>>> async def drop_and_check(acol: AsyncCollection) -> None:
...     doc0 = await acol.find_one({})
...     print("doc0", doc0)
...     drop_result = await acol.drop()
...     print("drop_result", drop_result)
...     doc1 = await acol.find_one({})
...
>>> asyncio.run(drop_and_check(my_async_coll))
doc0 {'_id': '...', 'z': -10}
drop_result {'ok': 1}
Traceback (most recent call last):
    ... ...
astrapy.exceptions.DataAPIResponseException: Collection does not exist, collection name: my_collection

Note

Use with caution.

Note

Once the method succeeds, methods on this object can still be invoked: however, this hardly makes sense as the underlying actual collection is no more. It is responsibility of the developer to design a correct flow which avoids using a deceased collection any further.

Expand source code

async def drop(self, *, max_time_ms: Optional[int] = None) -> Dict[str, Any]:
    """
    Drop the collection, i.e. delete it from the database along with
    all the documents it contains.

    Args:
        max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.
            Remember there is not guarantee that a request that has
            timed out us not in fact honored.

    Returns:
        a dictionary of the form {"ok": 1} to signal successful deletion.

    Example:
        >>> async def drop_and_check(acol: AsyncCollection) -> None:
        ...     doc0 = await acol.find_one({})
        ...     print("doc0", doc0)
        ...     drop_result = await acol.drop()
        ...     print("drop_result", drop_result)
        ...     doc1 = await acol.find_one({})
        ...
        >>> asyncio.run(drop_and_check(my_async_coll))
        doc0 {'_id': '...', 'z': -10}
        drop_result {'ok': 1}
        Traceback (most recent call last):
            ... ...
        astrapy.exceptions.DataAPIResponseException: Collection does not exist, collection name: my_collection

    Note:
        Use with caution.

    Note:
        Once the method succeeds, methods on this object can still be invoked:
        however, this hardly makes sense as the underlying actual collection
        is no more.
        It is responsibility of the developer to design a correct flow
        which avoids using a deceased collection any further.
    """

    logger.info(f"dropping collection '{self.name}' (self)")
    drop_result = await self.database.drop_collection(self, max_time_ms=max_time_ms)
    logger.info(f"finished dropping collection '{self.name}' (self)")
    return drop_result  # type: ignore[no-any-return]

def find(self, filter: Optional[FilterType] = None, *, projection: Optional[ProjectionType] = None, skip: Optional[int] = None, limit: Optional[int] = None, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, include_similarity: Optional[bool] = None, sort: Optional[SortType] = None, max_time_ms: Optional[int] = None) ‑> AsyncCursor

Find documents on the collection, matching a certain provided filter.

The method returns a Cursor that can then be iterated over. Depending on the method call pattern, the iteration over all documents can reflect collection mutations occurred since the find method was called, or not. In cases where the cursor reflects mutations in real-time, it will iterate over cursors in an approximate way (i.e. exhibiting occasional skipped or duplicate documents). This happens when making use of the sort option in a non-vector-search manner.

Args

filter

a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$le": 100}} {"$and": [{"name": "John"}, {"price": {"$le": 100}}]} See the Data API documentation for the full set of operators.

projection

used to select a subset of fields in the documents being returned. The projection can be: an iterable over the field names to return; a dictionary {field_name: True} to positively select certain fields; or a dictionary {field_name: False} if one wants to discard some fields from the response. The default is to return the whole documents.

skip

with this integer parameter, what would be the first skip documents returned by the query are discarded, and the results start from the (skip+1)-th document. This parameter can be used only in conjunction with an explicit sort criterion of the ascending/descending type (i.e. it cannot be used when not sorting, nor with vector-based ANN search).

limit

this (integer) parameter sets a limit over how many documents are returned. Once limit is reached (or the cursor is exhausted for lack of matching documents), nothing more is returned.

vector

a suitable vector, i.e. a list of float numbers of the appropriate dimensionality, to perform vector search (i.e. ANN, or "approximate nearest-neighbours" search). When running similarity search on a collection, no other sorting criteria can be specified. Moreover, there is an upper bound to the number of documents that can be returned. For details, see the Note about upper bounds and the Data API documentation.

vectorize

a string to be made into a vector to perform vector search. This can be supplied in (exclusive) alternative to vector, provided such a service is configured for the collection, and achieves the same effect. NOTE: This feature is under current development.

include_similarity

a boolean to request the numeric value of the similarity to be returned as an added "$similarity" key in each returned document. Can only be used for vector ANN search, i.e. when either vector is supplied or the sort parameter has the shape {"$vector": …}.

sort

with this dictionary parameter one can control the order the documents are returned. See the Note about sorting, as well as the one about upper bounds, for details.

max_time_ms

a timeout, in milliseconds, for each single one of the underlying HTTP requests used to fetch documents as the cursor is iterated over.

Returns

an AsyncCursor object representing iterations over the matching documents

(see the AsyncCursor object for how to use it. The simplest thing is to

run a for loop

for document in collection.sort(...):).

Examples

>>> async def run_finds(acol: AsyncCollection) -> None:
...             filter = {"seq": {"$exists": True}}
...             print("find results 1:")
...             async for doc in acol.find(filter, projection={"seq": True}, limit=5):
...                 print(doc["seq"])
...             async_cursor1 = acol.find(
...                 {},
...                 limit=4,
...                 sort={"seq": astrapy.constants.SortDocuments.DESCENDING},
...             )
...             ids = [doc["_id"] async for doc in async_cursor1]
...             print("find results 2:", ids)
...             async_cursor2 = acol.find({}, limit=3)
...             seqs = await async_cursor2.distinct("seq")
...             print("distinct results 3:", seqs)
...
>>> asyncio.run(run_finds(my_async_coll))
find results 1:
48
35
7
11
13
find results 2: ['d656cd9d-...', '479c7ce8-...', '96dc87fd-...', '83f0a21f-...']
distinct results 3: [48, 35, 7]

>>> async def run_vector_finds(acol: AsyncCollection) -> None:
...     await acol.insert_many([
...         {"tag": "A", "$vector": [4, 5]},
...         {"tag": "B", "$vector": [3, 4]},
...         {"tag": "C", "$vector": [3, 2]},
...         {"tag": "D", "$vector": [4, 1]},
...         {"tag": "E", "$vector": [2, 5]},
...     ])
...     ann_tags = [
...         document["tag"]
...         async for document in acol.find(
...             {},
...             limit=3,
...             vector=[3, 3],
...         )
...     ]
...     return ann_tags
...
>>> asyncio.run(run_vector_finds(my_async_coll))
['A', 'B', 'C']
# (assuming the collection has metric VectorMetric.COSINE)

Note

The following are example values for the sort parameter. When no particular order is required: sort={} When sorting by a certain value in ascending/descending order: sort={"field": SortDocuments.ASCENDING} sort={"field": SortDocuments.DESCENDING} When sorting first by "field" and then by "subfield" (while modern Python versions preserve the order of dictionaries, it is suggested for clarity to employ a collections.OrderedDict in these cases): sort={ "field": SortDocuments.ASCENDING, "subfield": SortDocuments.ASCENDING, } When running a vector similarity (ANN) search: sort={"$vector": [0.4, 0.15, -0.5]}

Note

Some combinations of arguments impose an implicit upper bound on the number of documents that are returned by the Data API. More specifically: (a) Vector ANN searches cannot return more than a number of documents that at the time of writing is set to 1000 items. (b) When using a sort criterion of the ascending/descending type, the Data API will return a smaller number of documents, set to 20 at the time of writing, and stop there. The returned documents are the top results across the whole collection according to the requested criterion. These provisions should be kept in mind even when subsequently running a command such as .distinct() on a cursor.

Note

When not specifying sorting criteria at all (by vector or otherwise), the cursor can scroll through an arbitrary number of documents as the Data API and the client periodically exchange new chunks of documents. It should be noted that the behavior of the cursor in the case documents have been added/removed after the find was started depends on database internals and it is not guaranteed, nor excluded, that such "real-time" changes in the data would be picked up by the cursor.

Expand source code

def find(
    self,
    filter: Optional[FilterType] = None,
    *,
    projection: Optional[ProjectionType] = None,
    skip: Optional[int] = None,
    limit: Optional[int] = None,
    vector: Optional[VectorType] = None,
    vectorize: Optional[str] = None,
    include_similarity: Optional[bool] = None,
    sort: Optional[SortType] = None,
    max_time_ms: Optional[int] = None,
) -> AsyncCursor:
    """
    Find documents on the collection, matching a certain provided filter.

    The method returns a Cursor that can then be iterated over. Depending
    on the method call pattern, the iteration over all documents can reflect
    collection mutations occurred since the `find` method was called, or not.
    In cases where the cursor reflects mutations in real-time, it will iterate
    over cursors in an approximate way (i.e. exhibiting occasional skipped
    or duplicate documents). This happens when making use of the `sort`
    option in a non-vector-search manner.

    Args:
        filter: a predicate expressed as a dictionary according to the
            Data API filter syntax. Examples are:
                {}
                {"name": "John"}
                {"price": {"$le": 100}}
                {"$and": [{"name": "John"}, {"price": {"$le": 100}}]}
            See the Data API documentation for the full set of operators.
        projection: used to select a subset of fields in the documents being
            returned. The projection can be: an iterable over the field names
            to return; a dictionary {field_name: True} to positively select
            certain fields; or a dictionary {field_name: False} if one wants
            to discard some fields from the response.
            The default is to return the whole documents.
        skip: with this integer parameter, what would be the first `skip`
            documents returned by the query are discarded, and the results
            start from the (skip+1)-th document.
            This parameter can be used only in conjunction with an explicit
            `sort` criterion of the ascending/descending type (i.e. it cannot
            be used when not sorting, nor with vector-based ANN search).
        limit: this (integer) parameter sets a limit over how many documents
            are returned. Once `limit` is reached (or the cursor is exhausted
            for lack of matching documents), nothing more is returned.
        vector: a suitable vector, i.e. a list of float numbers of the appropriate
            dimensionality, to perform vector search (i.e. ANN,
            or "approximate nearest-neighbours" search).
            When running similarity search on a collection, no other sorting
            criteria can be specified. Moreover, there is an upper bound
            to the number of documents that can be returned. For details,
            see the Note about upper bounds and the Data API documentation.
        vectorize: a string to be made into a vector to perform vector search.
            This can be supplied in (exclusive) alternative to `vector`,
            provided such a service is configured for the collection,
            and achieves the same effect.
            NOTE: This feature is under current development.
        include_similarity: a boolean to request the numeric value of the
            similarity to be returned as an added "$similarity" key in each
            returned document. Can only be used for vector ANN search, i.e.
            when either `vector` is supplied or the `sort` parameter has the
            shape {"$vector": ...}.
        sort: with this dictionary parameter one can control the order
            the documents are returned. See the Note about sorting, as well as
            the one about upper bounds, for details.
        max_time_ms: a timeout, in milliseconds, for each single one
            of the underlying HTTP requests used to fetch documents as the
            cursor is iterated over.

    Returns:
        an AsyncCursor object representing iterations over the matching documents
        (see the AsyncCursor object for how to use it. The simplest thing is to
        run a for loop: `for document in collection.sort(...):`).

    Examples:
        >>> async def run_finds(acol: AsyncCollection) -> None:
        ...             filter = {"seq": {"$exists": True}}
        ...             print("find results 1:")
        ...             async for doc in acol.find(filter, projection={"seq": True}, limit=5):
        ...                 print(doc["seq"])
        ...             async_cursor1 = acol.find(
        ...                 {},
        ...                 limit=4,
        ...                 sort={"seq": astrapy.constants.SortDocuments.DESCENDING},
        ...             )
        ...             ids = [doc["_id"] async for doc in async_cursor1]
        ...             print("find results 2:", ids)
        ...             async_cursor2 = acol.find({}, limit=3)
        ...             seqs = await async_cursor2.distinct("seq")
        ...             print("distinct results 3:", seqs)
        ...
        >>> asyncio.run(run_finds(my_async_coll))
        find results 1:
        48
        35
        7
        11
        13
        find results 2: ['d656cd9d-...', '479c7ce8-...', '96dc87fd-...', '83f0a21f-...']
        distinct results 3: [48, 35, 7]

        >>> async def run_vector_finds(acol: AsyncCollection) -> None:
        ...     await acol.insert_many([
        ...         {"tag": "A", "$vector": [4, 5]},
        ...         {"tag": "B", "$vector": [3, 4]},
        ...         {"tag": "C", "$vector": [3, 2]},
        ...         {"tag": "D", "$vector": [4, 1]},
        ...         {"tag": "E", "$vector": [2, 5]},
        ...     ])
        ...     ann_tags = [
        ...         document["tag"]
        ...         async for document in acol.find(
        ...             {},
        ...             limit=3,
        ...             vector=[3, 3],
        ...         )
        ...     ]
        ...     return ann_tags
        ...
        >>> asyncio.run(run_vector_finds(my_async_coll))
        ['A', 'B', 'C']
        # (assuming the collection has metric VectorMetric.COSINE)

    Note:
        The following are example values for the `sort` parameter.
        When no particular order is required:
            sort={}
        When sorting by a certain value in ascending/descending order:
            sort={"field": SortDocuments.ASCENDING}
            sort={"field": SortDocuments.DESCENDING}
        When sorting first by "field" and then by "subfield"
        (while modern Python versions preserve the order of dictionaries,
        it is suggested for clarity to employ a `collections.OrderedDict`
        in these cases):
            sort={
                "field": SortDocuments.ASCENDING,
                "subfield": SortDocuments.ASCENDING,
            }
        When running a vector similarity (ANN) search:
            sort={"$vector": [0.4, 0.15, -0.5]}

    Note:
        Some combinations of arguments impose an implicit upper bound on the
        number of documents that are returned by the Data API. More specifically:
        (a) Vector ANN searches cannot return more than a number of documents
        that at the time of writing is set to 1000 items.
        (b) When using a sort criterion of the ascending/descending type,
        the Data API will return a smaller number of documents, set to 20
        at the time of writing, and stop there. The returned documents are
        the top results across the whole collection according to the requested
        criterion.
        These provisions should be kept in mind even when subsequently running
        a command such as `.distinct()` on a cursor.

    Note:
        When not specifying sorting criteria at all (by vector or otherwise),
        the cursor can scroll through an arbitrary number of documents as
        the Data API and the client periodically exchange new chunks of documents.
        It should be noted that the behavior of the cursor in the case documents
        have been added/removed after the `find` was started depends on database
        internals and it is not guaranteed, nor excluded, that such "real-time"
        changes in the data would be picked up by the cursor.
    """

    _sort = _collate_vector_to_sort(sort, vector, vectorize)
    if include_similarity is not None and not _is_vector_sort(_sort):
        raise ValueError(
            "Cannot use `include_similarity` when not searching through `vector`."
        )
    return (
        AsyncCursor(
            collection=self,
            filter=filter,
            projection=projection,
            max_time_ms=max_time_ms,
            overall_max_time_ms=None,
        )
        .skip(skip)
        .limit(limit)
        .sort(_sort)
        .include_similarity(include_similarity)
    )

async def find_one(self, filter: Optional[FilterType] = None, *, projection: Optional[ProjectionType] = None, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, include_similarity: Optional[bool] = None, sort: Optional[SortType] = None, max_time_ms: Optional[int] = None) ‑> Optional[Dict[str, Any]]

Run a search, returning the first document in the collection that matches provided filters, if any is found.

Args

filter

a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$le": 100}} {"$and": [{"name": "John"}, {"price": {"$le": 100}}]} See the Data API documentation for the full set of operators.

projection

used to select a subset of fields in the documents being returned. The projection can be: an iterable over the field names to return; a dictionary {field_name: True} to positively select certain fields; or a dictionary {field_name: False} if one wants to discard some fields from the response. The default is to return the whole documents.

vector

a suitable vector, i.e. a list of float numbers of the appropriate dimensionality, to perform vector search (i.e. ANN, or "approximate nearest-neighbours" search), extracting the most similar document in the collection matching the filter. This parameter cannot be used together with sort. See the find method for more details on this parameter.

vectorize

a string to be made into a vector to perform vector search. This can be supplied in (exclusive) alternative to vector, provided such a service is configured for the collection, and achieves the same effect. NOTE: This feature is under current development.

include_similarity

a boolean to request the numeric value of the similarity to be returned as an added "$similarity" key in the returned document. Can only be used for vector ANN search, i.e. when either vector is supplied or the sort parameter has the shape {"$vector": …}.

sort

with this dictionary parameter one can control the order the documents are returned. See the Note about sorting for details.

max_time_ms

a timeout, in milliseconds, for the underlying HTTP request.

Returns

a dictionary expressing the required document, otherwise None.

Example

>>> async def demo_find_one(acol: AsyncCollection) -> None:
....    print("Count:", await acol.count_documents({}, upper_bound=100))
...     result0 = await acol.find_one({})
...     print("result0", result0)
...     result1 = await acol.find_one({"seq": 10})
...     print("result1", result1)
...     result2 = await acol.find_one({"seq": 1011})
...     print("result2", result2)
...     result3 = await acol.find_one({}, projection={"seq": False})
...     print("result3", result3)
...     result4 = await acol.find_one(
...         {},
...         sort={"seq": astrapy.constants.SortDocuments.DESCENDING},
...     )
...     print("result4", result4)
...
>>>
>>> asyncio.run(demo_find_one(my_async_coll))
Count: 50
result0 {'_id': '479c7ce8-...', 'seq': 48}
result1 {'_id': '93e992c4-...', 'seq': 10}
result2 None
result3 {'_id': '479c7ce8-...'}
result4 {'_id': 'd656cd9d-...', 'seq': 49}

>>> asyncio.run(my_async_coll.find_one({}, vector=[1, 0]))
{'_id': '...', 'tag': 'D', '$vector': [4.0, 1.0]}

Note

See the find method for more details on the accepted parameters (whereas skip and limit are not valid parameters for find_one).

Expand source code

async def find_one(
    self,
    filter: Optional[FilterType] = None,
    *,
    projection: Optional[ProjectionType] = None,
    vector: Optional[VectorType] = None,
    vectorize: Optional[str] = None,
    include_similarity: Optional[bool] = None,
    sort: Optional[SortType] = None,
    max_time_ms: Optional[int] = None,
) -> Union[DocumentType, None]:
    """
    Run a search, returning the first document in the collection that matches
    provided filters, if any is found.

    Args:
        filter: a predicate expressed as a dictionary according to the
            Data API filter syntax. Examples are:
                {}
                {"name": "John"}
                {"price": {"$le": 100}}
                {"$and": [{"name": "John"}, {"price": {"$le": 100}}]}
            See the Data API documentation for the full set of operators.
        projection: used to select a subset of fields in the documents being
            returned. The projection can be: an iterable over the field names
            to return; a dictionary {field_name: True} to positively select
            certain fields; or a dictionary {field_name: False} if one wants
            to discard some fields from the response.
            The default is to return the whole documents.
        vector: a suitable vector, i.e. a list of float numbers of the appropriate
            dimensionality, to perform vector search (i.e. ANN,
            or "approximate nearest-neighbours" search), extracting the most
            similar document in the collection matching the filter.
            This parameter cannot be used together with `sort`.
            See the `find` method for more details on this parameter.
        vectorize: a string to be made into a vector to perform vector search.
            This can be supplied in (exclusive) alternative to `vector`,
            provided such a service is configured for the collection,
            and achieves the same effect.
            NOTE: This feature is under current development.
        include_similarity: a boolean to request the numeric value of the
            similarity to be returned as an added "$similarity" key in the
            returned document. Can only be used for vector ANN search, i.e.
            when either `vector` is supplied or the `sort` parameter has the
            shape {"$vector": ...}.
        sort: with this dictionary parameter one can control the order
            the documents are returned. See the Note about sorting for details.
        max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

    Returns:
        a dictionary expressing the required document, otherwise None.

    Example:
        >>> async def demo_find_one(acol: AsyncCollection) -> None:
        ....    print("Count:", await acol.count_documents({}, upper_bound=100))
        ...     result0 = await acol.find_one({})
        ...     print("result0", result0)
        ...     result1 = await acol.find_one({"seq": 10})
        ...     print("result1", result1)
        ...     result2 = await acol.find_one({"seq": 1011})
        ...     print("result2", result2)
        ...     result3 = await acol.find_one({}, projection={"seq": False})
        ...     print("result3", result3)
        ...     result4 = await acol.find_one(
        ...         {},
        ...         sort={"seq": astrapy.constants.SortDocuments.DESCENDING},
        ...     )
        ...     print("result4", result4)
        ...
        >>>
        >>> asyncio.run(demo_find_one(my_async_coll))
        Count: 50
        result0 {'_id': '479c7ce8-...', 'seq': 48}
        result1 {'_id': '93e992c4-...', 'seq': 10}
        result2 None
        result3 {'_id': '479c7ce8-...'}
        result4 {'_id': 'd656cd9d-...', 'seq': 49}

        >>> asyncio.run(my_async_coll.find_one({}, vector=[1, 0]))
        {'_id': '...', 'tag': 'D', '$vector': [4.0, 1.0]}

    Note:
        See the `find` method for more details on the accepted parameters
        (whereas `skip` and `limit` are not valid parameters for `find_one`).
    """

    fo_cursor = self.find(
        filter=filter,
        projection=projection,
        skip=None,
        limit=1,
        vector=vector,
        vectorize=vectorize,
        include_similarity=include_similarity,
        sort=sort,
        max_time_ms=max_time_ms,
    )
    try:
        document = await fo_cursor.__anext__()
        return document  # type: ignore[no-any-return]
    except StopAsyncIteration:
        return None

async def find_one_and_delete(self, filter: Dict[str, Any], *, projection: Optional[ProjectionType] = None, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, sort: Optional[SortType] = None, max_time_ms: Optional[int] = None) ‑> Optional[Dict[str, Any]]

Find a document in the collection and delete it. The deleted document, however, is the return value of the method.

Args

filter

a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$le": 100}} {"$and": [{"name": "John"}, {"price": {"$le": 100}}]} See the Data API documentation for the full set of operators.

projection

used to select a subset of fields in the document being returned. The projection can be: an iterable over the field names to return; a dictionary {field_name: True} to positively select certain fields; or a dictionary {field_name: False} if one wants to discard some fields from the response. Note that the _id field will be returned with the document in any case, regardless of what the provided projection requires. The default is to return the whole documents.

vector

a suitable vector, i.e. a list of float numbers of the appropriate dimensionality, to use vector search (i.e. ANN, or "approximate nearest-neighbours" search), as the sorting criterion. In this way, the matched document (if any) will be the one that is most similar to the provided vector. This parameter cannot be used together with sort. See the find method for more details on this parameter.

vectorize

a string to be made into a vector to perform vector search. This can be supplied in (exclusive) alternative to vector, provided such a service is configured for the collection, and achieves the same effect. NOTE: This feature is under current development.

sort

with this dictionary parameter one can control the sorting order of the documents matching the filter, effectively determining what document will come first and hence be the deleted one. See the find method for more on sorting.

max_time_ms

a timeout, in milliseconds, for the underlying HTTP request.

Returns

Either the document (or a projection thereof, as requested), or None if no matches were found in the first place.

Example

>>> async def do_find_one_and_delete(acol: AsyncCollection) -> None:
...     await acol.insert_many(
...         [
...             {"species": "swan", "class": "Aves"},
...             {"species": "frog", "class": "Amphibia"},
...         ],
...     )
...     delete_result0 = await acol.find_one_and_delete(
...         {"species": {"$ne": "frog"}},
...         projection=["species"],
...     )
...     print("delete_result0", delete_result0)
...     delete_result1 = await acol.find_one_and_delete(
...         {"species": {"$ne": "frog"}},
...     )
...     print("delete_result1", delete_result1)
...
>>> asyncio.run(do_find_one_and_delete(my_async_coll))
delete_result0 {'_id': 'f335cd0f-...', 'species': 'swan'}
delete_result1 None

Expand source code

@recast_method_async
async def find_one_and_delete(
    self,
    filter: Dict[str, Any],
    *,
    projection: Optional[ProjectionType] = None,
    vector: Optional[VectorType] = None,
    vectorize: Optional[str] = None,
    sort: Optional[SortType] = None,
    max_time_ms: Optional[int] = None,
) -> Union[DocumentType, None]:
    """
    Find a document in the collection and delete it. The deleted document,
    however, is the return value of the method.

    Args:
        filter: a predicate expressed as a dictionary according to the
            Data API filter syntax. Examples are:
                {}
                {"name": "John"}
                {"price": {"$le": 100}}
                {"$and": [{"name": "John"}, {"price": {"$le": 100}}]}
            See the Data API documentation for the full set of operators.
        projection: used to select a subset of fields in the document being
            returned. The projection can be: an iterable over the field names
            to return; a dictionary {field_name: True} to positively select
            certain fields; or a dictionary {field_name: False} if one wants
            to discard some fields from the response.
            Note that the `_id` field will be returned with the document
            in any case, regardless of what the provided `projection` requires.
            The default is to return the whole documents.
        vector: a suitable vector, i.e. a list of float numbers of the appropriate
            dimensionality, to use vector search (i.e. ANN,
            or "approximate nearest-neighbours" search), as the sorting criterion.
            In this way, the matched document (if any) will be the one
            that is most similar to the provided vector.
            This parameter cannot be used together with `sort`.
            See the `find` method for more details on this parameter.
        vectorize: a string to be made into a vector to perform vector search.
            This can be supplied in (exclusive) alternative to `vector`,
            provided such a service is configured for the collection,
            and achieves the same effect.
            NOTE: This feature is under current development.
        sort: with this dictionary parameter one can control the sorting
            order of the documents matching the filter, effectively
            determining what document will come first and hence be the
            deleted one. See the `find` method for more on sorting.
        max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

    Returns:
        Either the document (or a projection thereof, as requested), or None
        if no matches were found in the first place.

    Example:
        >>> async def do_find_one_and_delete(acol: AsyncCollection) -> None:
        ...     await acol.insert_many(
        ...         [
        ...             {"species": "swan", "class": "Aves"},
        ...             {"species": "frog", "class": "Amphibia"},
        ...         ],
        ...     )
        ...     delete_result0 = await acol.find_one_and_delete(
        ...         {"species": {"$ne": "frog"}},
        ...         projection=["species"],
        ...     )
        ...     print("delete_result0", delete_result0)
        ...     delete_result1 = await acol.find_one_and_delete(
        ...         {"species": {"$ne": "frog"}},
        ...     )
        ...     print("delete_result1", delete_result1)
        ...
        >>> asyncio.run(do_find_one_and_delete(my_async_coll))
        delete_result0 {'_id': 'f335cd0f-...', 'species': 'swan'}
        delete_result1 None
    """

    _sort = _collate_vector_to_sort(sort, vector, vectorize)
    _projection = normalize_optional_projection(projection)
    logger.info(f"calling find_one_and_delete on '{self.name}'")
    fo_response = await self._astra_db_collection.find_one_and_delete(
        sort=_sort,
        filter=filter,
        projection=_projection,
        timeout_info=base_timeout_info(max_time_ms),
    )
    logger.info(f"finished calling find_one_and_delete on '{self.name}'")
    if "document" in fo_response.get("data", {}):
        document = fo_response["data"]["document"]
        return document  # type: ignore[no-any-return]
    else:
        deleted_count = fo_response.get("status", {}).get("deletedCount")
        if deleted_count == 0:
            return None
        else:
            raise DataAPIFaultyResponseException(
                text="Faulty response from find_one_and_delete API command.",
                raw_response=fo_response,
            )

async def find_one_and_replace(self, filter: Dict[str, Any], replacement: DocumentType, *, projection: Optional[ProjectionType] = None, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, sort: Optional[SortType] = None, upsert: bool = False, return_document: str = 'before', max_time_ms: Optional[int] = None) ‑> Optional[Dict[str, Any]]

Find a document on the collection and replace it entirely with a new one, optionally inserting a new one if no match is found.

Args

filter

a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$le": 100}} {"$and": [{"name": "John"}, {"price": {"$le": 100}}]} See the Data API documentation for the full set of operators.

replacement

the new document to write into the collection.

projection

used to select a subset of fields in the document being returned. The projection can be: an iterable over the field names to return; a dictionary {field_name: True} to positively select certain fields; or a dictionary {field_name: False} if one wants to discard some fields from the response. The default is to return the whole documents.

vector

a suitable vector, i.e. a list of float numbers of the appropriate dimensionality, to use vector search (i.e. ANN, or "approximate nearest-neighbours" search), as the sorting criterion. In this way, the matched document (if any) will be the one that is most similar to the provided vector. This parameter cannot be used together with sort. See the find method for more details on this parameter.

vectorize

a string to be made into a vector to perform vector search. This can be supplied in (exclusive) alternative to vector, provided such a service is configured for the collection, and achieves the same effect. NOTE: This feature is under current development.

sort

with this dictionary parameter one can control the sorting order of the documents matching the filter, effectively determining what document will come first and hence be the replaced one. See the find method for more on sorting.

upsert

this parameter controls the behavior in absence of matches. If True, replacement is inserted as a new document if no matches are found on the collection. If False, the operation silently does nothing in case of no matches.

return_document

a flag controlling what document is returned: if set to ReturnDocument.BEFORE, or the string "before", the document found on database is returned; if set to ReturnDocument.AFTER, or the string "after", the new document is returned. The default is "before".

max_time_ms

a timeout, in milliseconds, for the underlying HTTP request.

Returns

A document, either the one before the replace operation or the one after that. Alternatively, the method returns None to represent that no matching document was found, or that no replacement was inserted (depending on the return_document parameter).

Example

>>> async def do_find_one_and_replace(acol: AsyncCollection) -> None:
...             await acol.insert_one({"_id": "rule1", "text": "all animals are equal"})
...             result0 = await acol.find_one_and_replace(
...                 {"_id": "rule1"},
...                 {"text": "some animals are more equal!"},
...             )
...             print("result0", result0)
...             result1 = await acol.find_one_and_replace(
...                 {"text": "some animals are more equal!"},
...                 {"text": "and the pigs are the rulers"},
...                 return_document=astrapy.constants.ReturnDocument.AFTER,
...             )
...             print("result1", result1)
...             result2 = await acol.find_one_and_replace(
...                 {"_id": "rule2"},
...                 {"text": "F=ma^2"},
...                 return_document=astrapy.constants.ReturnDocument.AFTER,
...             )
...             print("result2", result2)
...             result3 = await acol.find_one_and_replace(
...                 {"_id": "rule2"},
...                 {"text": "F=ma"},
...                 upsert=True,
...                 return_document=astrapy.constants.ReturnDocument.AFTER,
...                 projection={"_id": False},
...             )
...             print("result3", result3)
...
>>> asyncio.run(do_find_one_and_replace(my_async_coll))
result0 {'_id': 'rule1', 'text': 'all animals are equal'}
result1 {'_id': 'rule1', 'text': 'and the pigs are the rulers'}
result2 None
result3 {'text': 'F=ma'}

Expand source code

@recast_method_async
async def find_one_and_replace(
    self,
    filter: Dict[str, Any],
    replacement: DocumentType,
    *,
    projection: Optional[ProjectionType] = None,
    vector: Optional[VectorType] = None,
    vectorize: Optional[str] = None,
    sort: Optional[SortType] = None,
    upsert: bool = False,
    return_document: str = ReturnDocument.BEFORE,
    max_time_ms: Optional[int] = None,
) -> Union[DocumentType, None]:
    """
    Find a document on the collection and replace it entirely with a new one,
    optionally inserting a new one if no match is found.

    Args:

        filter: a predicate expressed as a dictionary according to the
            Data API filter syntax. Examples are:
                {}
                {"name": "John"}
                {"price": {"$le": 100}}
                {"$and": [{"name": "John"}, {"price": {"$le": 100}}]}
            See the Data API documentation for the full set of operators.
        replacement: the new document to write into the collection.
        projection: used to select a subset of fields in the document being
            returned. The projection can be: an iterable over the field names
            to return; a dictionary {field_name: True} to positively select
            certain fields; or a dictionary {field_name: False} if one wants
            to discard some fields from the response.
            The default is to return the whole documents.
        vector: a suitable vector, i.e. a list of float numbers of the appropriate
            dimensionality, to use vector search (i.e. ANN,
            or "approximate nearest-neighbours" search), as the sorting criterion.
            In this way, the matched document (if any) will be the one
            that is most similar to the provided vector.
            This parameter cannot be used together with `sort`.
            See the `find` method for more details on this parameter.
        vectorize: a string to be made into a vector to perform vector search.
            This can be supplied in (exclusive) alternative to `vector`,
            provided such a service is configured for the collection,
            and achieves the same effect.
            NOTE: This feature is under current development.
        sort: with this dictionary parameter one can control the sorting
            order of the documents matching the filter, effectively
            determining what document will come first and hence be the
            replaced one. See the `find` method for more on sorting.
        upsert: this parameter controls the behavior in absence of matches.
            If True, `replacement` is inserted as a new document
            if no matches are found on the collection. If False,
            the operation silently does nothing in case of no matches.
        return_document: a flag controlling what document is returned:
            if set to `ReturnDocument.BEFORE`, or the string "before",
            the document found on database is returned; if set to
            `ReturnDocument.AFTER`, or the string "after", the new
            document is returned. The default is "before".
        max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

    Returns:
        A document, either the one before the replace operation or the
        one after that. Alternatively, the method returns None to represent
        that no matching document was found, or that no replacement
        was inserted (depending on the `return_document` parameter).

    Example:
        >>> async def do_find_one_and_replace(acol: AsyncCollection) -> None:
        ...             await acol.insert_one({"_id": "rule1", "text": "all animals are equal"})
        ...             result0 = await acol.find_one_and_replace(
        ...                 {"_id": "rule1"},
        ...                 {"text": "some animals are more equal!"},
        ...             )
        ...             print("result0", result0)
        ...             result1 = await acol.find_one_and_replace(
        ...                 {"text": "some animals are more equal!"},
        ...                 {"text": "and the pigs are the rulers"},
        ...                 return_document=astrapy.constants.ReturnDocument.AFTER,
        ...             )
        ...             print("result1", result1)
        ...             result2 = await acol.find_one_and_replace(
        ...                 {"_id": "rule2"},
        ...                 {"text": "F=ma^2"},
        ...                 return_document=astrapy.constants.ReturnDocument.AFTER,
        ...             )
        ...             print("result2", result2)
        ...             result3 = await acol.find_one_and_replace(
        ...                 {"_id": "rule2"},
        ...                 {"text": "F=ma"},
        ...                 upsert=True,
        ...                 return_document=astrapy.constants.ReturnDocument.AFTER,
        ...                 projection={"_id": False},
        ...             )
        ...             print("result3", result3)
        ...
        >>> asyncio.run(do_find_one_and_replace(my_async_coll))
        result0 {'_id': 'rule1', 'text': 'all animals are equal'}
        result1 {'_id': 'rule1', 'text': 'and the pigs are the rulers'}
        result2 None
        result3 {'text': 'F=ma'}
    """

    _sort = _collate_vector_to_sort(sort, vector, vectorize)
    options = {
        "returnDocument": return_document,
        "upsert": upsert,
    }
    logger.info(f"calling find_one_and_replace on '{self.name}'")
    fo_response = await self._astra_db_collection.find_one_and_replace(
        replacement=replacement,
        filter=filter,
        projection=normalize_optional_projection(projection),
        sort=_sort,
        options=options,
        timeout_info=base_timeout_info(max_time_ms),
    )
    logger.info(f"finished calling find_one_and_replace on '{self.name}'")
    if "document" in fo_response.get("data", {}):
        ret_document = fo_response.get("data", {}).get("document")
        if ret_document is None:
            return None
        else:
            return ret_document  # type: ignore[no-any-return]
    else:
        raise DataAPIFaultyResponseException(
            text="Faulty response from find_one_and_replace API command.",
            raw_response=fo_response,
        )

async def find_one_and_update(self, filter: Dict[str, Any], update: Dict[str, Any], *, projection: Optional[ProjectionType] = None, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, sort: Optional[SortType] = None, upsert: bool = False, return_document: str = 'before', max_time_ms: Optional[int] = None) ‑> Optional[Dict[str, Any]]

Find a document on the collection and update it as requested, optionally inserting a new one if no match is found.

Args

filter

a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$le": 100}} {"$and": [{"name": "John"}, {"price": {"$le": 100}}]} See the Data API documentation for the full set of operators.

update

the update prescription to apply to the document, expressed as a dictionary as per Data API syntax. Examples are: {"$set": {"field": "value}} {"$inc": {"counter": 10}} {"$unset": {"field": ""}} See the Data API documentation for the full syntax.

projection

used to select a subset of fields in the document being returned. The projection can be: an iterable over the field names to return; a dictionary {field_name: True} to positively select certain fields; or a dictionary {field_name: False} if one wants to discard some fields from the response. The default is to return the whole documents.

vector

a suitable vector, i.e. a list of float numbers of the appropriate dimensionality, to use vector search (i.e. ANN, or "approximate nearest-neighbours" search), as the sorting criterion. In this way, the matched document (if any) will be the one that is most similar to the provided vector. This parameter cannot be used together with sort. See the find method for more details on this parameter.

vectorize

a string to be made into a vector to perform vector search. This can be supplied in (exclusive) alternative to vector, provided such a service is configured for the collection, and achieves the same effect. NOTE: This feature is under current development.

sort

with this dictionary parameter one can control the sorting order of the documents matching the filter, effectively determining what document will come first and hence be the updated one. See the find method for more on sorting.

upsert

this parameter controls the behavior in absence of matches. If True, a new document (resulting from applying the update to an empty document) is inserted if no matches are found on the collection. If False, the operation silently does nothing in case of no matches.

return_document

a flag controlling what document is returned: if set to ReturnDocument.BEFORE, or the string "before", the document found on database is returned; if set to ReturnDocument.AFTER, or the string "after", the new document is returned. The default is "before".

max_time_ms

a timeout, in milliseconds, for the underlying HTTP request.

Returns

A document (or a projection thereof, as required), either the one before the replace operation or the one after that. Alternatively, the method returns None to represent that no matching document was found, or that no update was applied (depending on the return_document parameter).

Example

>>> async def do_find_one_and_update(acol: AsyncCollection) -> None:
...     await acol.insert_one({"Marco": "Polo"})
...     result0 = await acol.find_one_and_update(
...         {"Marco": {"$exists": True}},
...         {"$set": {"title": "Mr."}},
...     )
...     print("result0", result0)
...     result1 = await acol.find_one_and_update(
...         {"title": "Mr."},
...         {"$inc": {"rank": 3}},
...         projection=["title", "rank"],
...         return_document=astrapy.constants.ReturnDocument.AFTER,
...     )
...     print("result1", result1)
...     result2 = await acol.find_one_and_update(
...         {"name": "Johnny"},
...         {"$set": {"rank": 0}},
...         return_document=astrapy.constants.ReturnDocument.AFTER,
...     )
...     print("result2", result2)
...     result3 = await acol.find_one_and_update(
...         {"name": "Johnny"},
...         {"$set": {"rank": 0}},
...         upsert=True,
...         return_document=astrapy.constants.ReturnDocument.AFTER,
...     )
...     print("result3", result3)
...
>>> asyncio.run(do_find_one_and_update(my_async_coll))
result0 {'_id': 'f7c936d3-b0a0-45eb-a676-e2829662a57c', 'Marco': 'Polo'}
result1 {'_id': 'f7c936d3-b0a0-45eb-a676-e2829662a57c', 'title': 'Mr.', 'rank': 3}
result2 None
result3 {'_id': 'db3d678d-14d4-4caa-82d2-d5fb77dab7ec', 'name': 'Johnny', 'rank': 0}

Expand source code

@recast_method_async
async def find_one_and_update(
    self,
    filter: Dict[str, Any],
    update: Dict[str, Any],
    *,
    projection: Optional[ProjectionType] = None,
    vector: Optional[VectorType] = None,
    vectorize: Optional[str] = None,
    sort: Optional[SortType] = None,
    upsert: bool = False,
    return_document: str = ReturnDocument.BEFORE,
    max_time_ms: Optional[int] = None,
) -> Union[DocumentType, None]:
    """
    Find a document on the collection and update it as requested,
    optionally inserting a new one if no match is found.

    Args:
        filter: a predicate expressed as a dictionary according to the
            Data API filter syntax. Examples are:
                {}
                {"name": "John"}
                {"price": {"$le": 100}}
                {"$and": [{"name": "John"}, {"price": {"$le": 100}}]}
            See the Data API documentation for the full set of operators.
        update: the update prescription to apply to the document, expressed
            as a dictionary as per Data API syntax. Examples are:
                {"$set": {"field": "value}}
                {"$inc": {"counter": 10}}
                {"$unset": {"field": ""}}
            See the Data API documentation for the full syntax.
        projection: used to select a subset of fields in the document being
            returned. The projection can be: an iterable over the field names
            to return; a dictionary {field_name: True} to positively select
            certain fields; or a dictionary {field_name: False} if one wants
            to discard some fields from the response.
            The default is to return the whole documents.
        vector: a suitable vector, i.e. a list of float numbers of the appropriate
            dimensionality, to use vector search (i.e. ANN,
            or "approximate nearest-neighbours" search), as the sorting criterion.
            In this way, the matched document (if any) will be the one
            that is most similar to the provided vector.
            This parameter cannot be used together with `sort`.
            See the `find` method for more details on this parameter.
        vectorize: a string to be made into a vector to perform vector search.
            This can be supplied in (exclusive) alternative to `vector`,
            provided such a service is configured for the collection,
            and achieves the same effect.
            NOTE: This feature is under current development.
        sort: with this dictionary parameter one can control the sorting
            order of the documents matching the filter, effectively
            determining what document will come first and hence be the
            updated one. See the `find` method for more on sorting.
        upsert: this parameter controls the behavior in absence of matches.
            If True, a new document (resulting from applying the `update`
            to an empty document) is inserted if no matches are found on
            the collection. If False, the operation silently does nothing
            in case of no matches.
        return_document: a flag controlling what document is returned:
            if set to `ReturnDocument.BEFORE`, or the string "before",
            the document found on database is returned; if set to
            `ReturnDocument.AFTER`, or the string "after", the new
            document is returned. The default is "before".
        max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

    Returns:
        A document (or a projection thereof, as required), either the one
        before the replace operation or the one after that.
        Alternatively, the method returns None to represent
        that no matching document was found, or that no update
        was applied (depending on the `return_document` parameter).

    Example:
        >>> async def do_find_one_and_update(acol: AsyncCollection) -> None:
        ...     await acol.insert_one({"Marco": "Polo"})
        ...     result0 = await acol.find_one_and_update(
        ...         {"Marco": {"$exists": True}},
        ...         {"$set": {"title": "Mr."}},
        ...     )
        ...     print("result0", result0)
        ...     result1 = await acol.find_one_and_update(
        ...         {"title": "Mr."},
        ...         {"$inc": {"rank": 3}},
        ...         projection=["title", "rank"],
        ...         return_document=astrapy.constants.ReturnDocument.AFTER,
        ...     )
        ...     print("result1", result1)
        ...     result2 = await acol.find_one_and_update(
        ...         {"name": "Johnny"},
        ...         {"$set": {"rank": 0}},
        ...         return_document=astrapy.constants.ReturnDocument.AFTER,
        ...     )
        ...     print("result2", result2)
        ...     result3 = await acol.find_one_and_update(
        ...         {"name": "Johnny"},
        ...         {"$set": {"rank": 0}},
        ...         upsert=True,
        ...         return_document=astrapy.constants.ReturnDocument.AFTER,
        ...     )
        ...     print("result3", result3)
        ...
        >>> asyncio.run(do_find_one_and_update(my_async_coll))
        result0 {'_id': 'f7c936d3-b0a0-45eb-a676-e2829662a57c', 'Marco': 'Polo'}
        result1 {'_id': 'f7c936d3-b0a0-45eb-a676-e2829662a57c', 'title': 'Mr.', 'rank': 3}
        result2 None
        result3 {'_id': 'db3d678d-14d4-4caa-82d2-d5fb77dab7ec', 'name': 'Johnny', 'rank': 0}
    """

    _sort = _collate_vector_to_sort(sort, vector, vectorize)
    options = {
        "returnDocument": return_document,
        "upsert": upsert,
    }
    logger.info(f"calling find_one_and_update on '{self.name}'")
    fo_response = await self._astra_db_collection.find_one_and_update(
        update=update,
        filter=filter,
        projection=normalize_optional_projection(projection),
        sort=_sort,
        options=options,
        timeout_info=base_timeout_info(max_time_ms),
    )
    logger.info(f"finished calling find_one_and_update on '{self.name}'")
    if "document" in fo_response.get("data", {}):
        ret_document = fo_response.get("data", {}).get("document")
        if ret_document is None:
            return None
        else:
            return ret_document  # type: ignore[no-any-return]
    else:
        raise DataAPIFaultyResponseException(
            text="Faulty response from find_one_and_update API command.",
            raw_response=fo_response,
        )

def info(self) ‑> CollectionInfo

Information on the collection (name, location, database), in the form of a CollectionInfo object.

Not to be confused with the collection options method (related to the collection internal configuration).

Example

>>> my_async_coll.info().database_info.region
'us-east1'
>>> my_async_coll.info().full_name
'default_keyspace.my_v_collection'

Note

the returned CollectionInfo wraps, among other things, the database information: as such, calling this method triggers the same-named method of a Database object (which, in turn, performs a HTTP request to the DevOps API). See the documentation for Database.info() for more details.

Expand source code

def info(self) -> CollectionInfo:
    """
    Information on the collection (name, location, database), in the
    form of a CollectionInfo object.

    Not to be confused with the collection `options` method (related
    to the collection internal configuration).

    Example:
        >>> my_async_coll.info().database_info.region
        'us-east1'
        >>> my_async_coll.info().full_name
        'default_keyspace.my_v_collection'

    Note:
        the returned CollectionInfo wraps, among other things,
        the database information: as such, calling this method
        triggers the same-named method of a Database object (which, in turn,
        performs a HTTP request to the DevOps API).
        See the documentation for `Database.info()` for more details.
    """

    return CollectionInfo(
        database_info=self.database.info(),
        namespace=self.namespace,
        name=self.name,
        full_name=self.full_name,
    )

async def insert_many(self, documents: Iterable[DocumentType], *, vectors: Optional[Iterable[Optional[VectorType]]] = None, vectorize: Optional[Iterable[Optional[str]]] = None, ordered: bool = True, chunk_size: Optional[int] = None, concurrency: Optional[int] = None, max_time_ms: Optional[int] = None) ‑> InsertManyResult

Insert a list of documents into the collection. This is not an atomic operation.

Args

documents

an iterable of dictionaries, each a document to insert. Documents may specify their _id field or leave it out, in which case it will be added automatically.

vectors

an optional list of vectors (as many vectors as the provided documents) to associate to the documents when inserting. Each vector is added to the corresponding document prior to insertion on database. The list can be a mixture of None and vectors, in which case some documents will not have a vector, unless it is specified in their "$vector" field already. Passing vectors this way is indeed equivalent to the "$vector" field of the documents, however the two are mutually exclusive.

vectorize

an optional list of strings to be made into as many vectors (one per document), if such a service is configured for the collection. Passing this parameter is equivalent to providing a $vectorize field in the documents themselves, however the two are mutually exclusive. For any given document, this parameter cannot coexist with the corresponding vector entry. NOTE: This feature is under current development.

ordered

if True (default), the insertions are processed sequentially. If False, they can occur in arbitrary order and possibly concurrently.

chunk_size

how many documents to include in a single API request. Exceeding the server maximum allowed value results in an error. Leave it unspecified (recommended) to use the system default.

concurrency

maximum number of concurrent requests to the API at a given time. It cannot be more than one for ordered insertions.

max_time_ms

a timeout, in milliseconds, for the operation.

Returns

an InsertManyResult object.

Examples

>>> async def write_and_count(acol: AsyncCollection) -> None:
...             count0 = await acol.count_documents({}, upper_bound=10)
...             print("count0", count0)
...             im_result1 = await acol.insert_many(
...                 [
...                     {"a": 10},
...                     {"a": 5},
...                     {"b": [True, False, False]},
...                 ],
...             )
...             print("inserted1", im_result1.inserted_ids)
...             count1 = await acol.count_documents({}, upper_bound=100)
...             print("count1", count1)
...             await acol.insert_many(
...                 [{"seq": i} for i in range(50)],
...                 ordered=False,
...                 concurrency=5,
...             )
...             count2 = await acol.count_documents({}, upper_bound=100)
...             print("count2", count2)
...
>>> asyncio.run(write_and_count(my_async_coll))
count0 0
inserted1 ['e3c2a684-...', '1de4949f-...', '167dacc3-...']
count1 3
count2 53

The following are three equivalent statements:

>>> asyncio.run(my_async_coll.insert_many(
...     [{"tag": "a"}, {"tag": "b"}],
...     vectors=[[1, 2], [3, 4]],
... ))
InsertManyResult(...)
>>> asyncio.run(my_async_coll.insert_many(
...     [{"tag": "a", "$vector": [1, 2]}, {"tag": "b"}],
...     vectors=[None, [3, 4]],
... ))
InsertManyResult(...)
>>> asyncio.run(my_async_coll.insert_many(
...     [
...         {"tag": "a", "$vector": [1, 2]},
...         {"tag": "b", "$vector": [3, 4]},
...     ]
... ))
InsertManyResult(...)

Note

Unordered insertions are executed with some degree of concurrency, so it is usually better to prefer this mode unless the order in the document sequence is important.

Note

A failure mode for this command is related to certain faulty documents found among those to insert: a document may have the an _id already present on the collection, or its vector dimension may not match the collection setting.

For an ordered insertion, the method will raise an exception at the first such faulty document – nevertheless, all documents processed until then will end up being written to the database.

For unordered insertions, if the error stems from faulty documents the insertion proceeds until exhausting the input documents: then, an exception is raised – and all insertable documents will have been written to the database, including those "after" the troublesome ones.

If, on the other hand, there are errors not related to individual documents (such as a network connectivity error), the whole insert_many operation will stop in mid-way, an exception will be raised, and only a certain amount of the input documents will have made their way to the database.

Expand source code

@recast_method_async
async def insert_many(
    self,
    documents: Iterable[DocumentType],
    *,
    vectors: Optional[Iterable[Optional[VectorType]]] = None,
    vectorize: Optional[Iterable[Optional[str]]] = None,
    ordered: bool = True,
    chunk_size: Optional[int] = None,
    concurrency: Optional[int] = None,
    max_time_ms: Optional[int] = None,
) -> InsertManyResult:
    """
    Insert a list of documents into the collection.
    This is not an atomic operation.

    Args:
        documents: an iterable of dictionaries, each a document to insert.
            Documents may specify their `_id` field or leave it out, in which
            case it will be added automatically.
        vectors: an optional list of vectors (as many vectors as the provided
            documents) to associate to the documents when inserting.
            Each vector is added to the corresponding document prior to
            insertion on database. The list can be a mixture of None and vectors,
            in which case some documents will not have a vector, unless it is
            specified in their "$vector" field already.
            Passing vectors this way is indeed equivalent to the "$vector" field
            of the documents, however the two are mutually exclusive.
        vectorize: an optional list of strings to be made into as many vectors
            (one per document), if such a service is configured for the collection.
            Passing this parameter is equivalent to providing a `$vectorize`
            field in the documents themselves, however the two are mutually exclusive.
            For any given document, this parameter cannot coexist with the
            corresponding `vector` entry.
            NOTE: This feature is under current development.
        ordered: if True (default), the insertions are processed sequentially.
            If False, they can occur in arbitrary order and possibly concurrently.
        chunk_size: how many documents to include in a single API request.
            Exceeding the server maximum allowed value results in an error.
            Leave it unspecified (recommended) to use the system default.
        concurrency: maximum number of concurrent requests to the API at
            a given time. It cannot be more than one for ordered insertions.
        max_time_ms: a timeout, in milliseconds, for the operation.

    Returns:
        an InsertManyResult object.

    Examples:
        >>> async def write_and_count(acol: AsyncCollection) -> None:
        ...             count0 = await acol.count_documents({}, upper_bound=10)
        ...             print("count0", count0)
        ...             im_result1 = await acol.insert_many(
        ...                 [
        ...                     {"a": 10},
        ...                     {"a": 5},
        ...                     {"b": [True, False, False]},
        ...                 ],
        ...             )
        ...             print("inserted1", im_result1.inserted_ids)
        ...             count1 = await acol.count_documents({}, upper_bound=100)
        ...             print("count1", count1)
        ...             await acol.insert_many(
        ...                 [{"seq": i} for i in range(50)],
        ...                 ordered=False,
        ...                 concurrency=5,
        ...             )
        ...             count2 = await acol.count_documents({}, upper_bound=100)
        ...             print("count2", count2)
        ...
        >>> asyncio.run(write_and_count(my_async_coll))
        count0 0
        inserted1 ['e3c2a684-...', '1de4949f-...', '167dacc3-...']
        count1 3
        count2 53

        # The following are three equivalent statements:
        >>> asyncio.run(my_async_coll.insert_many(
        ...     [{"tag": "a"}, {"tag": "b"}],
        ...     vectors=[[1, 2], [3, 4]],
        ... ))
        InsertManyResult(...)
        >>> asyncio.run(my_async_coll.insert_many(
        ...     [{"tag": "a", "$vector": [1, 2]}, {"tag": "b"}],
        ...     vectors=[None, [3, 4]],
        ... ))
        InsertManyResult(...)
        >>> asyncio.run(my_async_coll.insert_many(
        ...     [
        ...         {"tag": "a", "$vector": [1, 2]},
        ...         {"tag": "b", "$vector": [3, 4]},
        ...     ]
        ... ))
        InsertManyResult(...)


    Note:
        Unordered insertions are executed with some degree of concurrency,
        so it is usually better to prefer this mode unless the order in the
        document sequence is important.

    Note:
        A failure mode for this command is related to certain faulty documents
        found among those to insert: a document may have the an `_id` already
        present on the collection, or its vector dimension may not
        match the collection setting.

        For an ordered insertion, the method will raise an exception at
        the first such faulty document -- nevertheless, all documents processed
        until then will end up being written to the database.

        For unordered insertions, if the error stems from faulty documents
        the insertion proceeds until exhausting the input documents: then,
        an exception is raised -- and all insertable documents will have been
        written to the database, including those "after" the troublesome ones.

        If, on the other hand, there are errors not related to individual
        documents (such as a network connectivity error), the whole
        `insert_many` operation will stop in mid-way, an exception will be raised,
        and only a certain amount of the input documents will
        have made their way to the database.
    """

    if concurrency is None:
        if ordered:
            _concurrency = 1
        else:
            _concurrency = DEFAULT_INSERT_MANY_CONCURRENCY
    else:
        _concurrency = concurrency
    if _concurrency > 1 and ordered:
        raise ValueError("Cannot run ordered insert_many concurrently.")
    if chunk_size is None:
        _chunk_size = MAX_INSERT_NUM_DOCUMENTS
    else:
        _chunk_size = chunk_size
    _documents = _collate_vectors_to_documents(documents, vectors, vectorize)
    logger.info(f"inserting {len(_documents)} documents in '{self.name}'")
    raw_results: List[Dict[str, Any]] = []
    timeout_manager = MultiCallTimeoutManager(overall_max_time_ms=max_time_ms)
    if ordered:
        options = {"ordered": True}
        inserted_ids: List[Any] = []
        for i in range(0, len(_documents), _chunk_size):
            logger.info(f"inserting a chunk of documents in '{self.name}'")
            chunk_response = await self._astra_db_collection.insert_many(
                documents=_documents[i : i + _chunk_size],
                options=options,
                partial_failures_allowed=True,
                timeout_info=timeout_manager.remaining_timeout_info(),
            )
            logger.info(f"finished inserting a chunk of documents in '{self.name}'")
            # accumulate the results in this call
            chunk_inserted_ids = (chunk_response.get("status") or {}).get(
                "insertedIds", []
            )
            inserted_ids += chunk_inserted_ids
            raw_results += [chunk_response]
            # if errors, quit early
            if chunk_response.get("errors", []):
                partial_result = InsertManyResult(
                    raw_results=raw_results,
                    inserted_ids=inserted_ids,
                )
                raise InsertManyException.from_response(
                    command=None,
                    raw_response=chunk_response,
                    partial_result=partial_result,
                )

        # return
        full_result = InsertManyResult(
            raw_results=raw_results,
            inserted_ids=inserted_ids,
        )
        logger.info(
            f"finished inserting {len(_documents)} documents in '{self.name}'"
        )
        return full_result

    else:
        # unordered: concurrent or not, do all of them and parse the results
        options = {"ordered": False}

        sem = asyncio.Semaphore(_concurrency)

        async def concurrent_insert_chunk(
            document_chunk: List[DocumentType],
        ) -> Dict[str, Any]:
            async with sem:
                logger.info(f"inserting a chunk of documents in '{self.name}'")
                im_response = await self._astra_db_collection.insert_many(
                    document_chunk,
                    options=options,
                    partial_failures_allowed=True,
                    timeout_info=timeout_manager.remaining_timeout_info(),
                )
                logger.info(
                    f"finished inserting a chunk of documents in '{self.name}'"
                )
                return im_response

        if _concurrency > 1:
            tasks = [
                asyncio.create_task(
                    concurrent_insert_chunk(_documents[i : i + _chunk_size])
                )
                for i in range(0, len(_documents), _chunk_size)
            ]
            raw_results = await asyncio.gather(*tasks)
        else:
            raw_results = [
                await concurrent_insert_chunk(_documents[i : i + _chunk_size])
                for i in range(0, len(_documents), _chunk_size)
            ]

        # recast raw_results
        inserted_ids = [
            inserted_id
            for chunk_response in raw_results
            for inserted_id in (chunk_response.get("status") or {}).get(
                "insertedIds", []
            )
        ]

        # check-raise
        if any(
            [chunk_response.get("errors", []) for chunk_response in raw_results]
        ):
            partial_result = InsertManyResult(
                raw_results=raw_results,
                inserted_ids=inserted_ids,
            )
            raise InsertManyException.from_responses(
                commands=[None for _ in raw_results],
                raw_responses=raw_results,
                partial_result=partial_result,
            )

        # return
        full_result = InsertManyResult(
            raw_results=raw_results,
            inserted_ids=inserted_ids,
        )
        logger.info(
            f"finished inserting {len(_documents)} documents in '{self.name}'"
        )
        return full_result

async def insert_one(self, document: DocumentType, *, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, max_time_ms: Optional[int] = None) ‑> InsertOneResult

Insert a single document in the collection in an atomic operation.

Args

document

the dictionary expressing the document to insert. The _id field of the document can be left out, in which case it will be created automatically.

vector

a vector (a list of numbers appropriate for the collection) for the document. Passing this parameter is equivalent to providing the vector in the "$vector" field of the document itself, however the two are mutually exclusive.

vectorize

a string to be made into a vector, if such a service is configured for the collection. Passing this parameter is equivalent to providing a $vectorize field in the document itself, however the two are mutually exclusive. Moreover, this parameter cannot coexist with vector. NOTE: This feature is under current development.

max_time_ms

a timeout, in milliseconds, for the underlying HTTP request.

Returns

an InsertOneResult object.

Example

>>> async def write_and_count(acol: AsyncCollection) -> None:
...     count0 = await acol.count_documents({}, upper_bound=10)
...     print("count0", count0)
...     await acol.insert_one(
...         {
...             "age": 30,
...             "name": "Smith",
...             "food": ["pear", "peach"],
...             "likes_fruit": True,
...         },
...     )
...     await acol.insert_one({"_id": "user-123", "age": 50, "name": "Maccio"})
...     count1 = await acol.count_documents({}, upper_bound=10)
...     print("count1", count1)
...
>>> asyncio.run(write_and_count(my_async_coll))
count0 0
count1 2

>>> asyncio.run(my_async_coll.insert_one({"tag": v"}, vector=[10, 11]))
InsertOneResult(...)

Note

If an _id is explicitly provided, which corresponds to a document that exists already in the collection, an error is raised and the insertion fails.

Expand source code

@recast_method_async
async def insert_one(
    self,
    document: DocumentType,
    *,
    vector: Optional[VectorType] = None,
    vectorize: Optional[str] = None,
    max_time_ms: Optional[int] = None,
) -> InsertOneResult:
    """
    Insert a single document in the collection in an atomic operation.

    Args:
        document: the dictionary expressing the document to insert.
            The `_id` field of the document can be left out, in which
            case it will be created automatically.
        vector: a vector (a list of numbers appropriate for the collection)
            for the document. Passing this parameter is equivalent to
            providing the vector in the "$vector" field of the document itself,
            however the two are mutually exclusive.
        vectorize: a string to be made into a vector, if such a service
            is configured for the collection. Passing this parameter is
            equivalent to providing a `$vectorize` field in the document itself,
            however the two are mutually exclusive.
            Moreover, this parameter cannot coexist with `vector`.
            NOTE: This feature is under current development.
        max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

    Returns:
        an InsertOneResult object.

    Example:
        >>> async def write_and_count(acol: AsyncCollection) -> None:
        ...     count0 = await acol.count_documents({}, upper_bound=10)
        ...     print("count0", count0)
        ...     await acol.insert_one(
        ...         {
        ...             "age": 30,
        ...             "name": "Smith",
        ...             "food": ["pear", "peach"],
        ...             "likes_fruit": True,
        ...         },
        ...     )
        ...     await acol.insert_one({"_id": "user-123", "age": 50, "name": "Maccio"})
        ...     count1 = await acol.count_documents({}, upper_bound=10)
        ...     print("count1", count1)
        ...
        >>> asyncio.run(write_and_count(my_async_coll))
        count0 0
        count1 2

        >>> asyncio.run(my_async_coll.insert_one({"tag": v"}, vector=[10, 11]))
        InsertOneResult(...)

    Note:
        If an `_id` is explicitly provided, which corresponds to a document
        that exists already in the collection, an error is raised and
        the insertion fails.
    """

    _document = _collate_vector_to_document(document, vector, vectorize)
    logger.info(f"inserting one document in '{self.name}'")
    io_response = await self._astra_db_collection.insert_one(
        _document,
        timeout_info=base_timeout_info(max_time_ms),
    )
    logger.info(f"finished inserting one document in '{self.name}'")
    if "insertedIds" in io_response.get("status", {}):
        if io_response["status"]["insertedIds"]:
            inserted_id = io_response["status"]["insertedIds"][0]
            return InsertOneResult(
                raw_results=[io_response],
                inserted_id=inserted_id,
            )
        else:
            raise ValueError(
                "Could not complete a insert_one operation. "
                f"(gotten '${json.dumps(io_response)}')"
            )
    else:
        raise ValueError(
            "Could not complete a insert_one operation. "
            f"(gotten '${json.dumps(io_response)}')"
        )

async def options(self, *, max_time_ms: Optional[int] = None) ‑> CollectionOptions

Get the collection options, i.e. its configuration as read from the database.

The method issues a request to the Data API each time is invoked, without caching mechanisms: this ensures up-to-date information for usages such as real-time collection validation by the application.

Args

max_time_ms

a timeout, in milliseconds, for the underlying HTTP request.

Returns

a CollectionOptions instance describing the collection. (See also the database list_collections method.)

Example

>>> asyncio.run(my_async_coll.options())
CollectionOptions(vector=CollectionVectorOptions(dimension=3, metric='cosine'))

Expand source code

async def options(self, *, max_time_ms: Optional[int] = None) -> CollectionOptions:
    """
    Get the collection options, i.e. its configuration as read from the database.

    The method issues a request to the Data API each time is invoked,
    without caching mechanisms: this ensures up-to-date information
    for usages such as real-time collection validation by the application.

    Args:
        max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

    Returns:
        a CollectionOptions instance describing the collection.
        (See also the database `list_collections` method.)

    Example:
        >>> asyncio.run(my_async_coll.options())
        CollectionOptions(vector=CollectionVectorOptions(dimension=3, metric='cosine'))
    """

    logger.info(f"getting collections in search of '{self.name}'")
    self_descriptors = [
        coll_desc
        async for coll_desc in self.database.list_collections(
            max_time_ms=max_time_ms
        )
        if coll_desc.name == self.name
    ]
    logger.info(f"finished getting collections in search of '{self.name}'")
    if self_descriptors:
        return self_descriptors[0].options  # type: ignore[no-any-return]
    else:
        raise CollectionNotFoundException(
            text=f"Collection {self.namespace}.{self.name} not found.",
            namespace=self.namespace,
            collection_name=self.name,
        )

async def replace_one(self, filter: Dict[str, Any], replacement: DocumentType, *, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, sort: Optional[SortType] = None, upsert: bool = False, max_time_ms: Optional[int] = None) ‑> UpdateResult

Replace a single document on the collection with a new one, optionally inserting a new one if no match is found.

Args

filter

a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$le": 100}} {"$and": [{"name": "John"}, {"price": {"$le": 100}}]} See the Data API documentation for the full set of operators.

replacement

the new document to write into the collection.

vector

a suitable vector, i.e. a list of float numbers of the appropriate dimensionality, to use vector search (i.e. ANN, or "approximate nearest-neighbours" search), as the sorting criterion. In this way, the matched document (if any) will be the one that is most similar to the provided vector. This parameter cannot be used together with sort. See the find method for more details on this parameter.

vectorize

a string to be made into a vector to perform vector search. This can be supplied in (exclusive) alternative to vector, provided such a service is configured for the collection, and achieves the same effect. NOTE: This feature is under current development.

sort

with this dictionary parameter one can control the sorting order of the documents matching the filter, effectively determining what document will come first and hence be the replaced one. See the find method for more on sorting.

upsert

this parameter controls the behavior in absence of matches. If True, replacement is inserted as a new document if no matches are found on the collection. If False, the operation silently does nothing in case of no matches.

max_time_ms

a timeout, in milliseconds, for the underlying HTTP request.

Returns

an UpdateResult object summarizing the outcome of the replace operation.

Example

>>> async def do_replace_one(acol: AsyncCollection) -> None:
...     await acol.insert_one({"Marco": "Polo"})
...     result0 = await acol.replace_one(
...         {"Marco": {"$exists": True}},
...         {"Buda": "Pest"},
...     )
...     print("result0.update_info", result0.update_info)
...     doc1 = await acol.find_one({"Buda": "Pest"})
...     print("doc1", doc1)
...     result1 = await acol.replace_one(
...         {"Mirco": {"$exists": True}},
...         {"Oh": "yeah?"},
...     )
...     print("result1.update_info", result1.update_info)
...     result2 = await acol.replace_one(
...         {"Mirco": {"$exists": True}},
...         {"Oh": "yeah?"},
...         upsert=True,
...     )
...     print("result2.update_info", result2.update_info)
...
>>> asyncio.run(do_replace_one(my_async_coll))
result0.update_info {'n': 1, 'updatedExisting': True, 'ok': 1.0, 'nModified': 1}
doc1 {'_id': '6e669a5a-...', 'Buda': 'Pest'}
result1.update_info {'n': 0, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0}
result2.update_info {'n': 1, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0, 'upserted': '30e34e00-...'}

Expand source code

@recast_method_async
async def replace_one(
    self,
    filter: Dict[str, Any],
    replacement: DocumentType,
    *,
    vector: Optional[VectorType] = None,
    vectorize: Optional[str] = None,
    sort: Optional[SortType] = None,
    upsert: bool = False,
    max_time_ms: Optional[int] = None,
) -> UpdateResult:
    """
    Replace a single document on the collection with a new one,
    optionally inserting a new one if no match is found.

    Args:
        filter: a predicate expressed as a dictionary according to the
            Data API filter syntax. Examples are:
                {}
                {"name": "John"}
                {"price": {"$le": 100}}
                {"$and": [{"name": "John"}, {"price": {"$le": 100}}]}
            See the Data API documentation for the full set of operators.
        replacement: the new document to write into the collection.
        vector: a suitable vector, i.e. a list of float numbers of the appropriate
            dimensionality, to use vector search (i.e. ANN,
            or "approximate nearest-neighbours" search), as the sorting criterion.
            In this way, the matched document (if any) will be the one
            that is most similar to the provided vector.
            This parameter cannot be used together with `sort`.
            See the `find` method for more details on this parameter.
        vectorize: a string to be made into a vector to perform vector search.
            This can be supplied in (exclusive) alternative to `vector`,
            provided such a service is configured for the collection,
            and achieves the same effect.
            NOTE: This feature is under current development.
        sort: with this dictionary parameter one can control the sorting
            order of the documents matching the filter, effectively
            determining what document will come first and hence be the
            replaced one. See the `find` method for more on sorting.
        upsert: this parameter controls the behavior in absence of matches.
            If True, `replacement` is inserted as a new document
            if no matches are found on the collection. If False,
            the operation silently does nothing in case of no matches.
        max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

    Returns:
        an UpdateResult object summarizing the outcome of the replace operation.

    Example:
        >>> async def do_replace_one(acol: AsyncCollection) -> None:
        ...     await acol.insert_one({"Marco": "Polo"})
        ...     result0 = await acol.replace_one(
        ...         {"Marco": {"$exists": True}},
        ...         {"Buda": "Pest"},
        ...     )
        ...     print("result0.update_info", result0.update_info)
        ...     doc1 = await acol.find_one({"Buda": "Pest"})
        ...     print("doc1", doc1)
        ...     result1 = await acol.replace_one(
        ...         {"Mirco": {"$exists": True}},
        ...         {"Oh": "yeah?"},
        ...     )
        ...     print("result1.update_info", result1.update_info)
        ...     result2 = await acol.replace_one(
        ...         {"Mirco": {"$exists": True}},
        ...         {"Oh": "yeah?"},
        ...         upsert=True,
        ...     )
        ...     print("result2.update_info", result2.update_info)
        ...
        >>> asyncio.run(do_replace_one(my_async_coll))
        result0.update_info {'n': 1, 'updatedExisting': True, 'ok': 1.0, 'nModified': 1}
        doc1 {'_id': '6e669a5a-...', 'Buda': 'Pest'}
        result1.update_info {'n': 0, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0}
        result2.update_info {'n': 1, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0, 'upserted': '30e34e00-...'}
    """

    _sort = _collate_vector_to_sort(sort, vector, vectorize)
    options = {
        "upsert": upsert,
    }
    logger.info(f"calling find_one_and_replace on '{self.name}'")
    fo_response = await self._astra_db_collection.find_one_and_replace(
        replacement=replacement,
        filter=filter,
        sort=_sort,
        options=options,
        timeout_info=base_timeout_info(max_time_ms),
    )
    logger.info(f"finished calling find_one_and_replace on '{self.name}'")
    if "document" in fo_response.get("data", {}):
        fo_status = fo_response.get("status") or {}
        _update_info = _prepare_update_info([fo_status])
        return UpdateResult(
            raw_results=[fo_response],
            update_info=_update_info,
        )
    else:
        raise DataAPIFaultyResponseException(
            text="Faulty response from find_one_and_replace API command.",
            raw_response=fo_response,
        )

def set_caller(self, caller_name: Optional[str] = None, caller_version: Optional[str] = None) ‑> None

Set a new identity for the application/framework on behalf of which the Data API calls are performed (the "caller").

Args

caller_name

name of the application, or framework, on behalf of which the Data API calls are performed. This ends up in the request user-agent.

caller_version

version of the caller.

Example

>>> my_coll.set_caller(caller_name="the_caller", caller_version="0.1.0")

Expand source code

def set_caller(
    self,
    caller_name: Optional[str] = None,
    caller_version: Optional[str] = None,
) -> None:
    """
    Set a new identity for the application/framework on behalf of which
    the Data API calls are performed (the "caller").

    Args:
        caller_name: name of the application, or framework, on behalf of which
            the Data API calls are performed. This ends up in the request user-agent.
        caller_version: version of the caller.

    Example:
        >>> my_coll.set_caller(caller_name="the_caller", caller_version="0.1.0")
    """

    logger.info(f"setting caller to {caller_name}/{caller_version}")
    self._astra_db_collection.set_caller(
        caller_name=caller_name,
        caller_version=caller_version,
    )

def to_sync(self, *, database: Optional[Database] = None, name: Optional[str] = None, namespace: Optional[str] = None, caller_name: Optional[str] = None, caller_version: Optional[str] = None) ‑> Collection

Create a Collection from this one. Save for the arguments explicitly provided as overrides, everything else is kept identical to this collection in the copy (the database is converted into a sync object).

Args

database

a Database object, instantiated earlier. This represents the database the new collection belongs to.

name

the collection name. This parameter should match an existing collection on the database.

namespace

this is the namespace to which the collection belongs. If not specified, the database's working namespace is used.

caller_name

name of the application, or framework, on behalf of which the Data API calls are performed. This ends up in the request user-agent.

caller_version

version of the caller.

Returns

the new copy, a Collection instance.

Example

>>> my_async_coll.to_sync().count_documents({}, upper_bound=100)
77

Expand source code

def to_sync(
    self,
    *,
    database: Optional[Database] = None,
    name: Optional[str] = None,
    namespace: Optional[str] = None,
    caller_name: Optional[str] = None,
    caller_version: Optional[str] = None,
) -> Collection:
    """
    Create a Collection from this one. Save for the arguments
    explicitly provided as overrides, everything else is kept identical
    to this collection in the copy (the database is converted into
    a sync object).

    Args:
        database: a Database object, instantiated earlier.
            This represents the database the new collection belongs to.
        name: the collection name. This parameter should match an existing
            collection on the database.
        namespace: this is the namespace to which the collection belongs.
            If not specified, the database's working namespace is used.
        caller_name: name of the application, or framework, on behalf of which
            the Data API calls are performed. This ends up in the request user-agent.
        caller_version: version of the caller.

    Returns:
        the new copy, a Collection instance.

    Example:
        >>> my_async_coll.to_sync().count_documents({}, upper_bound=100)
        77
    """

    return Collection(
        database=database or self.database.to_sync(),
        name=name or self.name,
        namespace=namespace or self.namespace,
        caller_name=caller_name or self._astra_db_collection.caller_name,
        caller_version=caller_version or self._astra_db_collection.caller_version,
    )

async def update_many(self, filter: Dict[str, Any], update: Dict[str, Any], *, upsert: bool = False, max_time_ms: Optional[int] = None) ‑> UpdateResult

Apply an update operations to all documents matching a condition, optionally inserting one documents in absence of matches.

Args

filter

a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$le": 100}} {"$and": [{"name": "John"}, {"price": {"$le": 100}}]} See the Data API documentation for the full set of operators.

update

the update prescription to apply to the documents, expressed as a dictionary as per Data API syntax. Examples are: {"$set": {"field": "value}} {"$inc": {"counter": 10}} {"$unset": {"field": ""}} See the Data API documentation for the full syntax.

upsert

this parameter controls the behavior in absence of matches. If True, a single new document (resulting from applying update to an empty document) is inserted if no matches are found on the collection. If False, the operation silently does nothing in case of no matches.

max_time_ms

a timeout, in milliseconds, for the operation.

Returns

an UpdateResult object summarizing the outcome of the update operation.

Example

>>> async def do_update_many(acol: AsyncCollection) -> None:
...     await acol.insert_many([{"c": "red"}, {"c": "green"}, {"c": "blue"}])
...     result0 = await acol.update_many(
...         {"c": {"$ne": "green"}},
...         {"$set": {"nongreen": True}},
...     )
...     print("result0.update_info", result0.update_info)
...     result1 = await acol.update_many(
...         {"c": "orange"},
...         {"$set": {"is_also_fruit": True}},
...     )
...     print("result1.update_info", result1.update_info)
...     result2 = await acol.update_many(
...         {"c": "orange"},
...         {"$set": {"is_also_fruit": True}},
...         upsert=True,
...     )
...     print("result2.update_info", result2.update_info)
...
>>> asyncio.run(do_update_many(my_async_coll))
result0.update_info {'n': 2, 'updatedExisting': True, 'ok': 1.0, 'nModified': 2}
result1.update_info {'n': 0, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0}
result2.update_info {'n': 1, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0, 'upserted': '79ffd5a3-ab99-4dff-a2a5-4aaa0e59e854'}

Note

Similarly to the case of find (see its docstring for more details), running this command while, at the same time, another process is inserting new documents which match the filter of the update_many can result in an unpredictable fraction of these documents being updated. In other words, it cannot be easily predicted whether a given newly-inserted document will be picked up by the update_many command or not.

Expand source code

@recast_method_async
async def update_many(
    self,
    filter: Dict[str, Any],
    update: Dict[str, Any],
    *,
    upsert: bool = False,
    max_time_ms: Optional[int] = None,
) -> UpdateResult:
    """
    Apply an update operations to all documents matching a condition,
    optionally inserting one documents in absence of matches.

    Args:
        filter: a predicate expressed as a dictionary according to the
            Data API filter syntax. Examples are:
                {}
                {"name": "John"}
                {"price": {"$le": 100}}
                {"$and": [{"name": "John"}, {"price": {"$le": 100}}]}
            See the Data API documentation for the full set of operators.
        update: the update prescription to apply to the documents, expressed
            as a dictionary as per Data API syntax. Examples are:
                {"$set": {"field": "value}}
                {"$inc": {"counter": 10}}
                {"$unset": {"field": ""}}
            See the Data API documentation for the full syntax.
        upsert: this parameter controls the behavior in absence of matches.
            If True, a single new document (resulting from applying `update`
            to an empty document) is inserted if no matches are found on
            the collection. If False, the operation silently does nothing
            in case of no matches.
        max_time_ms: a timeout, in milliseconds, for the operation.

    Returns:
        an UpdateResult object summarizing the outcome of the update operation.

    Example:
        >>> async def do_update_many(acol: AsyncCollection) -> None:
        ...     await acol.insert_many([{"c": "red"}, {"c": "green"}, {"c": "blue"}])
        ...     result0 = await acol.update_many(
        ...         {"c": {"$ne": "green"}},
        ...         {"$set": {"nongreen": True}},
        ...     )
        ...     print("result0.update_info", result0.update_info)
        ...     result1 = await acol.update_many(
        ...         {"c": "orange"},
        ...         {"$set": {"is_also_fruit": True}},
        ...     )
        ...     print("result1.update_info", result1.update_info)
        ...     result2 = await acol.update_many(
        ...         {"c": "orange"},
        ...         {"$set": {"is_also_fruit": True}},
        ...         upsert=True,
        ...     )
        ...     print("result2.update_info", result2.update_info)
        ...
        >>> asyncio.run(do_update_many(my_async_coll))
        result0.update_info {'n': 2, 'updatedExisting': True, 'ok': 1.0, 'nModified': 2}
        result1.update_info {'n': 0, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0}
        result2.update_info {'n': 1, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0, 'upserted': '79ffd5a3-ab99-4dff-a2a5-4aaa0e59e854'}

    Note:
        Similarly to the case of `find` (see its docstring for more details),
        running this command while, at the same time, another process is
        inserting new documents which match the filter of the `update_many`
        can result in an unpredictable fraction of these documents being updated.
        In other words, it cannot be easily predicted whether a given
        newly-inserted document will be picked up by the update_many command or not.
    """

    base_options = {
        "upsert": upsert,
    }
    page_state_options: Dict[str, str] = {}
    um_responses: List[Dict[str, Any]] = []
    um_statuses: List[Dict[str, Any]] = []
    must_proceed = True
    timeout_manager = MultiCallTimeoutManager(overall_max_time_ms=max_time_ms)
    logger.info(f"starting update_many on '{self.name}'")
    while must_proceed:
        options = {**base_options, **page_state_options}
        logger.info(f"calling update_many on '{self.name}'")
        this_um_response = await self._astra_db_collection.update_many(
            update=update,
            filter=filter,
            options=options,
            timeout_info=timeout_manager.remaining_timeout_info(),
        )
        logger.info(f"finished calling update_many on '{self.name}'")
        this_um_status = this_um_response.get("status") or {}
        #
        # if errors, quit early
        if this_um_response.get("errors", []):
            partial_update_info = _prepare_update_info(um_statuses)
            partial_result = UpdateResult(
                raw_results=um_responses,
                update_info=partial_update_info,
            )
            all_um_responses = um_responses + [this_um_response]
            raise UpdateManyException.from_responses(
                commands=[None for _ in all_um_responses],
                raw_responses=all_um_responses,
                partial_result=partial_result,
            )
        else:
            if "status" not in this_um_response:
                raise DataAPIFaultyResponseException(
                    text="Faulty response from update_many API command.",
                    raw_response=this_um_response,
                )
            um_responses.append(this_um_response)
            um_statuses.append(this_um_status)
            next_page_state = this_um_status.get("nextPageState")
            if next_page_state is not None:
                must_proceed = True
                page_state_options = {"pageState": next_page_state}
            else:
                must_proceed = False
                page_state_options = {}

    update_info = _prepare_update_info(um_statuses)
    logger.info(f"finished update_many on '{self.name}'")
    return UpdateResult(
        raw_results=um_responses,
        update_info=update_info,
    )

async def update_one(self, filter: Dict[str, Any], update: Dict[str, Any], *, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, sort: Optional[SortType] = None, upsert: bool = False, max_time_ms: Optional[int] = None) ‑> UpdateResult

Update a single document on the collection as requested, optionally inserting a new one if no match is found.

Args

filter

a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$le": 100}} {"$and": [{"name": "John"}, {"price": {"$le": 100}}]} See the Data API documentation for the full set of operators.

update

the update prescription to apply to the document, expressed as a dictionary as per Data API syntax. Examples are: {"$set": {"field": "value}} {"$inc": {"counter": 10}} {"$unset": {"field": ""}} See the Data API documentation for the full syntax.

vector

a suitable vector, i.e. a list of float numbers of the appropriate dimensionality, to use vector search (i.e. ANN, or "approximate nearest-neighbours" search), as the sorting criterion. In this way, the matched document (if any) will be the one that is most similar to the provided vector. This parameter cannot be used together with sort. See the find method for more details on this parameter.

vectorize

a string to be made into a vector to perform vector search. This can be supplied in (exclusive) alternative to vector, provided such a service is configured for the collection, and achieves the same effect. NOTE: This feature is under current development.

sort

with this dictionary parameter one can control the sorting order of the documents matching the filter, effectively determining what document will come first and hence be the updated one. See the find method for more on sorting.

upsert

this parameter controls the behavior in absence of matches. If True, a new document (resulting from applying the update to an empty document) is inserted if no matches are found on the collection. If False, the operation silently does nothing in case of no matches.

max_time_ms

a timeout, in milliseconds, for the underlying HTTP request.

Returns

an UpdateResult object summarizing the outcome of the update operation.

Example

>>> async def do_update_one(acol: AsyncCollection) -> None:
...     await acol.insert_one({"Marco": "Polo"})
...     result0 = await acol.update_one(
...         {"Marco": {"$exists": True}},
...         {"$inc": {"rank": 3}},
...     )
...     print("result0.update_info", result0.update_info)
...     result1 = await acol.update_one(
...         {"Mirko": {"$exists": True}},
...         {"$inc": {"rank": 3}},
...     )
...     print("result1.update_info", result1.update_info)
...     result2 = await acol.update_one(
...         {"Mirko": {"$exists": True}},
...         {"$inc": {"rank": 3}},
...         upsert=True,
...     )
...     print("result2.update_info", result2.update_info)
...
>>> asyncio.run(do_update_one(my_async_coll))
result0.update_info {'n': 1, 'updatedExisting': True, 'ok': 1.0, 'nModified': 1})
result1.update_info {'n': 0, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0})
result2.update_info {'n': 1, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0, 'upserted': '75748092-...'}

Expand source code

@recast_method_async
async def update_one(
    self,
    filter: Dict[str, Any],
    update: Dict[str, Any],
    *,
    vector: Optional[VectorType] = None,
    vectorize: Optional[str] = None,
    sort: Optional[SortType] = None,
    upsert: bool = False,
    max_time_ms: Optional[int] = None,
) -> UpdateResult:
    """
    Update a single document on the collection as requested,
    optionally inserting a new one if no match is found.

    Args:
        filter: a predicate expressed as a dictionary according to the
            Data API filter syntax. Examples are:
                {}
                {"name": "John"}
                {"price": {"$le": 100}}
                {"$and": [{"name": "John"}, {"price": {"$le": 100}}]}
            See the Data API documentation for the full set of operators.
        update: the update prescription to apply to the document, expressed
            as a dictionary as per Data API syntax. Examples are:
                {"$set": {"field": "value}}
                {"$inc": {"counter": 10}}
                {"$unset": {"field": ""}}
            See the Data API documentation for the full syntax.
        vector: a suitable vector, i.e. a list of float numbers of the appropriate
            dimensionality, to use vector search (i.e. ANN,
            or "approximate nearest-neighbours" search), as the sorting criterion.
            In this way, the matched document (if any) will be the one
            that is most similar to the provided vector.
            This parameter cannot be used together with `sort`.
            See the `find` method for more details on this parameter.
        vectorize: a string to be made into a vector to perform vector search.
            This can be supplied in (exclusive) alternative to `vector`,
            provided such a service is configured for the collection,
            and achieves the same effect.
            NOTE: This feature is under current development.
        sort: with this dictionary parameter one can control the sorting
            order of the documents matching the filter, effectively
            determining what document will come first and hence be the
            updated one. See the `find` method for more on sorting.
        upsert: this parameter controls the behavior in absence of matches.
            If True, a new document (resulting from applying the `update`
            to an empty document) is inserted if no matches are found on
            the collection. If False, the operation silently does nothing
            in case of no matches.
        max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

    Returns:
        an UpdateResult object summarizing the outcome of the update operation.

    Example:
        >>> async def do_update_one(acol: AsyncCollection) -> None:
        ...     await acol.insert_one({"Marco": "Polo"})
        ...     result0 = await acol.update_one(
        ...         {"Marco": {"$exists": True}},
        ...         {"$inc": {"rank": 3}},
        ...     )
        ...     print("result0.update_info", result0.update_info)
        ...     result1 = await acol.update_one(
        ...         {"Mirko": {"$exists": True}},
        ...         {"$inc": {"rank": 3}},
        ...     )
        ...     print("result1.update_info", result1.update_info)
        ...     result2 = await acol.update_one(
        ...         {"Mirko": {"$exists": True}},
        ...         {"$inc": {"rank": 3}},
        ...         upsert=True,
        ...     )
        ...     print("result2.update_info", result2.update_info)
        ...
        >>> asyncio.run(do_update_one(my_async_coll))
        result0.update_info {'n': 1, 'updatedExisting': True, 'ok': 1.0, 'nModified': 1})
        result1.update_info {'n': 0, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0})
        result2.update_info {'n': 1, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0, 'upserted': '75748092-...'}
    """

    _sort = _collate_vector_to_sort(sort, vector, vectorize)
    options = {
        "upsert": upsert,
    }
    logger.info(f"calling find_one_and_update on '{self.name}'")
    fo_response = await self._astra_db_collection.find_one_and_update(
        update=update,
        sort=_sort,
        filter=filter,
        options=options,
        timeout_info=base_timeout_info(max_time_ms),
    )
    logger.info(f"finished calling find_one_and_update on '{self.name}'")
    if "document" in fo_response.get("data", {}):
        fo_status = fo_response.get("status") or {}
        _update_info = _prepare_update_info([fo_status])
        return UpdateResult(
            raw_results=[fo_response],
            update_info=_update_info,
        )
    else:
        raise DataAPIFaultyResponseException(
            text="Faulty response from find_one_and_update API command.",
            raw_response=fo_response,
        )

def with_options(self, *, name: Optional[str] = None, caller_name: Optional[str] = None, caller_version: Optional[str] = None) ‑> AsyncCollection

Create a clone of this collection with some changed attributes.

Args

name

the name of the collection. This parameter is useful to quickly spawn AsyncCollection instances each pointing to a different collection existing in the same namespace.

caller_name

name of the application, or framework, on behalf of which the Data API calls are performed. This ends up in the request user-agent.

caller_version

version of the caller.

Returns

a new AsyncCollection instance.

Example

>>> my_other_async_coll = my_async_coll.with_options(
...     name="the_other_coll",
...     caller_name="caller_identity",
... )

Expand source code

def with_options(
    self,
    *,
    name: Optional[str] = None,
    caller_name: Optional[str] = None,
    caller_version: Optional[str] = None,
) -> AsyncCollection:
    """
    Create a clone of this collection with some changed attributes.

    Args:
        name: the name of the collection. This parameter is useful to
            quickly spawn AsyncCollection instances each pointing to a different
            collection existing in the same namespace.
        caller_name: name of the application, or framework, on behalf of which
            the Data API calls are performed. This ends up in the request user-agent.
        caller_version: version of the caller.

    Returns:
        a new AsyncCollection instance.

    Example:
        >>> my_other_async_coll = my_async_coll.with_options(
        ...     name="the_other_coll",
        ...     caller_name="caller_identity",
        ... )
    """

    return self._copy(
        name=name,
        caller_name=caller_name,
        caller_version=caller_version,
    )

>>> from astrapy import DataAPIClient
>>> my_client = astrapy.DataAPIClient("AstraCS:...")
>>> my_db = my_client.get_async_database_by_api_endpoint(
...    "https://01234567-....apps.astra.datastax.com"
... )

class AsyncDatabase:
    """
    A Data API database. This is the entry-point object for doing database-level
    DML, such as creating/deleting collections, and for obtaining Collection
    objects themselves. This class has an asynchronous interface.

    A Database comes with an "API Endpoint", which implies a Database object
    instance reaches a specific region (relevant point in case of multi-region
    databases).

    Args:
        api_endpoint: the full "API Endpoint" string used to reach the Data API.
            Example: "https://<database_id>-<region>.apps.astra.datastax.com"
        token: an Access Token to the database. Example: "AstraCS:xyz..."
        namespace: this is the namespace all method calls will target, unless
            one is explicitly specified in the call. If no namespace is supplied
            when creating a Database, the name "default_namespace" is set.
        caller_name: name of the application, or framework, on behalf of which
            the Data API calls are performed. This ends up in the request user-agent.
        caller_version: version of the caller.
        api_path: path to append to the API Endpoint. In typical usage, this
            should be left to its default of "/api/json".
        api_version: version specifier to append to the API path. In typical
            usage, this should be left to its default of "v1".

    Example:
        >>> from astrapy import DataAPIClient
        >>> my_client = astrapy.DataAPIClient("AstraCS:...")
        >>> my_db = my_client.get_async_database_by_api_endpoint(
        ...    "https://01234567-....apps.astra.datastax.com"
        ... )

    Note:
        creating an instance of AsyncDatabase does not trigger actual creation
        of the database itself, which should exist beforehand. To create databases,
        see the AstraDBAdmin class.
    """

    def __init__(
        self,
        api_endpoint: str,
        token: str,
        *,
        namespace: Optional[str] = None,
        caller_name: Optional[str] = None,
        caller_version: Optional[str] = None,
        api_path: Optional[str] = None,
        api_version: Optional[str] = None,
    ) -> None:
        self._astra_db = AsyncAstraDB(
            token=token,
            api_endpoint=api_endpoint,
            api_path=api_path,
            api_version=api_version,
            namespace=namespace,
            caller_name=caller_name,
            caller_version=caller_version,
        )
        self._name: Optional[str] = None

    def __getattr__(self, collection_name: str) -> AsyncCollection:
        return self.to_sync().get_collection(name=collection_name).to_async()

    def __getitem__(self, collection_name: str) -> AsyncCollection:
        return self.to_sync().get_collection(name=collection_name).to_async()

    def __repr__(self) -> str:
        return (
            f'{self.__class__.__name__}(api_endpoint="{self._astra_db.api_endpoint}", '
            f'token="{self._astra_db.token[:12]}...", namespace="{self._astra_db.namespace}")'
        )

    def __eq__(self, other: Any) -> bool:
        if isinstance(other, AsyncDatabase):
            return self._astra_db == other._astra_db
        else:
            return False

    async def __aenter__(self) -> AsyncDatabase:
        return self

    async def __aexit__(
        self,
        exc_type: Optional[Type[BaseException]] = None,
        exc_value: Optional[BaseException] = None,
        traceback: Optional[TracebackType] = None,
    ) -> None:
        await self._astra_db.__aexit__(
            exc_type=exc_type,
            exc_value=exc_value,
            traceback=traceback,
        )

    def _copy(
        self,
        *,
        api_endpoint: Optional[str] = None,
        token: Optional[str] = None,
        namespace: Optional[str] = None,
        caller_name: Optional[str] = None,
        caller_version: Optional[str] = None,
        api_path: Optional[str] = None,
        api_version: Optional[str] = None,
    ) -> AsyncDatabase:
        return AsyncDatabase(
            api_endpoint=api_endpoint or self._astra_db.api_endpoint,
            token=token or self._astra_db.token,
            namespace=namespace or self._astra_db.namespace,
            caller_name=caller_name or self._astra_db.caller_name,
            caller_version=caller_version or self._astra_db.caller_version,
            api_path=api_path or self._astra_db.api_path,
            api_version=api_version or self._astra_db.api_version,
        )

    def with_options(
        self,
        *,
        namespace: Optional[str] = None,
        caller_name: Optional[str] = None,
        caller_version: Optional[str] = None,
    ) -> AsyncDatabase:
        """
        Create a clone of this database with some changed attributes.

        Args:
            namespace: this is the namespace all method calls will target, unless
                one is explicitly specified in the call. If no namespace is supplied
                when creating a Database, the name "default_namespace" is set.
            caller_name: name of the application, or framework, on behalf of which
                the Data API calls are performed. This ends up in the request user-agent.
            caller_version: version of the caller.

        Returns:
            a new `AsyncDatabase` instance.

        Example:
            >>> my_async_db_2 = my_async_db.with_options(
            ...     namespace="the_other_namespace",
            ...     caller_name="the_caller",
            ...     caller_version="0.1.0",
            ... )
        """

        return self._copy(
            namespace=namespace,
            caller_name=caller_name,
            caller_version=caller_version,
        )

    def to_sync(
        self,
        *,
        api_endpoint: Optional[str] = None,
        token: Optional[str] = None,
        namespace: Optional[str] = None,
        caller_name: Optional[str] = None,
        caller_version: Optional[str] = None,
        api_path: Optional[str] = None,
        api_version: Optional[str] = None,
    ) -> Database:
        """
        Create a (synchronous) Database from this one. Save for the arguments
        explicitly provided as overrides, everything else is kept identical
        to this database in the copy.

        Args:
            api_endpoint: the full "API Endpoint" string used to reach the Data API.
                Example: "https://<database_id>-<region>.apps.astra.datastax.com"
            token: an Access Token to the database. Example: "AstraCS:xyz..."
            namespace: this is the namespace all method calls will target, unless
                one is explicitly specified in the call. If no namespace is supplied
                when creating a Database, the name "default_namespace" is set.
            caller_name: name of the application, or framework, on behalf of which
                the Data API calls are performed. This ends up in the request user-agent.
            caller_version: version of the caller.
            api_path: path to append to the API Endpoint. In typical usage, this
                should be left to its default of "/api/json".
            api_version: version specifier to append to the API path. In typical
                usage, this should be left to its default of "v1".

        Returns:
            the new copy, a `Database` instance.

        Example:
            >>> my_sync_db = my_async_db.to_sync()
            >>> my_sync_db.list_collection_names()
            ['a_collection', 'another_collection']
        """

        return Database(
            api_endpoint=api_endpoint or self._astra_db.api_endpoint,
            token=token or self._astra_db.token,
            namespace=namespace or self._astra_db.namespace,
            caller_name=caller_name or self._astra_db.caller_name,
            caller_version=caller_version or self._astra_db.caller_version,
            api_path=api_path or self._astra_db.api_path,
            api_version=api_version or self._astra_db.api_version,
        )

    def set_caller(
        self,
        caller_name: Optional[str] = None,
        caller_version: Optional[str] = None,
    ) -> None:
        """
        Set a new identity for the application/framework on behalf of which
        the Data API calls are performed (the "caller").

        Args:
            caller_name: name of the application, or framework, on behalf of which
                the Data API calls are performed. This ends up in the request user-agent.
            caller_version: version of the caller.

        Example:
            >>> my_db.set_caller(caller_name="the_caller", caller_version="0.1.0")
        """

        logger.info(f"setting caller to {caller_name}/{caller_version}")
        self._astra_db.set_caller(
            caller_name=caller_name,
            caller_version=caller_version,
        )

    def info(self) -> DatabaseInfo:
        """
        Additional information on the database as a DatabaseInfo instance.

        Some of the returned properties are dynamic throughout the lifetime
        of the database (such as raw_info["keyspaces"]). For this reason,
        each invocation of this method triggers a new request to the DevOps API.

        Example:
            >>> my_async_db.info().region
            'eu-west-1'

            >>> my_async_db.info().raw_info['datacenters'][0]['dateCreated']
            '2023-01-30T12:34:56Z'

        Note:
            see the DatabaseInfo documentation for a caveat about the difference
            between the `region` and the `raw_info["region"]` attributes.
        """

        logger.info("getting database info")
        database_info = fetch_database_info(
            self._astra_db.api_endpoint,
            token=self._astra_db.token,
            namespace=self.namespace,
        )
        if database_info is not None:
            logger.info("finished getting database info")
            return database_info
        else:
            raise DevOpsAPIException(
                "Database is not in a supported environment for this operation."
            )

    @property
    def id(self) -> str:
        """
        The ID of this database.

        Example:
            >>> my_async_db.id
            '01234567-89ab-cdef-0123-456789abcdef'
        """

        parsed_api_endpoint = parse_api_endpoint(self._astra_db.api_endpoint)
        if parsed_api_endpoint is not None:
            return parsed_api_endpoint.database_id
        else:
            raise DevOpsAPIException(
                "Database is not in a supported environment for this operation."
            )

    def name(self) -> str:
        """
        The name of this database. Note that this bears no unicity guarantees.

        Calling this method the first time involves a request
        to the DevOps API (the resulting database name is then cached).
        See the `info()` method for more details.

        Example:
            >>> my_async_db.name()
            'the_application_database'
        """

        if self._name is None:
            self._name = self.info().name
        return self._name

    @property
    def namespace(self) -> str:
        """
        The namespace this database uses as target for all commands when
        no method-call-specific namespace is specified.

        Example:
            >>> my_async_db.namespace
            'the_keyspace'
        """

        return self._astra_db.namespace

    async def get_collection(
        self, name: str, *, namespace: Optional[str] = None
    ) -> AsyncCollection:
        """
        Spawn an `AsyncCollection` object instance representing a collection
        on this database.

        Creating an `AsyncCollection` instance does not have any effect on the
        actual state of the database: in other words, for the created
        `AsyncCollection` instance to be used meaningfully, the collection
        must exist already (for instance, it should have been created
        previously by calling the `create_collection` method).

        Args:
            name: the name of the collection.
            namespace: the namespace containing the collection. If no namespace
                is specified, the setting for this database is used.

        Returns:
            an `AsyncCollection` instance, representing the desired collection
                (but without any form of validation).

        Example:
            >>> async def count_docs(adb: AsyncDatabase, c_name: str) -> int:
            ...    async_col = await adb.get_collection(c_name)
            ...    return await async_col.count_documents({}, upper_bound=100)
            ...
            >>> asyncio.run(count_docs(my_async_db, "my_collection"))
            45

        Note: the attribute and indexing syntax forms achieve the same effect
            as this method, returning an AsyncCollection, albeit
            in a synchronous way. In other words, the following are equivalent:
                await my_async_db.get_collection("coll_name")
                my_async_db.coll_name
                my_async_db["coll_name"]
        """

        # lazy importing here against circular-import error
        from astrapy.collection import AsyncCollection

        _namespace = namespace or self._astra_db.namespace
        return AsyncCollection(self, name, namespace=_namespace)

    @recast_method_async
    async def create_collection(
        self,
        name: str,
        *,
        namespace: Optional[str] = None,
        dimension: Optional[int] = None,
        metric: Optional[str] = None,
        service: Optional[Union[CollectionVectorServiceOptions, Dict[str, Any]]] = None,
        indexing: Optional[Dict[str, Any]] = None,
        default_id_type: Optional[str] = None,
        additional_options: Optional[Dict[str, Any]] = None,
        check_exists: Optional[bool] = None,
        max_time_ms: Optional[int] = None,
    ) -> AsyncCollection:
        """
        Creates a collection on the database and return the AsyncCollection
        instance that represents it.

        This is a blocking operation: the method returns when the collection
        is ready to be used. As opposed to the `get_collection` instance,
        this method triggers causes the collection to be actually created on DB.

        Args:
            name: the name of the collection.
            namespace: the namespace where the collection is to be created.
                If not specified, the general setting for this database is used.
            dimension: for vector collections, the dimension of the vectors
                (i.e. the number of their components).
            metric: the similarity metric used for vector searches.
                Allowed values are `VectorMetric.DOT_PRODUCT`, `VectorMetric.EUCLIDEAN`
                or `VectorMetric.COSINE` (default).
            service: a dictionary describing a service for
                embedding computation, e.g. `{"provider": "ab", "modelName": "xy"}`.
                Alternatively, a CollectionVectorServiceOptions object to the same effect.
                NOTE: This feature is under current development.
            indexing: optional specification of the indexing options for
                the collection, in the form of a dictionary such as
                    {"deny": [...]}
                or
                    {"allow": [...]}
            default_id_type: this sets what type of IDs the API server will
                generate when inserting documents that do not specify their
                `_id` field explicitly. Can be set to any of the values
                `DefaultIdType.UUID`, `DefaultIdType.OBJECTID`,
                `DefaultIdType.UUIDV6`, `DefaultIdType.UUIDV7`,
                `DefaultIdType.DEFAULT`.
            additional_options: any further set of key-value pairs that will
                be added to the "options" part of the payload when sending
                the Data API command to create a collection.
            check_exists: whether to run an existence check for the collection
                name before attempting to create the collection:
                If check_exists is True, an error is raised when creating
                an existing collection.
                If it is False, the creation is attempted. In this case, for
                preexisting collections, the command will succeed or fail
                depending on whether the options match or not.
            max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

        Returns:
            an `AsyncCollection` instance, representing the newly-created collection.

        Example:
            >>> async def create_and_insert(adb: AsyncDatabase) -> Dict[str, Any]:
            ...     new_a_col = await adb.create_collection("my_v_col", dimension=3)
            ...     return await new_a_col.insert_one(
            ...         {"name": "the_row"},
            ...         vector=[0.4, 0.5, 0.7],
            ...     )
            ...
            >>> asyncio.run(create_and_insert(my_async_db))
            InsertOneResult(raw_results=..., inserted_id='08f05ecf-...-...-...')

        Note:
            A collection is considered a vector collection if at least one of
            `dimension` or `service` are provided and not null. In that case,
            and only in that case, is `metric` an accepted parameter.
            Note, moreover, that if passing both these parameters, then
            the dimension must be compatible with the chosen service.
        """

        _validate_create_collection_options(
            dimension=dimension,
            metric=metric,
            service=service,
            indexing=indexing,
            default_id_type=default_id_type,
            additional_options=additional_options,
        )
        _options = {
            **(additional_options or {}),
            **({"indexing": indexing} if indexing else {}),
            **({"defaultId": {"type": default_id_type}} if default_id_type else {}),
        }

        timeout_manager = MultiCallTimeoutManager(overall_max_time_ms=max_time_ms)

        if check_exists is None:
            _check_exists = True
        else:
            _check_exists = check_exists
        existing_names: List[str]
        if _check_exists:
            logger.info(f"checking collection existence for '{name}'")
            existing_names = await self.list_collection_names(
                namespace=namespace,
                max_time_ms=timeout_manager.remaining_timeout_ms(),
            )
        else:
            existing_names = []
        driver_db = self._astra_db.copy(namespace=namespace)
        if name in existing_names:
            raise CollectionAlreadyExistsException(
                text=f"CollectionInvalid: collection {name} already exists",
                namespace=driver_db.namespace,
                collection_name=name,
            )

        service_dict: Optional[Dict[str, Any]]
        if service is not None:
            service_dict = service if isinstance(service, dict) else service.as_dict()
        else:
            service_dict = None

        logger.info(f"creating collection '{name}'")
        await driver_db.create_collection(
            name,
            options=_options,
            dimension=dimension,
            metric=metric,
            service_dict=service_dict,
            timeout_info=timeout_manager.remaining_timeout_info(),
        )
        logger.info(f"finished creating collection '{name}'")
        return await self.get_collection(name, namespace=namespace)

    @recast_method_async
    async def drop_collection(
        self,
        name_or_collection: Union[str, AsyncCollection],
        *,
        max_time_ms: Optional[int] = None,
    ) -> Dict[str, Any]:
        """
        Drop a collection from the database, along with all documents therein.

        Args:
            name_or_collection: either the name of a collection or
                an `AsyncCollection` instance.
            max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

        Returns:
            a dictionary in the form {"ok": 1} if the command succeeds.

        Example:
            >>> asyncio.run(my_async_db.list_collection_names())
            ['a_collection', 'my_v_col', 'another_col']
            >>> asyncio.run(my_async_db.drop_collection("my_v_col"))
            {'ok': 1}
            >>> asyncio.run(my_async_db.list_collection_names())
            ['a_collection', 'another_col']

        Note:
            when providing a collection name, it is assumed that the collection
            is to be found in the namespace set at database instance level.
        """

        # lazy importing here against circular-import error
        from astrapy.collection import AsyncCollection

        if isinstance(name_or_collection, AsyncCollection):
            _namespace = name_or_collection.namespace
            _name = name_or_collection.name
            logger.info(f"dropping collection '{_name}'")
            dc_response = await self._astra_db.copy(
                namespace=_namespace
            ).delete_collection(
                _name,
                timeout_info=base_timeout_info(max_time_ms),
            )
            logger.info(f"finished dropping collection '{_name}'")
            return dc_response.get("status", {})  # type: ignore[no-any-return]
        else:
            logger.info(f"dropping collection '{name_or_collection}'")
            dc_response = await self._astra_db.delete_collection(
                name_or_collection,
                timeout_info=base_timeout_info(max_time_ms),
            )
            logger.info(f"finished dropping collection '{name_or_collection}'")
            return dc_response.get("status", {})  # type: ignore[no-any-return]

    @recast_method_sync
    def list_collections(
        self,
        *,
        namespace: Optional[str] = None,
        max_time_ms: Optional[int] = None,
    ) -> AsyncCommandCursor[CollectionDescriptor]:
        """
        List all collections in a given namespace for this database.

        Args:
            namespace: the namespace to be inspected. If not specified,
                the general setting for this database is assumed.
            max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

        Returns:
            an `AsyncCommandCursor` to iterate over CollectionDescriptor instances,
            each corresponding to a collection.

        Example:
            >>> async def a_list_colls(adb: AsyncDatabase) -> None:
            ...     a_ccur = adb.list_collections()
            ...     print("* a_ccur:", a_ccur)
            ...     print("* list:", [coll async for coll in a_ccur])
            ...     async for coll in adb.list_collections():
            ...         print("* coll:", coll)
            ...
            >>> asyncio.run(a_list_colls(my_async_db))
            * a_ccur: <astrapy.cursors.AsyncCommandCursor object at ...>
            * list: [CollectionDescriptor(name='my_v_col', options=CollectionOptions())]
            * coll: CollectionDescriptor(name='my_v_col', options=CollectionOptions())
        """

        _client: AsyncAstraDB
        if namespace:
            _client = self._astra_db.copy(namespace=namespace)
        else:
            _client = self._astra_db
        logger.info("getting collections")
        gc_response = _client.to_sync().get_collections(
            options={"explain": True},
            timeout_info=base_timeout_info(max_time_ms),
        )
        if "collections" not in gc_response.get("status", {}):
            raise DataAPIFaultyResponseException(
                text="Faulty response from get_collections API command.",
                raw_response=gc_response,
            )
        else:
            # we know this is a list of dicts, to marshal into "descriptors"
            logger.info("finished getting collections")
            return AsyncCommandCursor(
                address=self._astra_db.base_url,
                items=[
                    CollectionDescriptor.from_dict(col_dict)
                    for col_dict in gc_response["status"]["collections"]
                ],
            )

    @recast_method_async
    async def list_collection_names(
        self,
        *,
        namespace: Optional[str] = None,
        max_time_ms: Optional[int] = None,
    ) -> List[str]:
        """
        List the names of all collections in a given namespace of this database.

        Args:
            namespace: the namespace to be inspected. If not specified,
                the general setting for this database is assumed.
            max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

        Returns:
            a list of the collection names as strings, in no particular order.

        Example:
            >>> asyncio.run(my_async_db.list_collection_names())
            ['a_collection', 'another_col']
        """

        logger.info("getting collection names")
        gc_response = await self._astra_db.copy(namespace=namespace).get_collections(
            timeout_info=base_timeout_info(max_time_ms)
        )
        if "collections" not in gc_response.get("status", {}):
            raise DataAPIFaultyResponseException(
                text="Faulty response from get_collections API command.",
                raw_response=gc_response,
            )
        else:
            # we know this is a list of strings
            logger.info("finished getting collection names")
            return gc_response["status"]["collections"]  # type: ignore[no-any-return]

    @recast_method_async
    async def command(
        self,
        body: Dict[str, Any],
        *,
        namespace: Optional[str] = None,
        collection_name: Optional[str] = None,
        max_time_ms: Optional[int] = None,
    ) -> Dict[str, Any]:
        """
        Send a POST request to the Data API for this database with
        an arbitrary, caller-provided payload.

        Args:
            body: a JSON-serializable dictionary, the payload of the request.
            namespace: the namespace to use. Requests always target a namespace:
                if not specified, the general setting for this database is assumed.
            collection_name: if provided, the collection name is appended at the end
                of the endpoint. In this way, this method allows collection-level
                arbitrary POST requests as well.
            max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

        Returns:
            a dictionary with the response of the HTTP request.

        Example:
            >>> asyncio.run(my_async_db.command({"findCollections": {}}))
            {'status': {'collections': ['my_coll']}}
            >>> asyncio.run(my_async_db.command(
            ...     {"countDocuments": {}},
            ...     collection_name="my_coll",
            ... )
            {'status': {'count': 123}}
        """

        if namespace:
            _client = self._astra_db.copy(namespace=namespace)
        else:
            _client = self._astra_db
        if collection_name:
            _collection = await _client.collection(collection_name)
            logger.info(f"issuing custom command to API (on '{collection_name}')")
            req_response = await _collection.post_raw_request(
                body=body,
                timeout_info=base_timeout_info(max_time_ms),
            )
            logger.info(
                f"finished issuing custom command to API (on '{collection_name}')"
            )
            return req_response
        else:
            logger.info("issuing custom command to API")
            req_response = await _client.post_raw_request(
                body=body,
                timeout_info=base_timeout_info(max_time_ms),
            )
            logger.info("finished issuing custom command to API")
            return req_response

    def get_database_admin(
        self,
        *,
        token: Optional[str] = None,
        dev_ops_url: Optional[str] = None,
        dev_ops_api_version: Optional[str] = None,
    ) -> AstraDBDatabaseAdmin:
        """
        Return an AstraDBDatabaseAdmin object corresponding to this database, for
        use in admin tasks such as managing namespaces.

        Args:
            token: an access token with enough permission on the database to
                perform the desired tasks. If omitted (as it can generally be done),
                the token of this Database is used.
            dev_ops_url: in case of custom deployments, this can be used to specify
                the URL to the DevOps API, such as "https://api.astra.datastax.com".
                Generally it can be omitted. The environment (prod/dev/...) is
                determined from the API Endpoint.
            dev_ops_api_version: this can specify a custom version of the DevOps API
                (such as "v2"). Generally not needed.

        Returns:
            An AstraDBDatabaseAdmin instance targeting this database.

        Example:
            >>> my_db_admin = my_async_db.get_database_admin()
            >>> if "new_namespace" not in my_db_admin.list_namespaces():
            ...     my_db_admin.create_namespace("new_namespace")
            >>> my_db_admin.list_namespaces()
            ['default_keyspace', 'new_namespace']
        """

        # lazy importing here to avoid circular dependency
        from astrapy.admin import AstraDBDatabaseAdmin

        return AstraDBDatabaseAdmin.from_api_endpoint(
            api_endpoint=self._astra_db.api_endpoint,
            token=token or self._astra_db.token,
            caller_name=self._astra_db.caller_name,
            caller_version=self._astra_db.caller_version,
            dev_ops_url=dev_ops_url,
            dev_ops_api_version=dev_ops_api_version,
        )

Instance variables

var id : str

The ID of this database.

Example

>>> my_async_db.id
'01234567-89ab-cdef-0123-456789abcdef'

Expand source code

@property
def id(self) -> str:
    """
    The ID of this database.

    Example:
        >>> my_async_db.id
        '01234567-89ab-cdef-0123-456789abcdef'
    """

    parsed_api_endpoint = parse_api_endpoint(self._astra_db.api_endpoint)
    if parsed_api_endpoint is not None:
        return parsed_api_endpoint.database_id
    else:
        raise DevOpsAPIException(
            "Database is not in a supported environment for this operation."
        )

var namespace : str

The namespace this database uses as target for all commands when no method-call-specific namespace is specified.

Example

>>> my_async_db.namespace
'the_keyspace'

Expand source code

@property
def namespace(self) -> str:
    """
    The namespace this database uses as target for all commands when
    no method-call-specific namespace is specified.

    Example:
        >>> my_async_db.namespace
        'the_keyspace'
    """

    return self._astra_db.namespace

Methods

async def command(self, body: Dict[str, Any], *, namespace: Optional[str] = None, collection_name: Optional[str] = None, max_time_ms: Optional[int] = None) ‑> Dict[str, Any]

Send a POST request to the Data API for this database with an arbitrary, caller-provided payload.

Args

body

a JSON-serializable dictionary, the payload of the request.

namespace

the namespace to use. Requests always target a namespace: if not specified, the general setting for this database is assumed.

collection_name

if provided, the collection name is appended at the end of the endpoint. In this way, this method allows collection-level arbitrary POST requests as well.

max_time_ms

a timeout, in milliseconds, for the underlying HTTP request.

Returns

a dictionary with the response of the HTTP request.

Example

>>> asyncio.run(my_async_db.command({"findCollections": {}}))
{'status': {'collections': ['my_coll']}}
>>> asyncio.run(my_async_db.command(
...     {"countDocuments": {}},
...     collection_name="my_coll",
... )
{'status': {'count': 123}}

Expand source code

@recast_method_async
async def command(
    self,
    body: Dict[str, Any],
    *,
    namespace: Optional[str] = None,
    collection_name: Optional[str] = None,
    max_time_ms: Optional[int] = None,
) -> Dict[str, Any]:
    """
    Send a POST request to the Data API for this database with
    an arbitrary, caller-provided payload.

    Args:
        body: a JSON-serializable dictionary, the payload of the request.
        namespace: the namespace to use. Requests always target a namespace:
            if not specified, the general setting for this database is assumed.
        collection_name: if provided, the collection name is appended at the end
            of the endpoint. In this way, this method allows collection-level
            arbitrary POST requests as well.
        max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

    Returns:
        a dictionary with the response of the HTTP request.

    Example:
        >>> asyncio.run(my_async_db.command({"findCollections": {}}))
        {'status': {'collections': ['my_coll']}}
        >>> asyncio.run(my_async_db.command(
        ...     {"countDocuments": {}},
        ...     collection_name="my_coll",
        ... )
        {'status': {'count': 123}}
    """

    if namespace:
        _client = self._astra_db.copy(namespace=namespace)
    else:
        _client = self._astra_db
    if collection_name:
        _collection = await _client.collection(collection_name)
        logger.info(f"issuing custom command to API (on '{collection_name}')")
        req_response = await _collection.post_raw_request(
            body=body,
            timeout_info=base_timeout_info(max_time_ms),
        )
        logger.info(
            f"finished issuing custom command to API (on '{collection_name}')"
        )
        return req_response
    else:
        logger.info("issuing custom command to API")
        req_response = await _client.post_raw_request(
            body=body,
            timeout_info=base_timeout_info(max_time_ms),
        )
        logger.info("finished issuing custom command to API")
        return req_response

async def create_collection(self, name: str, *, namespace: Optional[str] = None, dimension: Optional[int] = None, metric: Optional[str] = None, service: Optional[Union[CollectionVectorServiceOptions, Dict[str, Any]]] = None, indexing: Optional[Dict[str, Any]] = None, default_id_type: Optional[str] = None, additional_options: Optional[Dict[str, Any]] = None, check_exists: Optional[bool] = None, max_time_ms: Optional[int] = None) ‑> AsyncCollection

Creates a collection on the database and return the AsyncCollection instance that represents it.

This is a blocking operation: the method returns when the collection is ready to be used. As opposed to the get_collection instance, this method triggers causes the collection to be actually created on DB.

Args

name

the name of the collection.

namespace

the namespace where the collection is to be created. If not specified, the general setting for this database is used.

dimension

for vector collections, the dimension of the vectors (i.e. the number of their components).

metric

the similarity metric used for vector searches. Allowed values are VectorMetric.DOT_PRODUCT, VectorMetric.EUCLIDEAN or VectorMetric.COSINE (default).

service

a dictionary describing a service for embedding computation, e.g. {"provider": "ab", "modelName": "xy"}. Alternatively, a CollectionVectorServiceOptions object to the same effect. NOTE: This feature is under current development.

indexing

optional specification of the indexing options for the collection, in the form of a dictionary such as {"deny": […]} or

default_id_type

this sets what type of IDs the API server will generate when inserting documents that do not specify their _id field explicitly. Can be set to any of the values DefaultIdType.UUID, DefaultIdType.OBJECTID, DefaultIdType.UUIDV6, DefaultIdType.UUIDV7, DefaultIdType.DEFAULT.

additional_options

any further set of key-value pairs that will be added to the "options" part of the payload when sending the Data API command to create a collection.

check_exists

whether to run an existence check for the collection name before attempting to create the collection: If check_exists is True, an error is raised when creating an existing collection. If it is False, the creation is attempted. In this case, for preexisting collections, the command will succeed or fail depending on whether the options match or not.

max_time_ms

a timeout, in milliseconds, for the underlying HTTP request.

Returns

an AsyncCollection instance, representing the newly-created collection.

Example

>>> async def create_and_insert(adb: AsyncDatabase) -> Dict[str, Any]:
...     new_a_col = await adb.create_collection("my_v_col", dimension=3)
...     return await new_a_col.insert_one(
...         {"name": "the_row"},
...         vector=[0.4, 0.5, 0.7],
...     )
...
>>> asyncio.run(create_and_insert(my_async_db))
InsertOneResult(raw_results=..., inserted_id='08f05ecf-...-...-...')

Note

A collection is considered a vector collection if at least one of dimension or service are provided and not null. In that case, and only in that case, is metric an accepted parameter. Note, moreover, that if passing both these parameters, then the dimension must be compatible with the chosen service.

Expand source code

@recast_method_async
async def create_collection(
    self,
    name: str,
    *,
    namespace: Optional[str] = None,
    dimension: Optional[int] = None,
    metric: Optional[str] = None,
    service: Optional[Union[CollectionVectorServiceOptions, Dict[str, Any]]] = None,
    indexing: Optional[Dict[str, Any]] = None,
    default_id_type: Optional[str] = None,
    additional_options: Optional[Dict[str, Any]] = None,
    check_exists: Optional[bool] = None,
    max_time_ms: Optional[int] = None,
) -> AsyncCollection:
    """
    Creates a collection on the database and return the AsyncCollection
    instance that represents it.

    This is a blocking operation: the method returns when the collection
    is ready to be used. As opposed to the `get_collection` instance,
    this method triggers causes the collection to be actually created on DB.

    Args:
        name: the name of the collection.
        namespace: the namespace where the collection is to be created.
            If not specified, the general setting for this database is used.
        dimension: for vector collections, the dimension of the vectors
            (i.e. the number of their components).
        metric: the similarity metric used for vector searches.
            Allowed values are `VectorMetric.DOT_PRODUCT`, `VectorMetric.EUCLIDEAN`
            or `VectorMetric.COSINE` (default).
        service: a dictionary describing a service for
            embedding computation, e.g. `{"provider": "ab", "modelName": "xy"}`.
            Alternatively, a CollectionVectorServiceOptions object to the same effect.
            NOTE: This feature is under current development.
        indexing: optional specification of the indexing options for
            the collection, in the form of a dictionary such as
                {"deny": [...]}
            or
                {"allow": [...]}
        default_id_type: this sets what type of IDs the API server will
            generate when inserting documents that do not specify their
            `_id` field explicitly. Can be set to any of the values
            `DefaultIdType.UUID`, `DefaultIdType.OBJECTID`,
            `DefaultIdType.UUIDV6`, `DefaultIdType.UUIDV7`,
            `DefaultIdType.DEFAULT`.
        additional_options: any further set of key-value pairs that will
            be added to the "options" part of the payload when sending
            the Data API command to create a collection.
        check_exists: whether to run an existence check for the collection
            name before attempting to create the collection:
            If check_exists is True, an error is raised when creating
            an existing collection.
            If it is False, the creation is attempted. In this case, for
            preexisting collections, the command will succeed or fail
            depending on whether the options match or not.
        max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

    Returns:
        an `AsyncCollection` instance, representing the newly-created collection.

    Example:
        >>> async def create_and_insert(adb: AsyncDatabase) -> Dict[str, Any]:
        ...     new_a_col = await adb.create_collection("my_v_col", dimension=3)
        ...     return await new_a_col.insert_one(
        ...         {"name": "the_row"},
        ...         vector=[0.4, 0.5, 0.7],
        ...     )
        ...
        >>> asyncio.run(create_and_insert(my_async_db))
        InsertOneResult(raw_results=..., inserted_id='08f05ecf-...-...-...')

    Note:
        A collection is considered a vector collection if at least one of
        `dimension` or `service` are provided and not null. In that case,
        and only in that case, is `metric` an accepted parameter.
        Note, moreover, that if passing both these parameters, then
        the dimension must be compatible with the chosen service.
    """

    _validate_create_collection_options(
        dimension=dimension,
        metric=metric,
        service=service,
        indexing=indexing,
        default_id_type=default_id_type,
        additional_options=additional_options,
    )
    _options = {
        **(additional_options or {}),
        **({"indexing": indexing} if indexing else {}),
        **({"defaultId": {"type": default_id_type}} if default_id_type else {}),
    }

    timeout_manager = MultiCallTimeoutManager(overall_max_time_ms=max_time_ms)

    if check_exists is None:
        _check_exists = True
    else:
        _check_exists = check_exists
    existing_names: List[str]
    if _check_exists:
        logger.info(f"checking collection existence for '{name}'")
        existing_names = await self.list_collection_names(
            namespace=namespace,
            max_time_ms=timeout_manager.remaining_timeout_ms(),
        )
    else:
        existing_names = []
    driver_db = self._astra_db.copy(namespace=namespace)
    if name in existing_names:
        raise CollectionAlreadyExistsException(
            text=f"CollectionInvalid: collection {name} already exists",
            namespace=driver_db.namespace,
            collection_name=name,
        )

    service_dict: Optional[Dict[str, Any]]
    if service is not None:
        service_dict = service if isinstance(service, dict) else service.as_dict()
    else:
        service_dict = None

    logger.info(f"creating collection '{name}'")
    await driver_db.create_collection(
        name,
        options=_options,
        dimension=dimension,
        metric=metric,
        service_dict=service_dict,
        timeout_info=timeout_manager.remaining_timeout_info(),
    )
    logger.info(f"finished creating collection '{name}'")
    return await self.get_collection(name, namespace=namespace)

async def drop_collection(self, name_or_collection: Union[str, AsyncCollection], *, max_time_ms: Optional[int] = None) ‑> Dict[str, Any]

Drop a collection from the database, along with all documents therein.

Args

name_or_collection

either the name of a collection or an AsyncCollection instance.

max_time_ms

a timeout, in milliseconds, for the underlying HTTP request.

Returns

a dictionary in the form {"ok": 1} if the command succeeds.

Example

>>> asyncio.run(my_async_db.list_collection_names())
['a_collection', 'my_v_col', 'another_col']
>>> asyncio.run(my_async_db.drop_collection("my_v_col"))
{'ok': 1}
>>> asyncio.run(my_async_db.list_collection_names())
['a_collection', 'another_col']

Note

when providing a collection name, it is assumed that the collection is to be found in the namespace set at database instance level.

Expand source code

@recast_method_async
async def drop_collection(
    self,
    name_or_collection: Union[str, AsyncCollection],
    *,
    max_time_ms: Optional[int] = None,
) -> Dict[str, Any]:
    """
    Drop a collection from the database, along with all documents therein.

    Args:
        name_or_collection: either the name of a collection or
            an `AsyncCollection` instance.
        max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

    Returns:
        a dictionary in the form {"ok": 1} if the command succeeds.

    Example:
        >>> asyncio.run(my_async_db.list_collection_names())
        ['a_collection', 'my_v_col', 'another_col']
        >>> asyncio.run(my_async_db.drop_collection("my_v_col"))
        {'ok': 1}
        >>> asyncio.run(my_async_db.list_collection_names())
        ['a_collection', 'another_col']

    Note:
        when providing a collection name, it is assumed that the collection
        is to be found in the namespace set at database instance level.
    """

    # lazy importing here against circular-import error
    from astrapy.collection import AsyncCollection

    if isinstance(name_or_collection, AsyncCollection):
        _namespace = name_or_collection.namespace
        _name = name_or_collection.name
        logger.info(f"dropping collection '{_name}'")
        dc_response = await self._astra_db.copy(
            namespace=_namespace
        ).delete_collection(
            _name,
            timeout_info=base_timeout_info(max_time_ms),
        )
        logger.info(f"finished dropping collection '{_name}'")
        return dc_response.get("status", {})  # type: ignore[no-any-return]
    else:
        logger.info(f"dropping collection '{name_or_collection}'")
        dc_response = await self._astra_db.delete_collection(
            name_or_collection,
            timeout_info=base_timeout_info(max_time_ms),
        )
        logger.info(f"finished dropping collection '{name_or_collection}'")
        return dc_response.get("status", {})  # type: ignore[no-any-return]

async def get_collection(self, name: str, *, namespace: Optional[str] = None) ‑> AsyncCollection

Spawn an AsyncCollection object instance representing a collection on this database.

Creating an AsyncCollection instance does not have any effect on the actual state of the database: in other words, for the created AsyncCollection instance to be used meaningfully, the collection must exist already (for instance, it should have been created previously by calling the create_collection method).

Args

name

the name of the collection.

namespace

the namespace containing the collection. If no namespace is specified, the setting for this database is used.

Returns

an AsyncCollection instance, representing the desired collection (but without any form of validation).

Example

>>> async def count_docs(adb: AsyncDatabase, c_name: str) -> int:
...    async_col = await adb.get_collection(c_name)
...    return await async_col.count_documents({}, upper_bound=100)
...
>>> asyncio.run(count_docs(my_async_db, "my_collection"))
45

Note: the attribute and indexing syntax forms achieve the same effect as this method, returning an AsyncCollection, albeit in a synchronous way. In other words, the following are equivalent: await my_async_db.get_collection("coll_name") my_async_db.coll_name my_async_db["coll_name"]

Expand source code

async def get_collection(
    self, name: str, *, namespace: Optional[str] = None
) -> AsyncCollection:
    """
    Spawn an `AsyncCollection` object instance representing a collection
    on this database.

    Creating an `AsyncCollection` instance does not have any effect on the
    actual state of the database: in other words, for the created
    `AsyncCollection` instance to be used meaningfully, the collection
    must exist already (for instance, it should have been created
    previously by calling the `create_collection` method).

    Args:
        name: the name of the collection.
        namespace: the namespace containing the collection. If no namespace
            is specified, the setting for this database is used.

    Returns:
        an `AsyncCollection` instance, representing the desired collection
            (but without any form of validation).

    Example:
        >>> async def count_docs(adb: AsyncDatabase, c_name: str) -> int:
        ...    async_col = await adb.get_collection(c_name)
        ...    return await async_col.count_documents({}, upper_bound=100)
        ...
        >>> asyncio.run(count_docs(my_async_db, "my_collection"))
        45

    Note: the attribute and indexing syntax forms achieve the same effect
        as this method, returning an AsyncCollection, albeit
        in a synchronous way. In other words, the following are equivalent:
            await my_async_db.get_collection("coll_name")
            my_async_db.coll_name
            my_async_db["coll_name"]
    """

    # lazy importing here against circular-import error
    from astrapy.collection import AsyncCollection

    _namespace = namespace or self._astra_db.namespace
    return AsyncCollection(self, name, namespace=_namespace)

def get_database_admin(self, *, token: Optional[str] = None, dev_ops_url: Optional[str] = None, dev_ops_api_version: Optional[str] = None) ‑> AstraDBDatabaseAdmin

Return an AstraDBDatabaseAdmin object corresponding to this database, for use in admin tasks such as managing namespaces.

Args

token

an access token with enough permission on the database to perform the desired tasks. If omitted (as it can generally be done), the token of this Database is used.

dev_ops_url

in case of custom deployments, this can be used to specify the URL to the DevOps API, such as "https://api.astra.datastax.com". Generally it can be omitted. The environment (prod/dev/…) is determined from the API Endpoint.

dev_ops_api_version

this can specify a custom version of the DevOps API (such as "v2"). Generally not needed.

Returns

An AstraDBDatabaseAdmin instance targeting this database.

Example

>>> my_db_admin = my_async_db.get_database_admin()
>>> if "new_namespace" not in my_db_admin.list_namespaces():
...     my_db_admin.create_namespace("new_namespace")
>>> my_db_admin.list_namespaces()
['default_keyspace', 'new_namespace']

Expand source code

def get_database_admin(
    self,
    *,
    token: Optional[str] = None,
    dev_ops_url: Optional[str] = None,
    dev_ops_api_version: Optional[str] = None,
) -> AstraDBDatabaseAdmin:
    """
    Return an AstraDBDatabaseAdmin object corresponding to this database, for
    use in admin tasks such as managing namespaces.

    Args:
        token: an access token with enough permission on the database to
            perform the desired tasks. If omitted (as it can generally be done),
            the token of this Database is used.
        dev_ops_url: in case of custom deployments, this can be used to specify
            the URL to the DevOps API, such as "https://api.astra.datastax.com".
            Generally it can be omitted. The environment (prod/dev/...) is
            determined from the API Endpoint.
        dev_ops_api_version: this can specify a custom version of the DevOps API
            (such as "v2"). Generally not needed.

    Returns:
        An AstraDBDatabaseAdmin instance targeting this database.

    Example:
        >>> my_db_admin = my_async_db.get_database_admin()
        >>> if "new_namespace" not in my_db_admin.list_namespaces():
        ...     my_db_admin.create_namespace("new_namespace")
        >>> my_db_admin.list_namespaces()
        ['default_keyspace', 'new_namespace']
    """

    # lazy importing here to avoid circular dependency
    from astrapy.admin import AstraDBDatabaseAdmin

    return AstraDBDatabaseAdmin.from_api_endpoint(
        api_endpoint=self._astra_db.api_endpoint,
        token=token or self._astra_db.token,
        caller_name=self._astra_db.caller_name,
        caller_version=self._astra_db.caller_version,
        dev_ops_url=dev_ops_url,
        dev_ops_api_version=dev_ops_api_version,
    )

def info(self) ‑> DatabaseInfo

Additional information on the database as a DatabaseInfo instance.

Some of the returned properties are dynamic throughout the lifetime of the database (such as raw_info["keyspaces"]). For this reason, each invocation of this method triggers a new request to the DevOps API.

Example

>>> my_async_db.info().region
'eu-west-1'

>>> my_async_db.info().raw_info['datacenters'][0]['dateCreated']
'2023-01-30T12:34:56Z'

Note

see the DatabaseInfo documentation for a caveat about the difference between the region and the raw_info["region"] attributes.

Expand source code

def info(self) -> DatabaseInfo:
    """
    Additional information on the database as a DatabaseInfo instance.

    Some of the returned properties are dynamic throughout the lifetime
    of the database (such as raw_info["keyspaces"]). For this reason,
    each invocation of this method triggers a new request to the DevOps API.

    Example:
        >>> my_async_db.info().region
        'eu-west-1'

        >>> my_async_db.info().raw_info['datacenters'][0]['dateCreated']
        '2023-01-30T12:34:56Z'

    Note:
        see the DatabaseInfo documentation for a caveat about the difference
        between the `region` and the `raw_info["region"]` attributes.
    """

    logger.info("getting database info")
    database_info = fetch_database_info(
        self._astra_db.api_endpoint,
        token=self._astra_db.token,
        namespace=self.namespace,
    )
    if database_info is not None:
        logger.info("finished getting database info")
        return database_info
    else:
        raise DevOpsAPIException(
            "Database is not in a supported environment for this operation."
        )

async def list_collection_names(self, *, namespace: Optional[str] = None, max_time_ms: Optional[int] = None) ‑> List[str]

List the names of all collections in a given namespace of this database.

Args

namespace

the namespace to be inspected. If not specified, the general setting for this database is assumed.

max_time_ms

a timeout, in milliseconds, for the underlying HTTP request.

Returns

a list of the collection names as strings, in no particular order.

Example

>>> asyncio.run(my_async_db.list_collection_names())
['a_collection', 'another_col']

Expand source code

@recast_method_async
async def list_collection_names(
    self,
    *,
    namespace: Optional[str] = None,
    max_time_ms: Optional[int] = None,
) -> List[str]:
    """
    List the names of all collections in a given namespace of this database.

    Args:
        namespace: the namespace to be inspected. If not specified,
            the general setting for this database is assumed.
        max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

    Returns:
        a list of the collection names as strings, in no particular order.

    Example:
        >>> asyncio.run(my_async_db.list_collection_names())
        ['a_collection', 'another_col']
    """

    logger.info("getting collection names")
    gc_response = await self._astra_db.copy(namespace=namespace).get_collections(
        timeout_info=base_timeout_info(max_time_ms)
    )
    if "collections" not in gc_response.get("status", {}):
        raise DataAPIFaultyResponseException(
            text="Faulty response from get_collections API command.",
            raw_response=gc_response,
        )
    else:
        # we know this is a list of strings
        logger.info("finished getting collection names")
        return gc_response["status"]["collections"]  # type: ignore[no-any-return]

def list_collections(self, *, namespace: Optional[str] = None, max_time_ms: Optional[int] = None) ‑> AsyncCommandCursor[CollectionDescriptor]

List all collections in a given namespace for this database.

Args

namespace

the namespace to be inspected. If not specified, the general setting for this database is assumed.

max_time_ms

a timeout, in milliseconds, for the underlying HTTP request.

Returns

an AsyncCommandCursor to iterate over CollectionDescriptor instances, each corresponding to a collection.

Example

>>> async def a_list_colls(adb: AsyncDatabase) -> None:
...     a_ccur = adb.list_collections()
...     print("* a_ccur:", a_ccur)
...     print("* list:", [coll async for coll in a_ccur])
...     async for coll in adb.list_collections():
...         print("* coll:", coll)
...
>>> asyncio.run(a_list_colls(my_async_db))
* a_ccur: <astrapy.cursors.AsyncCommandCursor object at ...>
* list: [CollectionDescriptor(name='my_v_col', options=CollectionOptions())]
* coll: CollectionDescriptor(name='my_v_col', options=CollectionOptions())

Expand source code

@recast_method_sync
def list_collections(
    self,
    *,
    namespace: Optional[str] = None,
    max_time_ms: Optional[int] = None,
) -> AsyncCommandCursor[CollectionDescriptor]:
    """
    List all collections in a given namespace for this database.

    Args:
        namespace: the namespace to be inspected. If not specified,
            the general setting for this database is assumed.
        max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

    Returns:
        an `AsyncCommandCursor` to iterate over CollectionDescriptor instances,
        each corresponding to a collection.

    Example:
        >>> async def a_list_colls(adb: AsyncDatabase) -> None:
        ...     a_ccur = adb.list_collections()
        ...     print("* a_ccur:", a_ccur)
        ...     print("* list:", [coll async for coll in a_ccur])
        ...     async for coll in adb.list_collections():
        ...         print("* coll:", coll)
        ...
        >>> asyncio.run(a_list_colls(my_async_db))
        * a_ccur: <astrapy.cursors.AsyncCommandCursor object at ...>
        * list: [CollectionDescriptor(name='my_v_col', options=CollectionOptions())]
        * coll: CollectionDescriptor(name='my_v_col', options=CollectionOptions())
    """

    _client: AsyncAstraDB
    if namespace:
        _client = self._astra_db.copy(namespace=namespace)
    else:
        _client = self._astra_db
    logger.info("getting collections")
    gc_response = _client.to_sync().get_collections(
        options={"explain": True},
        timeout_info=base_timeout_info(max_time_ms),
    )
    if "collections" not in gc_response.get("status", {}):
        raise DataAPIFaultyResponseException(
            text="Faulty response from get_collections API command.",
            raw_response=gc_response,
        )
    else:
        # we know this is a list of dicts, to marshal into "descriptors"
        logger.info("finished getting collections")
        return AsyncCommandCursor(
            address=self._astra_db.base_url,
            items=[
                CollectionDescriptor.from_dict(col_dict)
                for col_dict in gc_response["status"]["collections"]
            ],
        )

def name(self) ‑> str

The name of this database. Note that this bears no unicity guarantees.

Calling this method the first time involves a request to the DevOps API (the resulting database name is then cached). See the astrapy.info method for more details.

Example

>>> my_async_db.name()
'the_application_database'

Expand source code

def name(self) -> str:
    """
    The name of this database. Note that this bears no unicity guarantees.

    Calling this method the first time involves a request
    to the DevOps API (the resulting database name is then cached).
    See the `info()` method for more details.

    Example:
        >>> my_async_db.name()
        'the_application_database'
    """

    if self._name is None:
        self._name = self.info().name
    return self._name

def set_caller(self, caller_name: Optional[str] = None, caller_version: Optional[str] = None) ‑> None

Set a new identity for the application/framework on behalf of which the Data API calls are performed (the "caller").

Args

caller_name

name of the application, or framework, on behalf of which the Data API calls are performed. This ends up in the request user-agent.

caller_version

version of the caller.

Example

>>> my_db.set_caller(caller_name="the_caller", caller_version="0.1.0")

Expand source code

def set_caller(
    self,
    caller_name: Optional[str] = None,
    caller_version: Optional[str] = None,
) -> None:
    """
    Set a new identity for the application/framework on behalf of which
    the Data API calls are performed (the "caller").

    Args:
        caller_name: name of the application, or framework, on behalf of which
            the Data API calls are performed. This ends up in the request user-agent.
        caller_version: version of the caller.

    Example:
        >>> my_db.set_caller(caller_name="the_caller", caller_version="0.1.0")
    """

    logger.info(f"setting caller to {caller_name}/{caller_version}")
    self._astra_db.set_caller(
        caller_name=caller_name,
        caller_version=caller_version,
    )

def to_sync(self, *, api_endpoint: Optional[str] = None, token: Optional[str] = None, namespace: Optional[str] = None, caller_name: Optional[str] = None, caller_version: Optional[str] = None, api_path: Optional[str] = None, api_version: Optional[str] = None) ‑> Database

Create a (synchronous) Database from this one. Save for the arguments explicitly provided as overrides, everything else is kept identical to this database in the copy.

Args

api_endpoint

the full "API Endpoint" string used to reach the Data API. Example: "https://-.apps.astra.datastax.com"

token

an Access Token to the database. Example: "AstraCS:xyz…"

namespace

this is the namespace all method calls will target, unless one is explicitly specified in the call. If no namespace is supplied when creating a Database, the name "default_namespace" is set.

caller_name

name of the application, or framework, on behalf of which the Data API calls are performed. This ends up in the request user-agent.

caller_version

version of the caller.

api_path

path to append to the API Endpoint. In typical usage, this should be left to its default of "/api/json".

api_version

version specifier to append to the API path. In typical usage, this should be left to its default of "v1".

Returns

the new copy, a Database instance.

Example

>>> my_sync_db = my_async_db.to_sync()
>>> my_sync_db.list_collection_names()
['a_collection', 'another_collection']

Expand source code

def to_sync(
    self,
    *,
    api_endpoint: Optional[str] = None,
    token: Optional[str] = None,
    namespace: Optional[str] = None,
    caller_name: Optional[str] = None,
    caller_version: Optional[str] = None,
    api_path: Optional[str] = None,
    api_version: Optional[str] = None,
) -> Database:
    """
    Create a (synchronous) Database from this one. Save for the arguments
    explicitly provided as overrides, everything else is kept identical
    to this database in the copy.

    Args:
        api_endpoint: the full "API Endpoint" string used to reach the Data API.
            Example: "https://<database_id>-<region>.apps.astra.datastax.com"
        token: an Access Token to the database. Example: "AstraCS:xyz..."
        namespace: this is the namespace all method calls will target, unless
            one is explicitly specified in the call. If no namespace is supplied
            when creating a Database, the name "default_namespace" is set.
        caller_name: name of the application, or framework, on behalf of which
            the Data API calls are performed. This ends up in the request user-agent.
        caller_version: version of the caller.
        api_path: path to append to the API Endpoint. In typical usage, this
            should be left to its default of "/api/json".
        api_version: version specifier to append to the API path. In typical
            usage, this should be left to its default of "v1".

    Returns:
        the new copy, a `Database` instance.

    Example:
        >>> my_sync_db = my_async_db.to_sync()
        >>> my_sync_db.list_collection_names()
        ['a_collection', 'another_collection']
    """

    return Database(
        api_endpoint=api_endpoint or self._astra_db.api_endpoint,
        token=token or self._astra_db.token,
        namespace=namespace or self._astra_db.namespace,
        caller_name=caller_name or self._astra_db.caller_name,
        caller_version=caller_version or self._astra_db.caller_version,
        api_path=api_path or self._astra_db.api_path,
        api_version=api_version or self._astra_db.api_version,
    )

def with_options(self, *, namespace: Optional[str] = None, caller_name: Optional[str] = None, caller_version: Optional[str] = None) ‑> AsyncDatabase

Create a clone of this database with some changed attributes.

Args

namespace

this is the namespace all method calls will target, unless one is explicitly specified in the call. If no namespace is supplied when creating a Database, the name "default_namespace" is set.

caller_name

name of the application, or framework, on behalf of which the Data API calls are performed. This ends up in the request user-agent.

caller_version

version of the caller.

Returns

a new AsyncDatabase instance.

Example

>>> my_async_db_2 = my_async_db.with_options(
...     namespace="the_other_namespace",
...     caller_name="the_caller",
...     caller_version="0.1.0",
... )

Expand source code

def with_options(
    self,
    *,
    namespace: Optional[str] = None,
    caller_name: Optional[str] = None,
    caller_version: Optional[str] = None,
) -> AsyncDatabase:
    """
    Create a clone of this database with some changed attributes.

    Args:
        namespace: this is the namespace all method calls will target, unless
            one is explicitly specified in the call. If no namespace is supplied
            when creating a Database, the name "default_namespace" is set.
        caller_name: name of the application, or framework, on behalf of which
            the Data API calls are performed. This ends up in the request user-agent.
        caller_version: version of the caller.

    Returns:
        a new `AsyncDatabase` instance.

    Example:
        >>> my_async_db_2 = my_async_db.with_options(
        ...     namespace="the_other_namespace",
        ...     caller_name="the_caller",
        ...     caller_version="0.1.0",
        ... )
    """

    return self._copy(
        namespace=namespace,
        caller_name=caller_name,
        caller_version=caller_version,
    )

>>> from astrapy import DataAPIClient, Collection
>>> my_client = astrapy.DataAPIClient("AstraCS:...")
>>> my_db = my_client.get_database_by_api_endpoint(
...    "https://01234567-....apps.astra.datastax.com"
... )
>>> my_coll_1 = Collection(database=my_db, name="my_collection")
>>> my_coll_2 = my_db.create_collection(
...     "my_v_collection",
...     dimension=3,
...     metric="cosine",
... )
>>> my_coll_3a = my_db.get_collection("my_already_existing_collection")
>>> my_coll_3b = my_db.my_already_existing_collection
>>> my_coll_3c = my_db["my_already_existing_collection"]

class Collection:
    """
    A Data API collection, the main object to interact with the Data API,
    especially for DDL operations.
    This class has a synchronous interface.

    A Collection is spawned from a Database object, from which it inherits
    the details on how to reach the API server (endpoint, authentication token).

    Args:
        database: a Database object, instantiated earlier. This represents
            the database the collection belongs to.
        name: the collection name. This parameter should match an existing
            collection on the database.
        namespace: this is the namespace to which the collection belongs.
            If not specified, the database's working namespace is used.
        caller_name: name of the application, or framework, on behalf of which
            the Data API calls are performed. This ends up in the request user-agent.
        caller_version: version of the caller.

    Examples:
        >>> from astrapy import DataAPIClient, Collection
        >>> my_client = astrapy.DataAPIClient("AstraCS:...")
        >>> my_db = my_client.get_database_by_api_endpoint(
        ...    "https://01234567-....apps.astra.datastax.com"
        ... )
        >>> my_coll_1 = Collection(database=my_db, name="my_collection")
        >>> my_coll_2 = my_db.create_collection(
        ...     "my_v_collection",
        ...     dimension=3,
        ...     metric="cosine",
        ... )
        >>> my_coll_3a = my_db.get_collection("my_already_existing_collection")
        >>> my_coll_3b = my_db.my_already_existing_collection
        >>> my_coll_3c = my_db["my_already_existing_collection"]

    Note:
        creating an instance of Collection does not trigger actual creation
        of the collection on the database. The latter should have been created
        beforehand, e.g. through the `create_collection` method of a Database.
    """

    def __init__(
        self,
        database: Database,
        name: str,
        *,
        namespace: Optional[str] = None,
        caller_name: Optional[str] = None,
        caller_version: Optional[str] = None,
    ) -> None:
        self._astra_db_collection: AstraDBCollection = AstraDBCollection(
            collection_name=name,
            astra_db=database._astra_db,
            namespace=namespace,
            caller_name=caller_name,
            caller_version=caller_version,
        )
        # this comes after the above, lets AstraDBCollection resolve namespace
        self._database = database._copy(
            namespace=self._astra_db_collection.astra_db.namespace
        )

    def __repr__(self) -> str:
        return (
            f'{self.__class__.__name__}(name="{self.name}", '
            f'namespace="{self.namespace}", database={self.database})'
        )

    def __eq__(self, other: Any) -> bool:
        if isinstance(other, Collection):
            return self._astra_db_collection == other._astra_db_collection
        else:
            return False

    def __call__(self, *pargs: Any, **kwargs: Any) -> None:
        raise TypeError(
            f"'{self.__class__.__name__}' object is not callable. If you "
            f"meant to call the '{self.name}' method on a "
            f"'{self.database.__class__.__name__}' object "
            "it is failing because no such method exists."
        )

    def _copy(
        self,
        *,
        database: Optional[Database] = None,
        name: Optional[str] = None,
        namespace: Optional[str] = None,
        caller_name: Optional[str] = None,
        caller_version: Optional[str] = None,
    ) -> Collection:
        return Collection(
            database=database or self.database._copy(),
            name=name or self.name,
            namespace=namespace or self.namespace,
            caller_name=caller_name or self._astra_db_collection.caller_name,
            caller_version=caller_version or self._astra_db_collection.caller_version,
        )

    def with_options(
        self,
        *,
        name: Optional[str] = None,
        caller_name: Optional[str] = None,
        caller_version: Optional[str] = None,
    ) -> Collection:
        """
        Create a clone of this collection with some changed attributes.

        Args:
            name: the name of the collection. This parameter is useful to
                quickly spawn Collection instances each pointing to a different
                collection existing in the same namespace.
            caller_name: name of the application, or framework, on behalf of which
                the Data API calls are performed. This ends up in the request user-agent.
            caller_version: version of the caller.

        Returns:
            a new Collection instance.

        Example:
            >>> my_other_coll = my_coll.with_options(
            ...     name="the_other_coll",
            ...     caller_name="caller_identity",
            ... )
        """

        return self._copy(
            name=name,
            caller_name=caller_name,
            caller_version=caller_version,
        )

    def to_async(
        self,
        *,
        database: Optional[AsyncDatabase] = None,
        name: Optional[str] = None,
        namespace: Optional[str] = None,
        caller_name: Optional[str] = None,
        caller_version: Optional[str] = None,
    ) -> AsyncCollection:
        """
        Create an AsyncCollection from this one. Save for the arguments
        explicitly provided as overrides, everything else is kept identical
        to this collection in the copy (the database is converted into
        an async object).

        Args:
            database: an AsyncDatabase object, instantiated earlier.
                This represents the database the new collection belongs to.
            name: the collection name. This parameter should match an existing
                collection on the database.
            namespace: this is the namespace to which the collection belongs.
                If not specified, the database's working namespace is used.
            caller_name: name of the application, or framework, on behalf of which
                the Data API calls are performed. This ends up in the request user-agent.
            caller_version: version of the caller.

        Returns:
            the new copy, an AsyncCollection instance.

        Example:
            >>> asyncio.run(my_coll.to_async().count_documents({},upper_bound=100))
            77
        """

        return AsyncCollection(
            database=database or self.database.to_async(),
            name=name or self.name,
            namespace=namespace or self.namespace,
            caller_name=caller_name or self._astra_db_collection.caller_name,
            caller_version=caller_version or self._astra_db_collection.caller_version,
        )

    def set_caller(
        self,
        caller_name: Optional[str] = None,
        caller_version: Optional[str] = None,
    ) -> None:
        """
        Set a new identity for the application/framework on behalf of which
        the Data API calls are performed (the "caller").

        Args:
            caller_name: name of the application, or framework, on behalf of which
                the Data API calls are performed. This ends up in the request user-agent.
            caller_version: version of the caller.

        Example:
            >>> my_coll.set_caller(caller_name="the_caller", caller_version="0.1.0")
        """

        logger.info(f"setting caller to {caller_name}/{caller_version}")
        self._astra_db_collection.set_caller(
            caller_name=caller_name,
            caller_version=caller_version,
        )

    def options(self, *, max_time_ms: Optional[int] = None) -> CollectionOptions:
        """
        Get the collection options, i.e. its configuration as read from the database.

        The method issues a request to the Data API each time is invoked,
        without caching mechanisms: this ensures up-to-date information
        for usages such as real-time collection validation by the application.

        Args:
            max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

        Returns:
            a CollectionOptions instance describing the collection.
            (See also the database `list_collections` method.)

        Example:
            >>> my_coll.options()
            CollectionOptions(vector=CollectionVectorOptions(dimension=3, metric='cosine'))
        """

        logger.info(f"getting collections in search of '{self.name}'")
        self_descriptors = [
            coll_desc
            for coll_desc in self.database.list_collections(max_time_ms=max_time_ms)
            if coll_desc.name == self.name
        ]
        logger.info(f"finished getting collections in search of '{self.name}'")
        if self_descriptors:
            return self_descriptors[0].options  # type: ignore[no-any-return]
        else:
            raise CollectionNotFoundException(
                text=f"Collection {self.namespace}.{self.name} not found.",
                namespace=self.namespace,
                collection_name=self.name,
            )

    def info(self) -> CollectionInfo:
        """
        Information on the collection (name, location, database), in the
        form of a CollectionInfo object.

        Not to be confused with the collection `options` method (related
        to the collection internal configuration).

        Example:
            >>> my_coll.info().database_info.region
            'eu-west-1'
            >>> my_coll.info().full_name
            'default_keyspace.my_v_collection'

        Note:
            the returned CollectionInfo wraps, among other things,
            the database information: as such, calling this method
            triggers the same-named method of a Database object (which, in turn,
            performs a HTTP request to the DevOps API).
            See the documentation for `Database.info()` for more details.
        """

        return CollectionInfo(
            database_info=self.database.info(),
            namespace=self.namespace,
            name=self.name,
            full_name=self.full_name,
        )

    @property
    def database(self) -> Database:
        """
        a Database object, the database this collection belongs to.

        Example:
            >>> my_coll.database.name
            'the_application_database'
        """

        return self._database

    @property
    def namespace(self) -> str:
        """
        The namespace this collection is in.

        Example:
            >>> my_coll.namespace
            'default_keyspace'
        """

        return self.database.namespace

    @property
    def name(self) -> str:
        """
        The name of this collection.

        Example:
            >>> my_coll.name
            'my_v_collection'
        """

        # type hint added as for some reason the typechecker gets lost
        return self._astra_db_collection.collection_name  # type: ignore[no-any-return, has-type]

    @property
    def full_name(self) -> str:
        """
        The fully-qualified collection name within the database,
        in the form "namespace.collection_name".

        Example:
            >>> my_coll.full_name
            'default_keyspace.my_v_collection'
        """

        return f"{self.namespace}.{self.name}"

    @recast_method_sync
    def insert_one(
        self,
        document: DocumentType,
        *,
        vector: Optional[VectorType] = None,
        vectorize: Optional[str] = None,
        max_time_ms: Optional[int] = None,
    ) -> InsertOneResult:
        """
        Insert a single document in the collection in an atomic operation.

        Args:
            document: the dictionary expressing the document to insert.
                The `_id` field of the document can be left out, in which
                case it will be created automatically.
            vector: a vector (a list of numbers appropriate for the collection)
                for the document. Passing this parameter is equivalent to
                providing the vector in the "$vector" field of the document itself,
                however the two are mutually exclusive.
            vectorize: a string to be made into a vector, if such a service
                is configured for the collection. Passing this parameter is
                equivalent to providing a `$vectorize` field in the document itself,
                however the two are mutually exclusive.
                Moreover, this parameter cannot coexist with `vector`.
                NOTE: This feature is under current development.
            max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

        Returns:
            an InsertOneResult object.

        Examples:
            >>> my_coll.count_documents({}, upper_bound=10)
            0
            >>> my_coll.insert_one(
            ...     {
            ...         "age": 30,
            ...         "name": "Smith",
            ...         "food": ["pear", "peach"],
            ...         "likes_fruit": True,
            ...     },
            ... )
            InsertOneResult(raw_results=..., inserted_id='ed4587a4-...-...-...')
            >>> my_coll.insert_one({"_id": "user-123", "age": 50, "name": "Maccio"})
            InsertOneResult(raw_results=..., inserted_id='user-123')
            >>> my_coll.count_documents({}, upper_bound=10)
            2

            >>> my_coll.insert_one({"tag": v"}, vector=[10, 11])
            InsertOneResult(...)

        Note:
            If an `_id` is explicitly provided, which corresponds to a document
            that exists already in the collection, an error is raised and
            the insertion fails.
        """

        _document = _collate_vector_to_document(document, vector, vectorize)
        logger.info(f"inserting one document in '{self.name}'")
        io_response = self._astra_db_collection.insert_one(
            _document,
            timeout_info=base_timeout_info(max_time_ms),
        )
        logger.info(f"finished inserting one document in '{self.name}'")
        if "insertedIds" in io_response.get("status", {}):
            if io_response["status"]["insertedIds"]:
                inserted_id = io_response["status"]["insertedIds"][0]
                return InsertOneResult(
                    raw_results=[io_response],
                    inserted_id=inserted_id,
                )
            else:
                raise DataAPIFaultyResponseException(
                    text="Faulty response from insert_one API command.",
                    raw_response=io_response,
                )
        else:
            raise DataAPIFaultyResponseException(
                text="Faulty response from insert_one API command.",
                raw_response=io_response,
            )

    @recast_method_sync
    def insert_many(
        self,
        documents: Iterable[DocumentType],
        *,
        vectors: Optional[Iterable[Optional[VectorType]]] = None,
        vectorize: Optional[Iterable[Optional[str]]] = None,
        ordered: bool = True,
        chunk_size: Optional[int] = None,
        concurrency: Optional[int] = None,
        max_time_ms: Optional[int] = None,
    ) -> InsertManyResult:
        """
        Insert a list of documents into the collection.
        This is not an atomic operation.

        Args:
            documents: an iterable of dictionaries, each a document to insert.
                Documents may specify their `_id` field or leave it out, in which
                case it will be added automatically.
            vectors: an optional list of vectors (as many vectors as the provided
                documents) to associate to the documents when inserting.
                Each vector is added to the corresponding document prior to
                insertion on database. The list can be a mixture of None and vectors,
                in which case some documents will not have a vector, unless it is
                specified in their "$vector" field already.
                Passing vectors this way is indeed equivalent to the "$vector" field
                of the documents, however the two are mutually exclusive.
            vectorize: an optional list of strings to be made into as many vectors
                (one per document), if such a service is configured for the collection.
                Passing this parameter is equivalent to providing a `$vectorize`
                field in the documents themselves, however the two are mutually exclusive.
                For any given document, this parameter cannot coexist with the
                corresponding `vector` entry.
                NOTE: This feature is under current development.
            ordered: if True (default), the insertions are processed sequentially.
                If False, they can occur in arbitrary order and possibly concurrently.
            chunk_size: how many documents to include in a single API request.
                Exceeding the server maximum allowed value results in an error.
                Leave it unspecified (recommended) to use the system default.
            concurrency: maximum number of concurrent requests to the API at
                a given time. It cannot be more than one for ordered insertions.
            max_time_ms: a timeout, in milliseconds, for the operation.

        Returns:
            an InsertManyResult object.

        Examples:
            >>> my_coll.count_documents({}, upper_bound=10)
            0
            >>> my_coll.insert_many([{"a": 10}, {"a": 5}, {"b": [True, False, False]}])
            InsertManyResult(raw_results=..., inserted_ids=['184bb06f-...', '...', '...'])
            >>> my_coll.count_documents({}, upper_bound=100)
            3
            >>> my_coll.insert_many(
            ...     [{"seq": i} for i in range(50)],
            ...     ordered=False,
            ...     concurrency=5,
            ... )
            InsertManyResult(raw_results=..., inserted_ids=[... ...])
            >>> my_coll.count_documents({}, upper_bound=100)
            53

            # The following are three equivalent statements:
            >>> my_coll.insert_many(
            ...     [{"tag": "a"}, {"tag": "b"}],
            ...     vectors=[[1, 2], [3, 4]],
            ... )
            InsertManyResult(...)
            >>> my_coll.insert_many(
            ...     [{"tag": "a", "$vector": [1, 2]}, {"tag": "b"}],
            ...     vectors=[None, [3, 4]],
            ... )
            InsertManyResult(...)
            >>> my_coll.insert_many(
            ...     [
            ...         {"tag": "a", "$vector": [1, 2]},
            ...         {"tag": "b", "$vector": [3, 4]},
            ...     ]
            ... )
            InsertManyResult(...)

        Note:
            Unordered insertions are executed with some degree of concurrency,
            so it is usually better to prefer this mode unless the order in the
            document sequence is important.

        Note:
            A failure mode for this command is related to certain faulty documents
            found among those to insert: a document may have the an `_id` already
            present on the collection, or its vector dimension may not
            match the collection setting.

            For an ordered insertion, the method will raise an exception at
            the first such faulty document -- nevertheless, all documents processed
            until then will end up being written to the database.

            For unordered insertions, if the error stems from faulty documents
            the insertion proceeds until exhausting the input documents: then,
            an exception is raised -- and all insertable documents will have been
            written to the database, including those "after" the troublesome ones.

            If, on the other hand, there are errors not related to individual
            documents (such as a network connectivity error), the whole
            `insert_many` operation will stop in mid-way, an exception will be raised,
            and only a certain amount of the input documents will
            have made their way to the database.
        """

        if concurrency is None:
            if ordered:
                _concurrency = 1
            else:
                _concurrency = DEFAULT_INSERT_MANY_CONCURRENCY
        else:
            _concurrency = concurrency
        if _concurrency > 1 and ordered:
            raise ValueError("Cannot run ordered insert_many concurrently.")
        if chunk_size is None:
            _chunk_size = MAX_INSERT_NUM_DOCUMENTS
        else:
            _chunk_size = chunk_size
        _documents = _collate_vectors_to_documents(documents, vectors, vectorize)
        logger.info(f"inserting {len(_documents)} documents in '{self.name}'")
        raw_results: List[Dict[str, Any]] = []
        timeout_manager = MultiCallTimeoutManager(overall_max_time_ms=max_time_ms)
        if ordered:
            options = {"ordered": True}
            inserted_ids: List[Any] = []
            for i in range(0, len(_documents), _chunk_size):
                logger.info(f"inserting a chunk of documents in '{self.name}'")
                chunk_response = self._astra_db_collection.insert_many(
                    documents=_documents[i : i + _chunk_size],
                    options=options,
                    partial_failures_allowed=True,
                    timeout_info=timeout_manager.remaining_timeout_info(),
                )
                logger.info(f"finished inserting a chunk of documents in '{self.name}'")
                # accumulate the results in this call
                chunk_inserted_ids = (chunk_response.get("status") or {}).get(
                    "insertedIds", []
                )
                inserted_ids += chunk_inserted_ids
                raw_results += [chunk_response]
                # if errors, quit early
                if chunk_response.get("errors", []):
                    partial_result = InsertManyResult(
                        raw_results=raw_results,
                        inserted_ids=inserted_ids,
                    )
                    raise InsertManyException.from_response(
                        command=None,
                        raw_response=chunk_response,
                        partial_result=partial_result,
                    )

            # return
            full_result = InsertManyResult(
                raw_results=raw_results,
                inserted_ids=inserted_ids,
            )
            logger.info(
                f"finished inserting {len(_documents)} documents in '{self.name}'"
            )
            return full_result

        else:
            # unordered: concurrent or not, do all of them and parse the results
            options = {"ordered": False}
            if _concurrency > 1:
                with ThreadPoolExecutor(max_workers=_concurrency) as executor:

                    def _chunk_insertor(
                        document_chunk: List[Dict[str, Any]]
                    ) -> Dict[str, Any]:
                        logger.info(f"inserting a chunk of documents in '{self.name}'")
                        im_response = self._astra_db_collection.insert_many(
                            documents=document_chunk,
                            options=options,
                            partial_failures_allowed=True,
                            timeout_info=timeout_manager.remaining_timeout_info(),
                        )
                        logger.info(
                            f"finished inserting a chunk of documents in '{self.name}'"
                        )
                        return im_response

                    raw_results = list(
                        executor.map(
                            _chunk_insertor,
                            (
                                _documents[i : i + _chunk_size]
                                for i in range(0, len(_documents), _chunk_size)
                            ),
                        )
                    )
            else:
                for i in range(0, len(_documents), _chunk_size):
                    logger.info(f"inserting a chunk of documents in '{self.name}'")
                    raw_results.append(
                        self._astra_db_collection.insert_many(
                            _documents[i : i + _chunk_size],
                            options=options,
                            partial_failures_allowed=True,
                            timeout_info=timeout_manager.remaining_timeout_info(),
                        )
                    )
                    logger.info(
                        f"finished inserting a chunk of documents in '{self.name}'"
                    )
            # recast raw_results
            inserted_ids = [
                inserted_id
                for chunk_response in raw_results
                for inserted_id in (chunk_response.get("status") or {}).get(
                    "insertedIds", []
                )
            ]

            # check-raise
            if any(
                [chunk_response.get("errors", []) for chunk_response in raw_results]
            ):
                partial_result = InsertManyResult(
                    raw_results=raw_results,
                    inserted_ids=inserted_ids,
                )
                raise InsertManyException.from_responses(
                    commands=[None for _ in raw_results],
                    raw_responses=raw_results,
                    partial_result=partial_result,
                )

            # return
            full_result = InsertManyResult(
                raw_results=raw_results,
                inserted_ids=inserted_ids,
            )
            logger.info(
                f"finished inserting {len(_documents)} documents in '{self.name}'"
            )
            return full_result

    def find(
        self,
        filter: Optional[FilterType] = None,
        *,
        projection: Optional[ProjectionType] = None,
        skip: Optional[int] = None,
        limit: Optional[int] = None,
        vector: Optional[VectorType] = None,
        vectorize: Optional[str] = None,
        include_similarity: Optional[bool] = None,
        sort: Optional[SortType] = None,
        max_time_ms: Optional[int] = None,
    ) -> Cursor:
        """
        Find documents on the collection, matching a certain provided filter.

        The method returns a Cursor that can then be iterated over. Depending
        on the method call pattern, the iteration over all documents can reflect
        collection mutations occurred since the `find` method was called, or not.
        In cases where the cursor reflects mutations in real-time, it will iterate
        over cursors in an approximate way (i.e. exhibiting occasional skipped
        or duplicate documents). This happens when making use of the `sort`
        option in a non-vector-search manner.

        Args:
            filter: a predicate expressed as a dictionary according to the
                Data API filter syntax. Examples are:
                    {}
                    {"name": "John"}
                    {"price": {"$le": 100}}
                    {"$and": [{"name": "John"}, {"price": {"$le": 100}}]}
                See the Data API documentation for the full set of operators.
            projection: used to select a subset of fields in the documents being
                returned. The projection can be: an iterable over the field names
                to return; a dictionary {field_name: True} to positively select
                certain fields; or a dictionary {field_name: False} if one wants
                to discard some fields from the response.
                The default is to return the whole documents.
            skip: with this integer parameter, what would be the first `skip`
                documents returned by the query are discarded, and the results
                start from the (skip+1)-th document.
                This parameter can be used only in conjunction with an explicit
                `sort` criterion of the ascending/descending type (i.e. it cannot
                be used when not sorting, nor with vector-based ANN search).
            limit: this (integer) parameter sets a limit over how many documents
                are returned. Once `limit` is reached (or the cursor is exhausted
                for lack of matching documents), nothing more is returned.
            vector: a suitable vector, i.e. a list of float numbers of the appropriate
                dimensionality, to perform vector search (i.e. ANN,
                or "approximate nearest-neighbours" search).
                When running similarity search on a collection, no other sorting
                criteria can be specified. Moreover, there is an upper bound
                to the number of documents that can be returned. For details,
                see the Note about upper bounds and the Data API documentation.
            vectorize: a string to be made into a vector to perform vector search.
                This can be supplied in (exclusive) alternative to `vector`,
                provided such a service is configured for the collection,
                and achieves the same effect.
                NOTE: This feature is under current development.
            include_similarity: a boolean to request the numeric value of the
                similarity to be returned as an added "$similarity" key in each
                returned document. Can only be used for vector ANN search, i.e.
                when either `vector` is supplied or the `sort` parameter has the
                shape {"$vector": ...}.
            sort: with this dictionary parameter one can control the order
                the documents are returned. See the Note about sorting, as well as
                the one about upper bounds, for details.
            max_time_ms: a timeout, in milliseconds, for each single one
                of the underlying HTTP requests used to fetch documents as the
                cursor is iterated over.

        Returns:
            a Cursor object representing iterations over the matching documents
            (see the Cursor object for how to use it. The simplest thing is to
            run a for loop: `for document in collection.sort(...):`).

        Examples:
            >>> filter = {"seq": {"$exists": True}}
            >>> for doc in my_coll.find(filter, projection={"seq": True}, limit=5):
            ...     print(doc["seq"])
            ...
            37
            35
            10
            36
            27
            >>> cursor1 = my_coll.find(
            ...     {},
            ...     limit=4,
            ...     sort={"seq": astrapy.constants.SortDocuments.DESCENDING},
            ... )
            >>> [doc["_id"] for doc in cursor1]
            ['97e85f81-...', '1581efe4-...', '...', '...']
            >>> cursor2 = my_coll.find({}, limit=3)
            >>> cursor2.distinct("seq")
            [37, 35, 10]

            >>> my_coll.insert_many([
            ...     {"tag": "A", "$vector": [4, 5]},
            ...     {"tag": "B", "$vector": [3, 4]},
            ...     {"tag": "C", "$vector": [3, 2]},
            ...     {"tag": "D", "$vector": [4, 1]},
            ...     {"tag": "E", "$vector": [2, 5]},
            ... ])
            >>> ann_tags = [
            ...     document["tag"]
            ...     for document in my_coll.find(
            ...         {},
            ...         limit=3,
            ...         vector=[3, 3],
            ...     )
            ... ]
            >>> ann_tags
            ['A', 'B', 'C']
            # (assuming the collection has metric VectorMetric.COSINE)

        Note:
            The following are example values for the `sort` parameter.
            When no particular order is required:
                sort={}  # (default when parameter not provided)
            When sorting by a certain value in ascending/descending order:
                sort={"field": SortDocuments.ASCENDING}
                sort={"field": SortDocuments.DESCENDING}
            When sorting first by "field" and then by "subfield"
            (while modern Python versions preserve the order of dictionaries,
            it is suggested for clarity to employ a `collections.OrderedDict`
            in these cases):
                sort={
                    "field": SortDocuments.ASCENDING,
                    "subfield": SortDocuments.ASCENDING,
                }
            When running a vector similarity (ANN) search:
                sort={"$vector": [0.4, 0.15, -0.5]}

        Note:
            Some combinations of arguments impose an implicit upper bound on the
            number of documents that are returned by the Data API. More specifically:
            (a) Vector ANN searches cannot return more than a number of documents
            that at the time of writing is set to 1000 items.
            (b) When using a sort criterion of the ascending/descending type,
            the Data API will return a smaller number of documents, set to 20
            at the time of writing, and stop there. The returned documents are
            the top results across the whole collection according to the requested
            criterion.
            These provisions should be kept in mind even when subsequently running
            a command such as `.distinct()` on a cursor.

        Note:
            When not specifying sorting criteria at all (by vector or otherwise),
            the cursor can scroll through an arbitrary number of documents as
            the Data API and the client periodically exchange new chunks of documents.
            It should be noted that the behavior of the cursor in the case documents
            have been added/removed after the `find` was started depends on database
            internals and it is not guaranteed, nor excluded, that such "real-time"
            changes in the data would be picked up by the cursor.
        """

        _sort = _collate_vector_to_sort(sort, vector, vectorize)
        if include_similarity is not None and not _is_vector_sort(_sort):
            raise ValueError(
                "Cannot use `include_similarity` when not searching through `vector`."
            )
        return (
            Cursor(
                collection=self,
                filter=filter,
                projection=projection,
                max_time_ms=max_time_ms,
                overall_max_time_ms=None,
            )
            .skip(skip)
            .limit(limit)
            .sort(_sort)
            .include_similarity(include_similarity)
        )

    def find_one(
        self,
        filter: Optional[FilterType] = None,
        *,
        projection: Optional[ProjectionType] = None,
        vector: Optional[VectorType] = None,
        vectorize: Optional[str] = None,
        include_similarity: Optional[bool] = None,
        sort: Optional[SortType] = None,
        max_time_ms: Optional[int] = None,
    ) -> Union[DocumentType, None]:
        """
        Run a search, returning the first document in the collection that matches
        provided filters, if any is found.

        Args:
            filter: a predicate expressed as a dictionary according to the
                Data API filter syntax. Examples are:
                    {}
                    {"name": "John"}
                    {"price": {"$le": 100}}
                    {"$and": [{"name": "John"}, {"price": {"$le": 100}}]}
                See the Data API documentation for the full set of operators.
            projection: used to select a subset of fields in the documents being
                returned. The projection can be: an iterable over the field names
                to return; a dictionary {field_name: True} to positively select
                certain fields; or a dictionary {field_name: False} if one wants
                to discard some fields from the response.
                The default is to return the whole documents.
            vector: a suitable vector, i.e. a list of float numbers of the appropriate
                dimensionality, to perform vector search (i.e. ANN,
                or "approximate nearest-neighbours" search), extracting the most
                similar document in the collection matching the filter.
                This parameter cannot be used together with `sort`.
                See the `find` method for more details on this parameter.
            vectorize: a string to be made into a vector to perform vector search.
                This can be supplied in (exclusive) alternative to `vector`,
                provided such a service is configured for the collection,
                and achieves the same effect.
                NOTE: This feature is under current development.
            include_similarity: a boolean to request the numeric value of the
                similarity to be returned as an added "$similarity" key in the
                returned document. Can only be used for vector ANN search, i.e.
                when either `vector` is supplied or the `sort` parameter has the
                shape {"$vector": ...}.
            sort: with this dictionary parameter one can control the order
                the documents are returned. See the Note about sorting for details.
            max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

        Returns:
            a dictionary expressing the required document, otherwise None.

        Examples:
            >>> my_coll.find_one({})
            {'_id': '68d1e515-...', 'seq': 37}
            >>> my_coll.find_one({"seq": 10})
            {'_id': 'd560e217-...', 'seq': 10}
            >>> my_coll.find_one({"seq": 1011})
            >>> # (returns None for no matches)
            >>> my_coll.find_one({}, projection={"seq": False})
            {'_id': '68d1e515-...'}
            >>> my_coll.find_one(
            ...     {},
            ...     sort={"seq": astrapy.constants.SortDocuments.DESCENDING},
            ... )
            {'_id': '97e85f81-...', 'seq': 69}
            >>> my_coll.find_one({}, vector=[1, 0])
            {'_id': '...', 'tag': 'D', '$vector': [4.0, 1.0]}

        Note:
            See the `find` method for more details on the accepted parameters
            (whereas `skip` and `limit` are not valid parameters for `find_one`).
        """

        fo_cursor = self.find(
            filter=filter,
            projection=projection,
            skip=None,
            limit=1,
            vector=vector,
            vectorize=vectorize,
            include_similarity=include_similarity,
            sort=sort,
            max_time_ms=max_time_ms,
        )
        try:
            document = fo_cursor.__next__()
            return document  # type: ignore[no-any-return]
        except StopIteration:
            return None

    def distinct(
        self,
        key: str,
        *,
        filter: Optional[FilterType] = None,
        max_time_ms: Optional[int] = None,
    ) -> List[Any]:
        """
        Return a list of the unique values of `key` across the documents
        in the collection that match the provided filter.

        Args:
            key: the name of the field whose value is inspected across documents.
                Keys can use dot-notation to descend to deeper document levels.
                Example of acceptable `key` values:
                    "field"
                    "field.subfield"
                    "field.3"
                    "field.3.subfield"
                If lists are encountered and no numeric index is specified,
                all items in the list are visited.
            filter: a predicate expressed as a dictionary according to the
                Data API filter syntax. Examples are:
                    {}
                    {"name": "John"}
                    {"price": {"$le": 100}}
                    {"$and": [{"name": "John"}, {"price": {"$le": 100}}]}
                See the Data API documentation for the full set of operators.
            max_time_ms: a timeout, in milliseconds, for the operation.

        Returns:
            a list of all different values for `key` found across the documents
            that match the filter. The result list has no repeated items.

        Example:
            >>> my_coll.insert_many(
            ...     [
            ...         {"name": "Marco", "food": ["apple", "orange"], "city": "Helsinki"},
            ...         {"name": "Emma", "food": {"likes_fruit": True, "allergies": []}},
            ...     ]
            ... )
            InsertManyResult(raw_results=..., inserted_ids=['c5b99f37-...', 'd6416321-...'])
            >>> my_coll.distinct("name")
            ['Marco', 'Emma']
            >>> my_coll.distinct("city")
            ['Helsinki']
            >>> my_coll.distinct("food")
            ['apple', 'orange', {'likes_fruit': True, 'allergies': []}]
            >>> my_coll.distinct("food.1")
            ['orange']
            >>> my_coll.distinct("food.allergies")
            []
            >>> my_coll.distinct("food.likes_fruit")
            [True]

        Note:
            It must be kept in mind that `distinct` is a client-side operation,
            which effectively browses all required documents using the logic
            of the `find` method and collects the unique values found for `key`.
            As such, there may be performance, latency and ultimately
            billing implications if the amount of matching documents is large.

        Note:
            For details on the behaviour of "distinct" in conjunction with
            real-time changes in the collection contents, see the
            Note of the `find` command.
        """

        f_cursor = Cursor(
            collection=self,
            filter=filter,
            projection={key: True},
            max_time_ms=None,
            overall_max_time_ms=max_time_ms,
        )
        return f_cursor.distinct(key)  # type: ignore[no-any-return]

    @recast_method_sync
    def count_documents(
        self,
        filter: Dict[str, Any],
        *,
        upper_bound: int,
        max_time_ms: Optional[int] = None,
    ) -> int:
        """
        Count the documents in the collection matching the specified filter.

        Args:
            filter: a predicate expressed as a dictionary according to the
                Data API filter syntax. Examples are:
                    {}
                    {"name": "John"}
                    {"price": {"$le": 100}}
                    {"$and": [{"name": "John"}, {"price": {"$le": 100}}]}
                See the Data API documentation for the full set of operators.
            upper_bound: a required ceiling on the result of the count operation.
                If the actual number of documents exceeds this value,
                an exception will be raised.
                Furthermore, if the actual number of documents exceeds the maximum
                count that the Data API can reach (regardless of upper_bound),
                an exception will be raised.
            max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

        Returns:
            the exact count of matching documents.

        Example:
            >>> my_coll.insert_many([{"seq": i} for i in range(20)])
            InsertManyResult(...)
            >>> my_coll.count_documents({}, upper_bound=100)
            20
            >>> my_coll.count_documents({"seq":{"$gt": 15}}, upper_bound=100)
            4
            >>> my_coll.count_documents({}, upper_bound=10)
            Traceback (most recent call last):
                ... ...
            astrapy.exceptions.TooManyDocumentsToCountException

        Note:
            Count operations are expensive: for this reason, the best practice
            is to provide a reasonable `upper_bound` according to the caller
            expectations. Moreover, indiscriminate usage of count operations
            for sizeable amounts of documents (i.e. in the thousands and more)
            is discouraged in favor of alternative application-specific solutions.
            Keep in mind that the Data API has a hard upper limit on the amount
            of documents it will count, and that an exception will be thrown
            by this method if this limit is encountered.
        """

        logger.info("calling count_documents")
        cd_response = self._astra_db_collection.count_documents(
            filter=filter,
            timeout_info=base_timeout_info(max_time_ms),
        )
        logger.info("finished calling count_documents")
        if "count" in cd_response.get("status", {}):
            count: int = cd_response["status"]["count"]
            if cd_response["status"].get("moreData", False):
                raise TooManyDocumentsToCountException(
                    text=f"Document count exceeds {count}, the maximum allowed by the server",
                    server_max_count_exceeded=True,
                )
            else:
                if count > upper_bound:
                    raise TooManyDocumentsToCountException(
                        text="Document count exceeds required upper bound",
                        server_max_count_exceeded=False,
                    )
                else:
                    return count
        else:
            raise DataAPIFaultyResponseException(
                text="Faulty response from count_documents API command.",
                raw_response=cd_response,
            )

    @recast_method_sync
    def find_one_and_replace(
        self,
        filter: Dict[str, Any],
        replacement: DocumentType,
        *,
        projection: Optional[ProjectionType] = None,
        vector: Optional[VectorType] = None,
        vectorize: Optional[str] = None,
        sort: Optional[SortType] = None,
        upsert: bool = False,
        return_document: str = ReturnDocument.BEFORE,
        max_time_ms: Optional[int] = None,
    ) -> Union[DocumentType, None]:
        """
        Find a document on the collection and replace it entirely with a new one,
        optionally inserting a new one if no match is found.

        Args:
            filter: a predicate expressed as a dictionary according to the
                Data API filter syntax. Examples are:
                    {}
                    {"name": "John"}
                    {"price": {"$le": 100}}
                    {"$and": [{"name": "John"}, {"price": {"$le": 100}}]}
                See the Data API documentation for the full set of operators.
            replacement: the new document to write into the collection.
            projection: used to select a subset of fields in the document being
                returned. The projection can be: an iterable over the field names
                to return; a dictionary {field_name: True} to positively select
                certain fields; or a dictionary {field_name: False} if one wants
                to discard some fields from the response.
                The default is to return the whole documents.
            vector: a suitable vector, i.e. a list of float numbers of the appropriate
                dimensionality, to use vector search (i.e. ANN,
                or "approximate nearest-neighbours" search), as the sorting criterion.
                In this way, the matched document (if any) will be the one
                that is most similar to the provided vector.
                This parameter cannot be used together with `sort`.
                See the `find` method for more details on this parameter.
            vectorize: a string to be made into a vector to perform vector search.
                This can be supplied in (exclusive) alternative to `vector`,
                provided such a service is configured for the collection,
                and achieves the same effect.
                NOTE: This feature is under current development.
            sort: with this dictionary parameter one can control the sorting
                order of the documents matching the filter, effectively
                determining what document will come first and hence be the
                replaced one. See the `find` method for more on sorting.
            upsert: this parameter controls the behavior in absence of matches.
                If True, `replacement` is inserted as a new document
                if no matches are found on the collection. If False,
                the operation silently does nothing in case of no matches.
            return_document: a flag controlling what document is returned:
                if set to `ReturnDocument.BEFORE`, or the string "before",
                the document found on database is returned; if set to
                `ReturnDocument.AFTER`, or the string "after", the new
                document is returned. The default is "before".
            max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

        Returns:
            A document (or a projection thereof, as required), either the one
            before the replace operation or the one after that.
            Alternatively, the method returns None to represent
            that no matching document was found, or that no replacement
            was inserted (depending on the `return_document` parameter).

        Example:
            >>> my_coll.insert_one({"_id": "rule1", "text": "all animals are equal"})
            InsertOneResult(...)
            >>> my_coll.find_one_and_replace(
            ...     {"_id": "rule1"},
            ...     {"text": "some animals are more equal!"},
            ... )
            {'_id': 'rule1', 'text': 'all animals are equal'}
            >>> my_coll.find_one_and_replace(
            ...     {"text": "some animals are more equal!"},
            ...     {"text": "and the pigs are the rulers"},
            ...     return_document=astrapy.constants.ReturnDocument.AFTER,
            ... )
            {'_id': 'rule1', 'text': 'and the pigs are the rulers'}
            >>> my_coll.find_one_and_replace(
            ...     {"_id": "rule2"},
            ...     {"text": "F=ma^2"},
            ...     return_document=astrapy.constants.ReturnDocument.AFTER,
            ... )
            >>> # (returns None for no matches)
            >>> my_coll.find_one_and_replace(
            ...     {"_id": "rule2"},
            ...     {"text": "F=ma"},
            ...     upsert=True,
            ...     return_document=astrapy.constants.ReturnDocument.AFTER,
            ...     projection={"_id": False},
            ... )
            {'text': 'F=ma'}
        """

        _sort = _collate_vector_to_sort(sort, vector, vectorize)
        options = {
            "returnDocument": return_document,
            "upsert": upsert,
        }
        logger.info(f"calling find_one_and_replace on '{self.name}'")
        fo_response = self._astra_db_collection.find_one_and_replace(
            replacement=replacement,
            filter=filter,
            projection=normalize_optional_projection(projection),
            sort=_sort,
            options=options,
            timeout_info=base_timeout_info(max_time_ms),
        )
        logger.info(f"finished calling find_one_and_replace on '{self.name}'")
        if "document" in fo_response.get("data", {}):
            ret_document = fo_response.get("data", {}).get("document")
            if ret_document is None:
                return None
            else:
                return ret_document  # type: ignore[no-any-return]
        else:
            raise DataAPIFaultyResponseException(
                text="Faulty response from find_one_and_replace API command.",
                raw_response=fo_response,
            )

    @recast_method_sync
    def replace_one(
        self,
        filter: Dict[str, Any],
        replacement: DocumentType,
        *,
        vector: Optional[VectorType] = None,
        vectorize: Optional[str] = None,
        sort: Optional[SortType] = None,
        upsert: bool = False,
        max_time_ms: Optional[int] = None,
    ) -> UpdateResult:
        """
        Replace a single document on the collection with a new one,
        optionally inserting a new one if no match is found.

        Args:
            filter: a predicate expressed as a dictionary according to the
                Data API filter syntax. Examples are:
                    {}
                    {"name": "John"}
                    {"price": {"$le": 100}}
                    {"$and": [{"name": "John"}, {"price": {"$le": 100}}]}
                See the Data API documentation for the full set of operators.
            replacement: the new document to write into the collection.
            vector: a suitable vector, i.e. a list of float numbers of the appropriate
                dimensionality, to use vector search (i.e. ANN,
                or "approximate nearest-neighbours" search), as the sorting criterion.
                In this way, the matched document (if any) will be the one
                that is most similar to the provided vector.
                This parameter cannot be used together with `sort`.
                See the `find` method for more details on this parameter.
            vectorize: a string to be made into a vector to perform vector search.
                This can be supplied in (exclusive) alternative to `vector`,
                provided such a service is configured for the collection,
                and achieves the same effect.
                NOTE: This feature is under current development.
            sort: with this dictionary parameter one can control the sorting
                order of the documents matching the filter, effectively
                determining what document will come first and hence be the
                replaced one. See the `find` method for more on sorting.
            upsert: this parameter controls the behavior in absence of matches.
                If True, `replacement` is inserted as a new document
                if no matches are found on the collection. If False,
                the operation silently does nothing in case of no matches.
            max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

        Returns:
            an UpdateResult object summarizing the outcome of the replace operation.

        Example:
            >>> my_coll.insert_one({"Marco": "Polo"})
            InsertOneResult(...)
            >>> my_coll.replace_one({"Marco": {"$exists": True}}, {"Buda": "Pest"})
            UpdateResult(raw_results=..., update_info={'n': 1, 'updatedExisting': True, 'ok': 1.0, 'nModified': 1})
            >>> my_coll.find_one({"Buda": "Pest"})
            {'_id': '8424905a-...', 'Buda': 'Pest'}
            >>> my_coll.replace_one({"Mirco": {"$exists": True}}, {"Oh": "yeah?"})
            UpdateResult(raw_results=..., update_info={'n': 0, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0})
            >>> my_coll.replace_one({"Mirco": {"$exists": True}}, {"Oh": "yeah?"}, upsert=True)
            UpdateResult(raw_results=..., update_info={'n': 1, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0, 'upserted': '931b47d6-...'})
        """

        _sort = _collate_vector_to_sort(sort, vector, vectorize)
        options = {
            "upsert": upsert,
        }
        logger.info(f"calling find_one_and_replace on '{self.name}'")
        fo_response = self._astra_db_collection.find_one_and_replace(
            replacement=replacement,
            filter=filter,
            sort=_sort,
            options=options,
            timeout_info=base_timeout_info(max_time_ms),
        )
        logger.info(f"finished calling find_one_and_replace on '{self.name}'")
        if "document" in fo_response.get("data", {}):
            fo_status = fo_response.get("status") or {}
            _update_info = _prepare_update_info([fo_status])
            return UpdateResult(
                raw_results=[fo_response],
                update_info=_update_info,
            )
        else:
            raise DataAPIFaultyResponseException(
                text="Faulty response from find_one_and_replace API command.",
                raw_response=fo_response,
            )

    @recast_method_sync
    def find_one_and_update(
        self,
        filter: Dict[str, Any],
        update: Dict[str, Any],
        *,
        projection: Optional[ProjectionType] = None,
        vector: Optional[VectorType] = None,
        vectorize: Optional[str] = None,
        sort: Optional[SortType] = None,
        upsert: bool = False,
        return_document: str = ReturnDocument.BEFORE,
        max_time_ms: Optional[int] = None,
    ) -> Union[DocumentType, None]:
        """
        Find a document on the collection and update it as requested,
        optionally inserting a new one if no match is found.

        Args:
            filter: a predicate expressed as a dictionary according to the
                Data API filter syntax. Examples are:
                    {}
                    {"name": "John"}
                    {"price": {"$le": 100}}
                    {"$and": [{"name": "John"}, {"price": {"$le": 100}}]}
                See the Data API documentation for the full set of operators.
            update: the update prescription to apply to the document, expressed
                as a dictionary as per Data API syntax. Examples are:
                    {"$set": {"field": "value}}
                    {"$inc": {"counter": 10}}
                    {"$unset": {"field": ""}}
                See the Data API documentation for the full syntax.
            projection: used to select a subset of fields in the document being
                returned. The projection can be: an iterable over the field names
                to return; a dictionary {field_name: True} to positively select
                certain fields; or a dictionary {field_name: False} if one wants
                to discard some fields from the response.
                The default is to return the whole documents.
            vector: a suitable vector, i.e. a list of float numbers of the appropriate
                dimensionality, to use vector search (i.e. ANN,
                or "approximate nearest-neighbours" search), as the sorting criterion.
                In this way, the matched document (if any) will be the one
                that is most similar to the provided vector.
                This parameter cannot be used together with `sort`.
                See the `find` method for more details on this parameter.
            vectorize: a string to be made into a vector to perform vector search.
                This can be supplied in (exclusive) alternative to `vector`,
                provided such a service is configured for the collection,
                and achieves the same effect.
                NOTE: This feature is under current development.
            sort: with this dictionary parameter one can control the sorting
                order of the documents matching the filter, effectively
                determining what document will come first and hence be the
                updated one. See the `find` method for more on sorting.
            upsert: this parameter controls the behavior in absence of matches.
                If True, a new document (resulting from applying the `update`
                to an empty document) is inserted if no matches are found on
                the collection. If False, the operation silently does nothing
                in case of no matches.
            return_document: a flag controlling what document is returned:
                if set to `ReturnDocument.BEFORE`, or the string "before",
                the document found on database is returned; if set to
                `ReturnDocument.AFTER`, or the string "after", the new
                document is returned. The default is "before".
            max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

        Returns:
            A document (or a projection thereof, as required), either the one
            before the replace operation or the one after that.
            Alternatively, the method returns None to represent
            that no matching document was found, or that no update
            was applied (depending on the `return_document` parameter).

        Example:
            >>> my_coll.insert_one({"Marco": "Polo"})
            InsertOneResult(...)
            >>> my_coll.find_one_and_update(
            ...     {"Marco": {"$exists": True}},
            ...     {"$set": {"title": "Mr."}},
            ... )
            {'_id': 'a80106f2-...', 'Marco': 'Polo'}
            >>> my_coll.find_one_and_update(
            ...     {"title": "Mr."},
            ...     {"$inc": {"rank": 3}},
            ...     projection=["title", "rank"],
            ...     return_document=astrapy.constants.ReturnDocument.AFTER,
            ... )
            {'_id': 'a80106f2-...', 'title': 'Mr.', 'rank': 3}
            >>> my_coll.find_one_and_update(
            ...     {"name": "Johnny"},
            ...     {"$set": {"rank": 0}},
            ...     return_document=astrapy.constants.ReturnDocument.AFTER,
            ... )
            >>> # (returns None for no matches)
            >>> my_coll.find_one_and_update(
            ...     {"name": "Johnny"},
            ...     {"$set": {"rank": 0}},
            ...     upsert=True,
            ...     return_document=astrapy.constants.ReturnDocument.AFTER,
            ... )
            {'_id': 'cb4ef2ab-...', 'name': 'Johnny', 'rank': 0}
        """

        _sort = _collate_vector_to_sort(sort, vector, vectorize)
        options = {
            "returnDocument": return_document,
            "upsert": upsert,
        }
        logger.info(f"calling find_one_and_update on '{self.name}'")
        fo_response = self._astra_db_collection.find_one_and_update(
            update=update,
            filter=filter,
            projection=normalize_optional_projection(projection),
            sort=_sort,
            options=options,
            timeout_info=base_timeout_info(max_time_ms),
        )
        logger.info(f"finished calling find_one_and_update on '{self.name}'")
        if "document" in fo_response.get("data", {}):
            ret_document = fo_response.get("data", {}).get("document")
            if ret_document is None:
                return None
            else:
                return ret_document  # type: ignore[no-any-return]
        else:
            raise DataAPIFaultyResponseException(
                text="Faulty response from find_one_and_update API command.",
                raw_response=fo_response,
            )

    @recast_method_sync
    def update_one(
        self,
        filter: Dict[str, Any],
        update: Dict[str, Any],
        *,
        vector: Optional[VectorType] = None,
        vectorize: Optional[str] = None,
        sort: Optional[SortType] = None,
        upsert: bool = False,
        max_time_ms: Optional[int] = None,
    ) -> UpdateResult:
        """
        Update a single document on the collection as requested,
        optionally inserting a new one if no match is found.

        Args:
            filter: a predicate expressed as a dictionary according to the
                Data API filter syntax. Examples are:
                    {}
                    {"name": "John"}
                    {"price": {"$le": 100}}
                    {"$and": [{"name": "John"}, {"price": {"$le": 100}}]}
                See the Data API documentation for the full set of operators.
            update: the update prescription to apply to the document, expressed
                as a dictionary as per Data API syntax. Examples are:
                    {"$set": {"field": "value}}
                    {"$inc": {"counter": 10}}
                    {"$unset": {"field": ""}}
                See the Data API documentation for the full syntax.
            vector: a suitable vector, i.e. a list of float numbers of the appropriate
                dimensionality, to use vector search (i.e. ANN,
                or "approximate nearest-neighbours" search), as the sorting criterion.
                In this way, the matched document (if any) will be the one
                that is most similar to the provided vector.
                This parameter cannot be used together with `sort`.
                See the `find` method for more details on this parameter.
            vectorize: a string to be made into a vector to perform vector search.
                This can be supplied in (exclusive) alternative to `vector`,
                provided such a service is configured for the collection,
                and achieves the same effect.
                NOTE: This feature is under current development.
            sort: with this dictionary parameter one can control the sorting
                order of the documents matching the filter, effectively
                determining what document will come first and hence be the
                updated one. See the `find` method for more on sorting.
            upsert: this parameter controls the behavior in absence of matches.
                If True, a new document (resulting from applying the `update`
                to an empty document) is inserted if no matches are found on
                the collection. If False, the operation silently does nothing
                in case of no matches.
            max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

        Returns:
            an UpdateResult object summarizing the outcome of the update operation.

        Example:
            >>> my_coll.insert_one({"Marco": "Polo"})
            InsertOneResult(...)
            >>> my_coll.update_one({"Marco": {"$exists": True}}, {"$inc": {"rank": 3}})
            UpdateResult(raw_results=..., update_info={'n': 1, 'updatedExisting': True, 'ok': 1.0, 'nModified': 1})
            >>> my_coll.update_one({"Mirko": {"$exists": True}}, {"$inc": {"rank": 3}})
            UpdateResult(raw_results=..., update_info={'n': 0, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0})
            >>> my_coll.update_one({"Mirko": {"$exists": True}}, {"$inc": {"rank": 3}}, upsert=True)
            UpdateResult(raw_results=..., update_info={'n': 1, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0, 'upserted': '2a45ff60-...'})
        """

        _sort = _collate_vector_to_sort(sort, vector, vectorize)
        options = {
            "upsert": upsert,
        }
        logger.info(f"calling find_one_and_update on '{self.name}'")
        fo_response = self._astra_db_collection.find_one_and_update(
            update=update,
            sort=_sort,
            filter=filter,
            options=options,
            timeout_info=base_timeout_info(max_time_ms),
        )
        logger.info(f"finished calling find_one_and_update on '{self.name}'")
        if "document" in fo_response.get("data", {}):
            fo_status = fo_response.get("status") or {}
            _update_info = _prepare_update_info([fo_status])
            return UpdateResult(
                raw_results=[fo_response],
                update_info=_update_info,
            )
        else:
            raise DataAPIFaultyResponseException(
                text="Faulty response from find_one_and_update API command.",
                raw_response=fo_response,
            )

    @recast_method_sync
    def update_many(
        self,
        filter: Dict[str, Any],
        update: Dict[str, Any],
        *,
        upsert: bool = False,
        max_time_ms: Optional[int] = None,
    ) -> UpdateResult:
        """
        Apply an update operations to all documents matching a condition,
        optionally inserting one documents in absence of matches.

        Args:
            filter: a predicate expressed as a dictionary according to the
                Data API filter syntax. Examples are:
                    {}
                    {"name": "John"}
                    {"price": {"$le": 100}}
                    {"$and": [{"name": "John"}, {"price": {"$le": 100}}]}
                See the Data API documentation for the full set of operators.
            update: the update prescription to apply to the documents, expressed
                as a dictionary as per Data API syntax. Examples are:
                    {"$set": {"field": "value}}
                    {"$inc": {"counter": 10}}
                    {"$unset": {"field": ""}}
                See the Data API documentation for the full syntax.
            upsert: this parameter controls the behavior in absence of matches.
                If True, a single new document (resulting from applying `update`
                to an empty document) is inserted if no matches are found on
                the collection. If False, the operation silently does nothing
                in case of no matches.
            max_time_ms: a timeout, in milliseconds, for the operation.

        Returns:
            an UpdateResult object summarizing the outcome of the update operation.

        Example:
            >>> my_coll.insert_many([{"c": "red"}, {"c": "green"}, {"c": "blue"}])
            InsertManyResult(...)
            >>> my_coll.update_many({"c": {"$ne": "green"}}, {"$set": {"nongreen": True}})
            UpdateResult(raw_results=..., update_info={'n': 2, 'updatedExisting': True, 'ok': 1.0, 'nModified': 2})
            >>> my_coll.update_many({"c": "orange"}, {"$set": {"is_also_fruit": True}})
            UpdateResult(raw_results=..., update_info={'n': 0, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0})
            >>> my_coll.update_many(
            ...     {"c": "orange"},
            ...     {"$set": {"is_also_fruit": True}},
            ...     upsert=True,
            ... )
            UpdateResult(raw_results=..., update_info={'n': 1, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0, 'upserted': '46643050-...'})

        Note:
            Similarly to the case of `find` (see its docstring for more details),
            running this command while, at the same time, another process is
            inserting new documents which match the filter of the `update_many`
            can result in an unpredictable fraction of these documents being updated.
            In other words, it cannot be easily predicted whether a given
            newly-inserted document will be picked up by the update_many command or not.
        """

        base_options = {
            "upsert": upsert,
        }
        page_state_options: Dict[str, str] = {}
        um_responses: List[Dict[str, Any]] = []
        um_statuses: List[Dict[str, Any]] = []
        must_proceed = True
        timeout_manager = MultiCallTimeoutManager(overall_max_time_ms=max_time_ms)
        logger.info(f"starting update_many on '{self.name}'")
        while must_proceed:
            options = {**base_options, **page_state_options}
            logger.info(f"calling update_many on '{self.name}'")
            this_um_response = self._astra_db_collection.update_many(
                update=update,
                filter=filter,
                options=options,
                timeout_info=timeout_manager.remaining_timeout_info(),
            )
            logger.info(f"finished calling update_many on '{self.name}'")
            this_um_status = this_um_response.get("status") or {}
            #
            # if errors, quit early
            if this_um_response.get("errors", []):
                partial_update_info = _prepare_update_info(um_statuses)
                partial_result = UpdateResult(
                    raw_results=um_responses,
                    update_info=partial_update_info,
                )
                all_um_responses = um_responses + [this_um_response]
                raise UpdateManyException.from_responses(
                    commands=[None for _ in all_um_responses],
                    raw_responses=all_um_responses,
                    partial_result=partial_result,
                )
            else:
                if "status" not in this_um_response:
                    raise DataAPIFaultyResponseException(
                        text="Faulty response from update_many API command.",
                        raw_response=this_um_response,
                    )
                um_responses.append(this_um_response)
                um_statuses.append(this_um_status)
                next_page_state = this_um_status.get("nextPageState")
                if next_page_state is not None:
                    must_proceed = True
                    page_state_options = {"pageState": next_page_state}
                else:
                    must_proceed = False
                    page_state_options = {}

        update_info = _prepare_update_info(um_statuses)
        logger.info(f"finished update_many on '{self.name}'")
        return UpdateResult(
            raw_results=um_responses,
            update_info=update_info,
        )

    @recast_method_sync
    def find_one_and_delete(
        self,
        filter: Dict[str, Any],
        *,
        projection: Optional[ProjectionType] = None,
        vector: Optional[VectorType] = None,
        vectorize: Optional[str] = None,
        sort: Optional[SortType] = None,
        max_time_ms: Optional[int] = None,
    ) -> Union[DocumentType, None]:
        """
        Find a document in the collection and delete it. The deleted document,
        however, is the return value of the method.

        Args:
            filter: a predicate expressed as a dictionary according to the
                Data API filter syntax. Examples are:
                    {}
                    {"name": "John"}
                    {"price": {"$le": 100}}
                    {"$and": [{"name": "John"}, {"price": {"$le": 100}}]}
                See the Data API documentation for the full set of operators.
            projection: used to select a subset of fields in the document being
                returned. The projection can be: an iterable over the field names
                to return; a dictionary {field_name: True} to positively select
                certain fields; or a dictionary {field_name: False} if one wants
                to discard some fields from the response.
                Note that the `_id` field will be returned with the document
                in any case, regardless of what the provided `projection` requires.
                The default is to return the whole documents.
            vector: a suitable vector, i.e. a list of float numbers of the appropriate
                dimensionality, to use vector search (i.e. ANN,
                or "approximate nearest-neighbours" search), as the sorting criterion.
                In this way, the matched document (if any) will be the one
                that is most similar to the provided vector.
                This parameter cannot be used together with `sort`.
                See the `find` method for more details on this parameter.
            vectorize: a string to be made into a vector to perform vector search.
                This can be supplied in (exclusive) alternative to `vector`,
                provided such a service is configured for the collection,
                and achieves the same effect.
                NOTE: This feature is under current development.
            sort: with this dictionary parameter one can control the sorting
                order of the documents matching the filter, effectively
                determining what document will come first and hence be the
                deleted one. See the `find` method for more on sorting.
            max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

        Returns:
            Either the document (or a projection thereof, as requested), or None
            if no matches were found in the first place.

        Example:
            >>> my_coll.insert_many(
            ...     [
            ...         {"species": "swan", "class": "Aves"},
            ...         {"species": "frog", "class": "Amphibia"},
            ...     ],
            ... )
            InsertManyResult(...)
            >>> my_coll.find_one_and_delete(
            ...     {"species": {"$ne": "frog"}},
            ...     projection=["species"],
            ... )
            {'_id': '5997fb48-...', 'species': 'swan'}
            >>> my_coll.find_one_and_delete({"species": {"$ne": "frog"}})
            >>> # (returns None for no matches)
        """

        _sort = _collate_vector_to_sort(sort, vector, vectorize)
        _projection = normalize_optional_projection(projection)
        logger.info(f"calling find_one_and_delete on '{self.name}'")
        fo_response = self._astra_db_collection.find_one_and_delete(
            sort=_sort,
            filter=filter,
            projection=_projection,
            timeout_info=base_timeout_info(max_time_ms),
        )
        logger.info(f"finished calling find_one_and_delete on '{self.name}'")
        if "document" in fo_response.get("data", {}):
            document = fo_response["data"]["document"]
            return document  # type: ignore[no-any-return]
        else:
            deleted_count = fo_response.get("status", {}).get("deletedCount")
            if deleted_count == 0:
                return None
            else:
                raise DataAPIFaultyResponseException(
                    text="Faulty response from find_one_and_delete API command.",
                    raw_response=fo_response,
                )

    @recast_method_sync
    def delete_one(
        self,
        filter: Dict[str, Any],
        *,
        vector: Optional[VectorType] = None,
        vectorize: Optional[str] = None,
        sort: Optional[SortType] = None,
        max_time_ms: Optional[int] = None,
    ) -> DeleteResult:
        """
        Delete one document matching a provided filter.
        This method never deletes more than a single document, regardless
        of the number of matches to the provided filters.

        Args:
            filter: a predicate expressed as a dictionary according to the
                Data API filter syntax. Examples are:
                    {}
                    {"name": "John"}
                    {"price": {"$le": 100}}
                    {"$and": [{"name": "John"}, {"price": {"$le": 100}}]}
                See the Data API documentation for the full set of operators.
            vector: a suitable vector, i.e. a list of float numbers of the appropriate
                dimensionality, to use vector search (i.e. ANN,
                or "approximate nearest-neighbours" search), as the sorting criterion.
                In this way, the matched document (if any) will be the one
                that is most similar to the provided vector.
                This parameter cannot be used together with `sort`.
                See the `find` method for more details on this parameter.
            vectorize: a string to be made into a vector to perform vector search.
                This can be supplied in (exclusive) alternative to `vector`,
                provided such a service is configured for the collection,
                and achieves the same effect.
                NOTE: This feature is under current development.
            sort: with this dictionary parameter one can control the sorting
                order of the documents matching the filter, effectively
                determining what document will come first and hence be the
                deleted one. See the `find` method for more on sorting.
            max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

        Returns:
            a DeleteResult object summarizing the outcome of the delete operation.

        Example:
            >>> my_coll.insert_many([{"seq": 1}, {"seq": 0}, {"seq": 2}])
            InsertManyResult(...)
            >>> my_coll.delete_one({"seq": 1})
            DeleteResult(raw_results=..., deleted_count=1)
            >>> my_coll.distinct("seq")
            [0, 2]
            >>> my_coll.delete_one(
            ...     {"seq": {"$exists": True}},
            ...     sort={"seq": astrapy.constants.SortDocuments.DESCENDING},
            ... )
            DeleteResult(raw_results=..., deleted_count=1)
            >>> my_coll.distinct("seq")
            [0]
            >>> my_coll.delete_one({"seq": 2})
            DeleteResult(raw_results=..., deleted_count=0)
        """

        _sort = _collate_vector_to_sort(sort, vector, vectorize)
        logger.info(f"calling delete_one_by_predicate on '{self.name}'")
        do_response = self._astra_db_collection.delete_one_by_predicate(
            filter=filter, timeout_info=base_timeout_info(max_time_ms), sort=_sort
        )
        logger.info(f"finished calling delete_one_by_predicate on '{self.name}'")
        if "deletedCount" in do_response.get("status", {}):
            deleted_count = do_response["status"]["deletedCount"]
            if deleted_count == -1:
                return DeleteResult(
                    deleted_count=None,
                    raw_results=[do_response],
                )
            else:
                # expected a non-negative integer:
                return DeleteResult(
                    deleted_count=deleted_count,
                    raw_results=[do_response],
                )
        else:
            raise DataAPIFaultyResponseException(
                text="Faulty response from delete_one API command.",
                raw_response=do_response,
            )

    @recast_method_sync
    def delete_many(
        self,
        filter: Dict[str, Any],
        *,
        max_time_ms: Optional[int] = None,
    ) -> DeleteResult:
        """
        Delete all documents matching a provided filter.

        Args:
            filter: a predicate expressed as a dictionary according to the
                Data API filter syntax. Examples are:
                    {}
                    {"name": "John"}
                    {"price": {"$le": 100}}
                    {"$and": [{"name": "John"}, {"price": {"$le": 100}}]}
                See the Data API documentation for the full set of operators.
                The `delete_many` method does not accept an empty filter: see
                `delete_all` to completely erase all contents of a collection
            max_time_ms: a timeout, in milliseconds, for the operation.

        Returns:
            a DeleteResult object summarizing the outcome of the delete operation.

        Example:
            >>> my_coll.insert_many([{"seq": 1}, {"seq": 0}, {"seq": 2}])
            InsertManyResult(...)
            >>> my_coll.delete_many({"seq": {"$lte": 1}})
            DeleteResult(raw_results=..., deleted_count=2)
            >>> my_coll.distinct("seq")
            [2]
            >>> my_coll.delete_many({"seq": {"$lte": 1}})
            DeleteResult(raw_results=..., deleted_count=0)

        Note:
            This operation is not atomic. Depending on the amount of matching
            documents, it can keep running (in a blocking way) for a macroscopic
            time. In that case, new documents that are meanwhile inserted
            (e.g. from another process/application) will be deleted during
            the execution of this method call until the collection is devoid
            of matches.
        """

        if not filter:
            raise ValueError(
                "The `filter` parameter to method `delete_many` cannot be "
                "empty. In order to completely clear the contents of a "
                "collection, please use the `delete_all` method."
            )

        dm_responses: List[Dict[str, Any]] = []
        deleted_count = 0
        must_proceed = True
        timeout_manager = MultiCallTimeoutManager(overall_max_time_ms=max_time_ms)
        logger.info(f"starting delete_many on '{self.name}'")
        while must_proceed:
            logger.info(f"calling delete_many on '{self.name}'")
            this_dm_response = self._astra_db_collection.delete_many(
                filter=filter,
                skip_error_check=True,
                timeout_info=timeout_manager.remaining_timeout_info(),
            )
            logger.info(f"finished calling delete_many on '{self.name}'")
            # if errors, quit early
            if this_dm_response.get("errors", []):
                partial_result = DeleteResult(
                    deleted_count=deleted_count,
                    raw_results=dm_responses,
                )
                all_dm_responses = dm_responses + [this_dm_response]
                raise DeleteManyException.from_responses(
                    commands=[None for _ in all_dm_responses],
                    raw_responses=all_dm_responses,
                    partial_result=partial_result,
                )
            else:
                this_dc = this_dm_response.get("status", {}).get("deletedCount")
                if this_dc is None or this_dc < 0:
                    raise DataAPIFaultyResponseException(
                        text="Faulty response from delete_many API command.",
                        raw_response=this_dm_response,
                    )
                dm_responses.append(this_dm_response)
                deleted_count += this_dc
                must_proceed = this_dm_response.get("status", {}).get("moreData", False)

        logger.info(f"finished delete_many on '{self.name}'")
        return DeleteResult(
            deleted_count=deleted_count,
            raw_results=dm_responses,
        )

    @recast_method_sync
    def delete_all(self, *, max_time_ms: Optional[int] = None) -> Dict[str, Any]:
        """
        Delete all documents in a collection.

        Args:
            max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

        Returns:
            a dictionary of the form {"ok": 1} to signal successful deletion.

        Example:
            >>> my_coll.distinct("seq")
            [2, 1, 0]
            >>> my_coll.count_documents({}, upper_bound=100)
            4
            >>> my_coll.delete_all()
            {'ok': 1}
            >>> my_coll.count_documents({}, upper_bound=100)
            0

        Note:
            Use with caution.
        """

        logger.info(f"calling unfiltered delete_many on '{self.name}'")
        dm_response = self._astra_db_collection.delete_many(
            filter={}, timeout_info=base_timeout_info(max_time_ms)
        )
        logger.info(f"finished calling unfiltered delete_many on '{self.name}'")
        deleted_count = dm_response["status"]["deletedCount"]
        if deleted_count == -1:
            return {"ok": 1}
        else:
            raise DataAPIFaultyResponseException(
                text="Faulty response from delete_many API command.",
                raw_response=dm_response,
            )

    def bulk_write(
        self,
        requests: Iterable[BaseOperation],
        *,
        ordered: bool = True,
        concurrency: Optional[int] = None,
        max_time_ms: Optional[int] = None,
    ) -> BulkWriteResult:
        """
        Execute an arbitrary amount of operations such as inserts, updates, deletes
        either sequentially or concurrently.

        This method does not execute atomically, i.e. individual operations are
        each performed in the same way as the corresponding collection method,
        and each one is a different and unrelated database mutation.

        Args:
            requests: an iterable over concrete subclasses of `BaseOperation`,
                such as `InsertMany` or `ReplaceOne`. Each such object
                represents an operation ready to be executed on a collection,
                and is instantiated by passing the same parameters as one
                would the corresponding collection method.
            ordered: whether to launch the `requests` one after the other or
                in arbitrary order, possibly in a concurrent fashion. For
                performance reasons, `ordered=False` should be preferred
                when compatible with the needs of the application flow.
            concurrency: maximum number of concurrent operations executing at
                a given time. It cannot be more than one for ordered bulk writes.
            max_time_ms: a timeout, in milliseconds, for the whole bulk write.
                Remember that, if the method call times out, then there's no
                guarantee about what portion of the bulk write has been received
                and successfully executed by the Data API.

        Returns:
            A single BulkWriteResult summarizing the whole list of requested
            operations. The keys in the map attributes of BulkWriteResult
            (when present) are the integer indices of the corresponding operation
            in the `requests` iterable.

        Example:
            >>> from astrapy.operations import InsertMany, ReplaceOne
            >>> op1 = InsertMany([{"a": 1}, {"a": 2}])
            >>> op2 = ReplaceOne({"z": 9}, replacement={"z": 9, "replaced": True}, upsert=True)
            >>> my_coll.bulk_write([op1, op2])
            BulkWriteResult(bulk_api_results={0: ..., 1: ...}, deleted_count=0, inserted_count=3, matched_count=0, modified_count=0, upserted_count=1, upserted_ids={1: '2addd676-...'})
            >>> my_coll.count_documents({}, upper_bound=100)
            3
            >>> my_coll.distinct("replaced")
            [True]
        """

        # lazy importing here against circular-import error
        from astrapy.operations import reduce_bulk_write_results

        if concurrency is None:
            if ordered:
                _concurrency = 1
            else:
                _concurrency = DEFAULT_BULK_WRITE_CONCURRENCY
        else:
            _concurrency = concurrency
        if _concurrency > 1 and ordered:
            raise ValueError("Cannot run ordered bulk_write concurrently.")
        timeout_manager = MultiCallTimeoutManager(overall_max_time_ms=max_time_ms)
        logger.info(f"startng a bulk write on '{self.name}'")
        if ordered:
            bulk_write_results: List[BulkWriteResult] = []
            for operation_i, operation in enumerate(requests):
                try:
                    this_bw_result = operation.execute(
                        self,
                        index_in_bulk_write=operation_i,
                        bulk_write_timeout_ms=timeout_manager.remaining_timeout_ms(),
                    )
                    bulk_write_results.append(this_bw_result)
                except CumulativeOperationException as exc:
                    partial_result = exc.partial_result
                    partial_bw_result = reduce_bulk_write_results(
                        bulk_write_results
                        + [
                            partial_result.to_bulk_write_result(
                                index_in_bulk_write=operation_i
                            )
                        ]
                    )
                    dar_exception = exc.data_api_response_exception()
                    raise BulkWriteException(
                        text=dar_exception.text,
                        error_descriptors=dar_exception.error_descriptors,
                        detailed_error_descriptors=dar_exception.detailed_error_descriptors,
                        partial_result=partial_bw_result,
                        exceptions=[dar_exception],
                    )
                except DataAPIResponseException as exc:
                    # the cumulative exceptions, with their
                    # partially-done-info, are handled above:
                    # here it's just one-shot d.a.r. exceptions
                    partial_bw_result = reduce_bulk_write_results(bulk_write_results)
                    dar_exception = exc.data_api_response_exception()
                    raise BulkWriteException(
                        text=dar_exception.text,
                        error_descriptors=dar_exception.error_descriptors,
                        detailed_error_descriptors=dar_exception.detailed_error_descriptors,
                        partial_result=partial_bw_result,
                        exceptions=[dar_exception],
                    )
            full_bw_result = reduce_bulk_write_results(bulk_write_results)
            logger.info(f"finished a bulk write on '{self.name}'")
            return full_bw_result
        else:

            def _execute_as_either(
                operation: BaseOperation, operation_i: int
            ) -> Tuple[Optional[BulkWriteResult], Optional[DataAPIResponseException]]:
                try:
                    ex_result = operation.execute(
                        self,
                        index_in_bulk_write=operation_i,
                        bulk_write_timeout_ms=timeout_manager.remaining_timeout_ms(),
                    )
                    return (ex_result, None)
                except DataAPIResponseException as exc:
                    return (None, exc)

            with ThreadPoolExecutor(max_workers=_concurrency) as executor:
                bulk_write_either_futures = [
                    executor.submit(
                        _execute_as_either,
                        operation,
                        operation_i,
                    )
                    for operation_i, operation in enumerate(requests)
                ]
                bulk_write_either_results = [
                    bulk_write_either_future.result()
                    for bulk_write_either_future in bulk_write_either_futures
                ]
                # regroup
                bulk_write_successes = [
                    bwr for bwr, _ in bulk_write_either_results if bwr
                ]
                bulk_write_failures = [
                    bwf for _, bwf in bulk_write_either_results if bwf
                ]
                if bulk_write_failures:
                    # extract and cumulate
                    partial_results_from_failures = [
                        failure.partial_result.to_bulk_write_result(
                            index_in_bulk_write=operation_i
                        )
                        for failure in bulk_write_failures
                        if isinstance(failure, CumulativeOperationException)
                    ]
                    partial_bw_result = reduce_bulk_write_results(
                        bulk_write_successes + partial_results_from_failures
                    )
                    # raise and recast the first exception
                    all_dar_exceptions = [
                        bw_failure.data_api_response_exception()
                        for bw_failure in bulk_write_failures
                    ]
                    dar_exception = all_dar_exceptions[0]
                    raise BulkWriteException(
                        text=dar_exception.text,
                        error_descriptors=dar_exception.error_descriptors,
                        detailed_error_descriptors=dar_exception.detailed_error_descriptors,
                        partial_result=partial_bw_result,
                        exceptions=all_dar_exceptions,
                    )
                else:
                    logger.info(f"finished a bulk write on '{self.name}'")
                    return reduce_bulk_write_results(bulk_write_successes)

    def drop(self, *, max_time_ms: Optional[int] = None) -> Dict[str, Any]:
        """
        Drop the collection, i.e. delete it from the database along with
        all the documents it contains.

        Args:
            max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.
                Remember there is not guarantee that a request that has
                timed out us not in fact honored.

        Returns:
            a dictionary of the form {"ok": 1} to signal successful deletion.

        Example:
            >>> my_coll.find_one({})
            {'_id': '...', 'a': 100}
            >>> my_coll.drop()
            {'ok': 1}
            >>> my_coll.find_one({})
            Traceback (most recent call last):
                ... ...
            astrapy.exceptions.DataAPIResponseException: Collection does not exist, collection name: my_collection

        Note:
            Use with caution.


        Note:
            Once the method succeeds, methods on this object can still be invoked:
            however, this hardly makes sense as the underlying actual collection
            is no more.
            It is responsibility of the developer to design a correct flow
            which avoids using a deceased collection any further.

        Note:
            Once the method succeeds, methods on this object can still be invoked:
            however, this hardly makes sense as the underlying actual collection
            is no more.
            It is responsibility of the developer to design a correct flow
            which avoids using a deceased collection any further.
        """

        logger.info(f"dropping collection '{self.name}' (self)")
        drop_result = self.database.drop_collection(self, max_time_ms=max_time_ms)
        logger.info(f"finished dropping collection '{self.name}' (self)")
        return drop_result  # type: ignore[no-any-return]

    def command(
        self,
        body: Dict[str, Any],
        *,
        max_time_ms: Optional[int] = None,
    ) -> Dict[str, Any]:
        """
        Send a POST request to the Data API for this collection with
        an arbitrary, caller-provided payload.

        Args:
            body: a JSON-serializable dictionary, the payload of the request.
            max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

        Returns:
            a dictionary with the response of the HTTP request.

        Example:
            >>> my_coll.command({"countDocuments": {}})
            {'status': {'count': 123}}
        """

        logger.info(f"calling command on '{self.name}'")
        command_result = self.database.command(
            body=body,
            namespace=self.namespace,
            collection_name=self.name,
            max_time_ms=max_time_ms,
        )
        logger.info(f"finished calling command on '{self.name}'")
        return command_result  # type: ignore[no-any-return]

Instance variables

var database : Database

a Database object, the database this collection belongs to.

Example

>>> my_coll.database.name
'the_application_database'

Expand source code

@property
def database(self) -> Database:
    """
    a Database object, the database this collection belongs to.

    Example:
        >>> my_coll.database.name
        'the_application_database'
    """

    return self._database

var full_name : str

The fully-qualified collection name within the database, in the form "namespace.collection_name".

Example

>>> my_coll.full_name
'default_keyspace.my_v_collection'

Expand source code

@property
def full_name(self) -> str:
    """
    The fully-qualified collection name within the database,
    in the form "namespace.collection_name".

    Example:
        >>> my_coll.full_name
        'default_keyspace.my_v_collection'
    """

    return f"{self.namespace}.{self.name}"

var name : str

The name of this collection.

Example

>>> my_coll.name
'my_v_collection'

Expand source code

@property
def name(self) -> str:
    """
    The name of this collection.

    Example:
        >>> my_coll.name
        'my_v_collection'
    """

    # type hint added as for some reason the typechecker gets lost
    return self._astra_db_collection.collection_name  # type: ignore[no-any-return, has-type]

var namespace : str

The namespace this collection is in.

Example

>>> my_coll.namespace
'default_keyspace'

Expand source code

@property
def namespace(self) -> str:
    """
    The namespace this collection is in.

    Example:
        >>> my_coll.namespace
        'default_keyspace'
    """

    return self.database.namespace

Methods

def bulk_write(self, requests: Iterable[BaseOperation], *, ordered: bool = True, concurrency: Optional[int] = None, max_time_ms: Optional[int] = None) ‑> BulkWriteResult

Execute an arbitrary amount of operations such as inserts, updates, deletes either sequentially or concurrently.

This method does not execute atomically, i.e. individual operations are each performed in the same way as the corresponding collection method, and each one is a different and unrelated database mutation.

Args

requests

an iterable over concrete subclasses of BaseOperation, such as InsertMany or ReplaceOne. Each such object represents an operation ready to be executed on a collection, and is instantiated by passing the same parameters as one would the corresponding collection method.

ordered

whether to launch the requests one after the other or in arbitrary order, possibly in a concurrent fashion. For performance reasons, ordered=False should be preferred when compatible with the needs of the application flow.

concurrency

maximum number of concurrent operations executing at a given time. It cannot be more than one for ordered bulk writes.

max_time_ms

a timeout, in milliseconds, for the whole bulk write. Remember that, if the method call times out, then there's no guarantee about what portion of the bulk write has been received and successfully executed by the Data API.

Returns

A single BulkWriteResult summarizing the whole list of requested operations. The keys in the map attributes of BulkWriteResult (when present) are the integer indices of the corresponding operation in the requests iterable.

Example

>>> from astrapy.operations import InsertMany, ReplaceOne
>>> op1 = InsertMany([{"a": 1}, {"a": 2}])
>>> op2 = ReplaceOne({"z": 9}, replacement={"z": 9, "replaced": True}, upsert=True)
>>> my_coll.bulk_write([op1, op2])
BulkWriteResult(bulk_api_results={0: ..., 1: ...}, deleted_count=0, inserted_count=3, matched_count=0, modified_count=0, upserted_count=1, upserted_ids={1: '2addd676-...'})
>>> my_coll.count_documents({}, upper_bound=100)
3
>>> my_coll.distinct("replaced")
[True]

Expand source code

def bulk_write(
    self,
    requests: Iterable[BaseOperation],
    *,
    ordered: bool = True,
    concurrency: Optional[int] = None,
    max_time_ms: Optional[int] = None,
) -> BulkWriteResult:
    """
    Execute an arbitrary amount of operations such as inserts, updates, deletes
    either sequentially or concurrently.

    This method does not execute atomically, i.e. individual operations are
    each performed in the same way as the corresponding collection method,
    and each one is a different and unrelated database mutation.

    Args:
        requests: an iterable over concrete subclasses of `BaseOperation`,
            such as `InsertMany` or `ReplaceOne`. Each such object
            represents an operation ready to be executed on a collection,
            and is instantiated by passing the same parameters as one
            would the corresponding collection method.
        ordered: whether to launch the `requests` one after the other or
            in arbitrary order, possibly in a concurrent fashion. For
            performance reasons, `ordered=False` should be preferred
            when compatible with the needs of the application flow.
        concurrency: maximum number of concurrent operations executing at
            a given time. It cannot be more than one for ordered bulk writes.
        max_time_ms: a timeout, in milliseconds, for the whole bulk write.
            Remember that, if the method call times out, then there's no
            guarantee about what portion of the bulk write has been received
            and successfully executed by the Data API.

    Returns:
        A single BulkWriteResult summarizing the whole list of requested
        operations. The keys in the map attributes of BulkWriteResult
        (when present) are the integer indices of the corresponding operation
        in the `requests` iterable.

    Example:
        >>> from astrapy.operations import InsertMany, ReplaceOne
        >>> op1 = InsertMany([{"a": 1}, {"a": 2}])
        >>> op2 = ReplaceOne({"z": 9}, replacement={"z": 9, "replaced": True}, upsert=True)
        >>> my_coll.bulk_write([op1, op2])
        BulkWriteResult(bulk_api_results={0: ..., 1: ...}, deleted_count=0, inserted_count=3, matched_count=0, modified_count=0, upserted_count=1, upserted_ids={1: '2addd676-...'})
        >>> my_coll.count_documents({}, upper_bound=100)
        3
        >>> my_coll.distinct("replaced")
        [True]
    """

    # lazy importing here against circular-import error
    from astrapy.operations import reduce_bulk_write_results

    if concurrency is None:
        if ordered:
            _concurrency = 1
        else:
            _concurrency = DEFAULT_BULK_WRITE_CONCURRENCY
    else:
        _concurrency = concurrency
    if _concurrency > 1 and ordered:
        raise ValueError("Cannot run ordered bulk_write concurrently.")
    timeout_manager = MultiCallTimeoutManager(overall_max_time_ms=max_time_ms)
    logger.info(f"startng a bulk write on '{self.name}'")
    if ordered:
        bulk_write_results: List[BulkWriteResult] = []
        for operation_i, operation in enumerate(requests):
            try:
                this_bw_result = operation.execute(
                    self,
                    index_in_bulk_write=operation_i,
                    bulk_write_timeout_ms=timeout_manager.remaining_timeout_ms(),
                )
                bulk_write_results.append(this_bw_result)
            except CumulativeOperationException as exc:
                partial_result = exc.partial_result
                partial_bw_result = reduce_bulk_write_results(
                    bulk_write_results
                    + [
                        partial_result.to_bulk_write_result(
                            index_in_bulk_write=operation_i
                        )
                    ]
                )
                dar_exception = exc.data_api_response_exception()
                raise BulkWriteException(
                    text=dar_exception.text,
                    error_descriptors=dar_exception.error_descriptors,
                    detailed_error_descriptors=dar_exception.detailed_error_descriptors,
                    partial_result=partial_bw_result,
                    exceptions=[dar_exception],
                )
            except DataAPIResponseException as exc:
                # the cumulative exceptions, with their
                # partially-done-info, are handled above:
                # here it's just one-shot d.a.r. exceptions
                partial_bw_result = reduce_bulk_write_results(bulk_write_results)
                dar_exception = exc.data_api_response_exception()
                raise BulkWriteException(
                    text=dar_exception.text,
                    error_descriptors=dar_exception.error_descriptors,
                    detailed_error_descriptors=dar_exception.detailed_error_descriptors,
                    partial_result=partial_bw_result,
                    exceptions=[dar_exception],
                )
        full_bw_result = reduce_bulk_write_results(bulk_write_results)
        logger.info(f"finished a bulk write on '{self.name}'")
        return full_bw_result
    else:

        def _execute_as_either(
            operation: BaseOperation, operation_i: int
        ) -> Tuple[Optional[BulkWriteResult], Optional[DataAPIResponseException]]:
            try:
                ex_result = operation.execute(
                    self,
                    index_in_bulk_write=operation_i,
                    bulk_write_timeout_ms=timeout_manager.remaining_timeout_ms(),
                )
                return (ex_result, None)
            except DataAPIResponseException as exc:
                return (None, exc)

        with ThreadPoolExecutor(max_workers=_concurrency) as executor:
            bulk_write_either_futures = [
                executor.submit(
                    _execute_as_either,
                    operation,
                    operation_i,
                )
                for operation_i, operation in enumerate(requests)
            ]
            bulk_write_either_results = [
                bulk_write_either_future.result()
                for bulk_write_either_future in bulk_write_either_futures
            ]
            # regroup
            bulk_write_successes = [
                bwr for bwr, _ in bulk_write_either_results if bwr
            ]
            bulk_write_failures = [
                bwf for _, bwf in bulk_write_either_results if bwf
            ]
            if bulk_write_failures:
                # extract and cumulate
                partial_results_from_failures = [
                    failure.partial_result.to_bulk_write_result(
                        index_in_bulk_write=operation_i
                    )
                    for failure in bulk_write_failures
                    if isinstance(failure, CumulativeOperationException)
                ]
                partial_bw_result = reduce_bulk_write_results(
                    bulk_write_successes + partial_results_from_failures
                )
                # raise and recast the first exception
                all_dar_exceptions = [
                    bw_failure.data_api_response_exception()
                    for bw_failure in bulk_write_failures
                ]
                dar_exception = all_dar_exceptions[0]
                raise BulkWriteException(
                    text=dar_exception.text,
                    error_descriptors=dar_exception.error_descriptors,
                    detailed_error_descriptors=dar_exception.detailed_error_descriptors,
                    partial_result=partial_bw_result,
                    exceptions=all_dar_exceptions,
                )
            else:
                logger.info(f"finished a bulk write on '{self.name}'")
                return reduce_bulk_write_results(bulk_write_successes)

def command(self, body: Dict[str, Any], *, max_time_ms: Optional[int] = None) ‑> Dict[str, Any]

Send a POST request to the Data API for this collection with an arbitrary, caller-provided payload.

Args

body

a JSON-serializable dictionary, the payload of the request.

max_time_ms

a timeout, in milliseconds, for the underlying HTTP request.

Returns

a dictionary with the response of the HTTP request.

Example

>>> my_coll.command({"countDocuments": {}})
{'status': {'count': 123}}

Expand source code

def command(
    self,
    body: Dict[str, Any],
    *,
    max_time_ms: Optional[int] = None,
) -> Dict[str, Any]:
    """
    Send a POST request to the Data API for this collection with
    an arbitrary, caller-provided payload.

    Args:
        body: a JSON-serializable dictionary, the payload of the request.
        max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

    Returns:
        a dictionary with the response of the HTTP request.

    Example:
        >>> my_coll.command({"countDocuments": {}})
        {'status': {'count': 123}}
    """

    logger.info(f"calling command on '{self.name}'")
    command_result = self.database.command(
        body=body,
        namespace=self.namespace,
        collection_name=self.name,
        max_time_ms=max_time_ms,
    )
    logger.info(f"finished calling command on '{self.name}'")
    return command_result  # type: ignore[no-any-return]

def count_documents(self, filter: Dict[str, Any], *, upper_bound: int, max_time_ms: Optional[int] = None) ‑> int

Count the documents in the collection matching the specified filter.

Args

filter

a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$le": 100}} {"$and": [{"name": "John"}, {"price": {"$le": 100}}]} See the Data API documentation for the full set of operators.

upper_bound

a required ceiling on the result of the count operation. If the actual number of documents exceeds this value, an exception will be raised. Furthermore, if the actual number of documents exceeds the maximum count that the Data API can reach (regardless of upper_bound), an exception will be raised.

max_time_ms

a timeout, in milliseconds, for the underlying HTTP request.

Returns

the exact count of matching documents.

Example

>>> my_coll.insert_many([{"seq": i} for i in range(20)])
InsertManyResult(...)
>>> my_coll.count_documents({}, upper_bound=100)
20
>>> my_coll.count_documents({"seq":{"$gt": 15}}, upper_bound=100)
4
>>> my_coll.count_documents({}, upper_bound=10)
Traceback (most recent call last):
    ... ...
astrapy.exceptions.TooManyDocumentsToCountException

Note

Count operations are expensive: for this reason, the best practice is to provide a reasonable upper_bound according to the caller expectations. Moreover, indiscriminate usage of count operations for sizeable amounts of documents (i.e. in the thousands and more) is discouraged in favor of alternative application-specific solutions. Keep in mind that the Data API has a hard upper limit on the amount of documents it will count, and that an exception will be thrown by this method if this limit is encountered.

Expand source code

@recast_method_sync
def count_documents(
    self,
    filter: Dict[str, Any],
    *,
    upper_bound: int,
    max_time_ms: Optional[int] = None,
) -> int:
    """
    Count the documents in the collection matching the specified filter.

    Args:
        filter: a predicate expressed as a dictionary according to the
            Data API filter syntax. Examples are:
                {}
                {"name": "John"}
                {"price": {"$le": 100}}
                {"$and": [{"name": "John"}, {"price": {"$le": 100}}]}
            See the Data API documentation for the full set of operators.
        upper_bound: a required ceiling on the result of the count operation.
            If the actual number of documents exceeds this value,
            an exception will be raised.
            Furthermore, if the actual number of documents exceeds the maximum
            count that the Data API can reach (regardless of upper_bound),
            an exception will be raised.
        max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

    Returns:
        the exact count of matching documents.

    Example:
        >>> my_coll.insert_many([{"seq": i} for i in range(20)])
        InsertManyResult(...)
        >>> my_coll.count_documents({}, upper_bound=100)
        20
        >>> my_coll.count_documents({"seq":{"$gt": 15}}, upper_bound=100)
        4
        >>> my_coll.count_documents({}, upper_bound=10)
        Traceback (most recent call last):
            ... ...
        astrapy.exceptions.TooManyDocumentsToCountException

    Note:
        Count operations are expensive: for this reason, the best practice
        is to provide a reasonable `upper_bound` according to the caller
        expectations. Moreover, indiscriminate usage of count operations
        for sizeable amounts of documents (i.e. in the thousands and more)
        is discouraged in favor of alternative application-specific solutions.
        Keep in mind that the Data API has a hard upper limit on the amount
        of documents it will count, and that an exception will be thrown
        by this method if this limit is encountered.
    """

    logger.info("calling count_documents")
    cd_response = self._astra_db_collection.count_documents(
        filter=filter,
        timeout_info=base_timeout_info(max_time_ms),
    )
    logger.info("finished calling count_documents")
    if "count" in cd_response.get("status", {}):
        count: int = cd_response["status"]["count"]
        if cd_response["status"].get("moreData", False):
            raise TooManyDocumentsToCountException(
                text=f"Document count exceeds {count}, the maximum allowed by the server",
                server_max_count_exceeded=True,
            )
        else:
            if count > upper_bound:
                raise TooManyDocumentsToCountException(
                    text="Document count exceeds required upper bound",
                    server_max_count_exceeded=False,
                )
            else:
                return count
    else:
        raise DataAPIFaultyResponseException(
            text="Faulty response from count_documents API command.",
            raw_response=cd_response,
        )

def delete_all(self, *, max_time_ms: Optional[int] = None) ‑> Dict[str, Any]

Delete all documents in a collection.

Args

max_time_ms

a timeout, in milliseconds, for the underlying HTTP request.

Returns

a dictionary of the form {"ok": 1} to signal successful deletion.

Example

>>> my_coll.distinct("seq")
[2, 1, 0]
>>> my_coll.count_documents({}, upper_bound=100)
4
>>> my_coll.delete_all()
{'ok': 1}
>>> my_coll.count_documents({}, upper_bound=100)
0

Note

Use with caution.

Expand source code

@recast_method_sync
def delete_all(self, *, max_time_ms: Optional[int] = None) -> Dict[str, Any]:
    """
    Delete all documents in a collection.

    Args:
        max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

    Returns:
        a dictionary of the form {"ok": 1} to signal successful deletion.

    Example:
        >>> my_coll.distinct("seq")
        [2, 1, 0]
        >>> my_coll.count_documents({}, upper_bound=100)
        4
        >>> my_coll.delete_all()
        {'ok': 1}
        >>> my_coll.count_documents({}, upper_bound=100)
        0

    Note:
        Use with caution.
    """

    logger.info(f"calling unfiltered delete_many on '{self.name}'")
    dm_response = self._astra_db_collection.delete_many(
        filter={}, timeout_info=base_timeout_info(max_time_ms)
    )
    logger.info(f"finished calling unfiltered delete_many on '{self.name}'")
    deleted_count = dm_response["status"]["deletedCount"]
    if deleted_count == -1:
        return {"ok": 1}
    else:
        raise DataAPIFaultyResponseException(
            text="Faulty response from delete_many API command.",
            raw_response=dm_response,
        )

def delete_many(self, filter: Dict[str, Any], *, max_time_ms: Optional[int] = None) ‑> DeleteResult

Delete all documents matching a provided filter.

Args

filter

a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$le": 100}} {"$and": [{"name": "John"}, {"price": {"$le": 100}}]} See the Data API documentation for the full set of operators. The delete_many method does not accept an empty filter: see delete_all to completely erase all contents of a collection

max_time_ms

a timeout, in milliseconds, for the operation.

Returns

a DeleteResult object summarizing the outcome of the delete operation.

Example

>>> my_coll.insert_many([{"seq": 1}, {"seq": 0}, {"seq": 2}])
InsertManyResult(...)
>>> my_coll.delete_many({"seq": {"$lte": 1}})
DeleteResult(raw_results=..., deleted_count=2)
>>> my_coll.distinct("seq")
[2]
>>> my_coll.delete_many({"seq": {"$lte": 1}})
DeleteResult(raw_results=..., deleted_count=0)

Note

This operation is not atomic. Depending on the amount of matching documents, it can keep running (in a blocking way) for a macroscopic time. In that case, new documents that are meanwhile inserted (e.g. from another process/application) will be deleted during the execution of this method call until the collection is devoid of matches.

Expand source code

@recast_method_sync
def delete_many(
    self,
    filter: Dict[str, Any],
    *,
    max_time_ms: Optional[int] = None,
) -> DeleteResult:
    """
    Delete all documents matching a provided filter.

    Args:
        filter: a predicate expressed as a dictionary according to the
            Data API filter syntax. Examples are:
                {}
                {"name": "John"}
                {"price": {"$le": 100}}
                {"$and": [{"name": "John"}, {"price": {"$le": 100}}]}
            See the Data API documentation for the full set of operators.
            The `delete_many` method does not accept an empty filter: see
            `delete_all` to completely erase all contents of a collection
        max_time_ms: a timeout, in milliseconds, for the operation.

    Returns:
        a DeleteResult object summarizing the outcome of the delete operation.

    Example:
        >>> my_coll.insert_many([{"seq": 1}, {"seq": 0}, {"seq": 2}])
        InsertManyResult(...)
        >>> my_coll.delete_many({"seq": {"$lte": 1}})
        DeleteResult(raw_results=..., deleted_count=2)
        >>> my_coll.distinct("seq")
        [2]
        >>> my_coll.delete_many({"seq": {"$lte": 1}})
        DeleteResult(raw_results=..., deleted_count=0)

    Note:
        This operation is not atomic. Depending on the amount of matching
        documents, it can keep running (in a blocking way) for a macroscopic
        time. In that case, new documents that are meanwhile inserted
        (e.g. from another process/application) will be deleted during
        the execution of this method call until the collection is devoid
        of matches.
    """

    if not filter:
        raise ValueError(
            "The `filter` parameter to method `delete_many` cannot be "
            "empty. In order to completely clear the contents of a "
            "collection, please use the `delete_all` method."
        )

    dm_responses: List[Dict[str, Any]] = []
    deleted_count = 0
    must_proceed = True
    timeout_manager = MultiCallTimeoutManager(overall_max_time_ms=max_time_ms)
    logger.info(f"starting delete_many on '{self.name}'")
    while must_proceed:
        logger.info(f"calling delete_many on '{self.name}'")
        this_dm_response = self._astra_db_collection.delete_many(
            filter=filter,
            skip_error_check=True,
            timeout_info=timeout_manager.remaining_timeout_info(),
        )
        logger.info(f"finished calling delete_many on '{self.name}'")
        # if errors, quit early
        if this_dm_response.get("errors", []):
            partial_result = DeleteResult(
                deleted_count=deleted_count,
                raw_results=dm_responses,
            )
            all_dm_responses = dm_responses + [this_dm_response]
            raise DeleteManyException.from_responses(
                commands=[None for _ in all_dm_responses],
                raw_responses=all_dm_responses,
                partial_result=partial_result,
            )
        else:
            this_dc = this_dm_response.get("status", {}).get("deletedCount")
            if this_dc is None or this_dc < 0:
                raise DataAPIFaultyResponseException(
                    text="Faulty response from delete_many API command.",
                    raw_response=this_dm_response,
                )
            dm_responses.append(this_dm_response)
            deleted_count += this_dc
            must_proceed = this_dm_response.get("status", {}).get("moreData", False)

    logger.info(f"finished delete_many on '{self.name}'")
    return DeleteResult(
        deleted_count=deleted_count,
        raw_results=dm_responses,
    )

def delete_one(self, filter: Dict[str, Any], *, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, sort: Optional[SortType] = None, max_time_ms: Optional[int] = None) ‑> DeleteResult

Delete one document matching a provided filter. This method never deletes more than a single document, regardless of the number of matches to the provided filters.

Args

filter

a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$le": 100}} {"$and": [{"name": "John"}, {"price": {"$le": 100}}]} See the Data API documentation for the full set of operators.

vector

a suitable vector, i.e. a list of float numbers of the appropriate dimensionality, to use vector search (i.e. ANN, or "approximate nearest-neighbours" search), as the sorting criterion. In this way, the matched document (if any) will be the one that is most similar to the provided vector. This parameter cannot be used together with sort. See the find method for more details on this parameter.

vectorize

a string to be made into a vector to perform vector search. This can be supplied in (exclusive) alternative to vector, provided such a service is configured for the collection, and achieves the same effect. NOTE: This feature is under current development.

sort

with this dictionary parameter one can control the sorting order of the documents matching the filter, effectively determining what document will come first and hence be the deleted one. See the find method for more on sorting.

max_time_ms

a timeout, in milliseconds, for the underlying HTTP request.

Returns

a DeleteResult object summarizing the outcome of the delete operation.

Example

>>> my_coll.insert_many([{"seq": 1}, {"seq": 0}, {"seq": 2}])
InsertManyResult(...)
>>> my_coll.delete_one({"seq": 1})
DeleteResult(raw_results=..., deleted_count=1)
>>> my_coll.distinct("seq")
[0, 2]
>>> my_coll.delete_one(
...     {"seq": {"$exists": True}},
...     sort={"seq": astrapy.constants.SortDocuments.DESCENDING},
... )
DeleteResult(raw_results=..., deleted_count=1)
>>> my_coll.distinct("seq")
[0]
>>> my_coll.delete_one({"seq": 2})
DeleteResult(raw_results=..., deleted_count=0)

Expand source code

@recast_method_sync
def delete_one(
    self,
    filter: Dict[str, Any],
    *,
    vector: Optional[VectorType] = None,
    vectorize: Optional[str] = None,
    sort: Optional[SortType] = None,
    max_time_ms: Optional[int] = None,
) -> DeleteResult:
    """
    Delete one document matching a provided filter.
    This method never deletes more than a single document, regardless
    of the number of matches to the provided filters.

    Args:
        filter: a predicate expressed as a dictionary according to the
            Data API filter syntax. Examples are:
                {}
                {"name": "John"}
                {"price": {"$le": 100}}
                {"$and": [{"name": "John"}, {"price": {"$le": 100}}]}
            See the Data API documentation for the full set of operators.
        vector: a suitable vector, i.e. a list of float numbers of the appropriate
            dimensionality, to use vector search (i.e. ANN,
            or "approximate nearest-neighbours" search), as the sorting criterion.
            In this way, the matched document (if any) will be the one
            that is most similar to the provided vector.
            This parameter cannot be used together with `sort`.
            See the `find` method for more details on this parameter.
        vectorize: a string to be made into a vector to perform vector search.
            This can be supplied in (exclusive) alternative to `vector`,
            provided such a service is configured for the collection,
            and achieves the same effect.
            NOTE: This feature is under current development.
        sort: with this dictionary parameter one can control the sorting
            order of the documents matching the filter, effectively
            determining what document will come first and hence be the
            deleted one. See the `find` method for more on sorting.
        max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

    Returns:
        a DeleteResult object summarizing the outcome of the delete operation.

    Example:
        >>> my_coll.insert_many([{"seq": 1}, {"seq": 0}, {"seq": 2}])
        InsertManyResult(...)
        >>> my_coll.delete_one({"seq": 1})
        DeleteResult(raw_results=..., deleted_count=1)
        >>> my_coll.distinct("seq")
        [0, 2]
        >>> my_coll.delete_one(
        ...     {"seq": {"$exists": True}},
        ...     sort={"seq": astrapy.constants.SortDocuments.DESCENDING},
        ... )
        DeleteResult(raw_results=..., deleted_count=1)
        >>> my_coll.distinct("seq")
        [0]
        >>> my_coll.delete_one({"seq": 2})
        DeleteResult(raw_results=..., deleted_count=0)
    """

    _sort = _collate_vector_to_sort(sort, vector, vectorize)
    logger.info(f"calling delete_one_by_predicate on '{self.name}'")
    do_response = self._astra_db_collection.delete_one_by_predicate(
        filter=filter, timeout_info=base_timeout_info(max_time_ms), sort=_sort
    )
    logger.info(f"finished calling delete_one_by_predicate on '{self.name}'")
    if "deletedCount" in do_response.get("status", {}):
        deleted_count = do_response["status"]["deletedCount"]
        if deleted_count == -1:
            return DeleteResult(
                deleted_count=None,
                raw_results=[do_response],
            )
        else:
            # expected a non-negative integer:
            return DeleteResult(
                deleted_count=deleted_count,
                raw_results=[do_response],
            )
    else:
        raise DataAPIFaultyResponseException(
            text="Faulty response from delete_one API command.",
            raw_response=do_response,
        )

def distinct(self, key: str, *, filter: Optional[FilterType] = None, max_time_ms: Optional[int] = None) ‑> List[Any]

Return a list of the unique values of key across the documents in the collection that match the provided filter.

Args

key

the name of the field whose value is inspected across documents. Keys can use dot-notation to descend to deeper document levels. Example of acceptable key values: "field" "field.subfield" "field.3" "field.3.subfield" If lists are encountered and no numeric index is specified, all items in the list are visited.

filter

a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$le": 100}} {"$and": [{"name": "John"}, {"price": {"$le": 100}}]} See the Data API documentation for the full set of operators.

max_time_ms

a timeout, in milliseconds, for the operation.

Returns

a list of all different values for key found across the documents that match the filter. The result list has no repeated items.

Example

>>> my_coll.insert_many(
...     [
...         {"name": "Marco", "food": ["apple", "orange"], "city": "Helsinki"},
...         {"name": "Emma", "food": {"likes_fruit": True, "allergies": []}},
...     ]
... )
InsertManyResult(raw_results=..., inserted_ids=['c5b99f37-...', 'd6416321-...'])
>>> my_coll.distinct("name")
['Marco', 'Emma']
>>> my_coll.distinct("city")
['Helsinki']
>>> my_coll.distinct("food")
['apple', 'orange', {'likes_fruit': True, 'allergies': []}]
>>> my_coll.distinct("food.1")
['orange']
>>> my_coll.distinct("food.allergies")
[]
>>> my_coll.distinct("food.likes_fruit")
[True]

Note

It must be kept in mind that distinct is a client-side operation, which effectively browses all required documents using the logic of the find method and collects the unique values found for key. As such, there may be performance, latency and ultimately billing implications if the amount of matching documents is large.

Note

For details on the behaviour of "distinct" in conjunction with real-time changes in the collection contents, see the Note of the find command.

Expand source code

def distinct(
    self,
    key: str,
    *,
    filter: Optional[FilterType] = None,
    max_time_ms: Optional[int] = None,
) -> List[Any]:
    """
    Return a list of the unique values of `key` across the documents
    in the collection that match the provided filter.

    Args:
        key: the name of the field whose value is inspected across documents.
            Keys can use dot-notation to descend to deeper document levels.
            Example of acceptable `key` values:
                "field"
                "field.subfield"
                "field.3"
                "field.3.subfield"
            If lists are encountered and no numeric index is specified,
            all items in the list are visited.
        filter: a predicate expressed as a dictionary according to the
            Data API filter syntax. Examples are:
                {}
                {"name": "John"}
                {"price": {"$le": 100}}
                {"$and": [{"name": "John"}, {"price": {"$le": 100}}]}
            See the Data API documentation for the full set of operators.
        max_time_ms: a timeout, in milliseconds, for the operation.

    Returns:
        a list of all different values for `key` found across the documents
        that match the filter. The result list has no repeated items.

    Example:
        >>> my_coll.insert_many(
        ...     [
        ...         {"name": "Marco", "food": ["apple", "orange"], "city": "Helsinki"},
        ...         {"name": "Emma", "food": {"likes_fruit": True, "allergies": []}},
        ...     ]
        ... )
        InsertManyResult(raw_results=..., inserted_ids=['c5b99f37-...', 'd6416321-...'])
        >>> my_coll.distinct("name")
        ['Marco', 'Emma']
        >>> my_coll.distinct("city")
        ['Helsinki']
        >>> my_coll.distinct("food")
        ['apple', 'orange', {'likes_fruit': True, 'allergies': []}]
        >>> my_coll.distinct("food.1")
        ['orange']
        >>> my_coll.distinct("food.allergies")
        []
        >>> my_coll.distinct("food.likes_fruit")
        [True]

    Note:
        It must be kept in mind that `distinct` is a client-side operation,
        which effectively browses all required documents using the logic
        of the `find` method and collects the unique values found for `key`.
        As such, there may be performance, latency and ultimately
        billing implications if the amount of matching documents is large.

    Note:
        For details on the behaviour of "distinct" in conjunction with
        real-time changes in the collection contents, see the
        Note of the `find` command.
    """

    f_cursor = Cursor(
        collection=self,
        filter=filter,
        projection={key: True},
        max_time_ms=None,
        overall_max_time_ms=max_time_ms,
    )
    return f_cursor.distinct(key)  # type: ignore[no-any-return]

def drop(self, *, max_time_ms: Optional[int] = None) ‑> Dict[str, Any]

Drop the collection, i.e. delete it from the database along with all the documents it contains.

Args

max_time_ms

a timeout, in milliseconds, for the underlying HTTP request. Remember there is not guarantee that a request that has timed out us not in fact honored.

Returns

a dictionary of the form {"ok": 1} to signal successful deletion.

Example

>>> my_coll.find_one({})
{'_id': '...', 'a': 100}
>>> my_coll.drop()
{'ok': 1}
>>> my_coll.find_one({})
Traceback (most recent call last):
    ... ...
astrapy.exceptions.DataAPIResponseException: Collection does not exist, collection name: my_collection

Note

Use with caution.

Note

Once the method succeeds, methods on this object can still be invoked: however, this hardly makes sense as the underlying actual collection is no more. It is responsibility of the developer to design a correct flow which avoids using a deceased collection any further.

Note

Once the method succeeds, methods on this object can still be invoked: however, this hardly makes sense as the underlying actual collection is no more. It is responsibility of the developer to design a correct flow which avoids using a deceased collection any further.

Expand source code

def drop(self, *, max_time_ms: Optional[int] = None) -> Dict[str, Any]:
    """
    Drop the collection, i.e. delete it from the database along with
    all the documents it contains.

    Args:
        max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.
            Remember there is not guarantee that a request that has
            timed out us not in fact honored.

    Returns:
        a dictionary of the form {"ok": 1} to signal successful deletion.

    Example:
        >>> my_coll.find_one({})
        {'_id': '...', 'a': 100}
        >>> my_coll.drop()
        {'ok': 1}
        >>> my_coll.find_one({})
        Traceback (most recent call last):
            ... ...
        astrapy.exceptions.DataAPIResponseException: Collection does not exist, collection name: my_collection

    Note:
        Use with caution.


    Note:
        Once the method succeeds, methods on this object can still be invoked:
        however, this hardly makes sense as the underlying actual collection
        is no more.
        It is responsibility of the developer to design a correct flow
        which avoids using a deceased collection any further.

    Note:
        Once the method succeeds, methods on this object can still be invoked:
        however, this hardly makes sense as the underlying actual collection
        is no more.
        It is responsibility of the developer to design a correct flow
        which avoids using a deceased collection any further.
    """

    logger.info(f"dropping collection '{self.name}' (self)")
    drop_result = self.database.drop_collection(self, max_time_ms=max_time_ms)
    logger.info(f"finished dropping collection '{self.name}' (self)")
    return drop_result  # type: ignore[no-any-return]

def find(self, filter: Optional[FilterType] = None, *, projection: Optional[ProjectionType] = None, skip: Optional[int] = None, limit: Optional[int] = None, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, include_similarity: Optional[bool] = None, sort: Optional[SortType] = None, max_time_ms: Optional[int] = None) ‑> Cursor

Find documents on the collection, matching a certain provided filter.

The method returns a Cursor that can then be iterated over. Depending on the method call pattern, the iteration over all documents can reflect collection mutations occurred since the find method was called, or not. In cases where the cursor reflects mutations in real-time, it will iterate over cursors in an approximate way (i.e. exhibiting occasional skipped or duplicate documents). This happens when making use of the sort option in a non-vector-search manner.

Args

filter

a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$le": 100}} {"$and": [{"name": "John"}, {"price": {"$le": 100}}]} See the Data API documentation for the full set of operators.

projection

used to select a subset of fields in the documents being returned. The projection can be: an iterable over the field names to return; a dictionary {field_name: True} to positively select certain fields; or a dictionary {field_name: False} if one wants to discard some fields from the response. The default is to return the whole documents.

skip

with this integer parameter, what would be the first skip documents returned by the query are discarded, and the results start from the (skip+1)-th document. This parameter can be used only in conjunction with an explicit sort criterion of the ascending/descending type (i.e. it cannot be used when not sorting, nor with vector-based ANN search).

limit

this (integer) parameter sets a limit over how many documents are returned. Once limit is reached (or the cursor is exhausted for lack of matching documents), nothing more is returned.

vector

a suitable vector, i.e. a list of float numbers of the appropriate dimensionality, to perform vector search (i.e. ANN, or "approximate nearest-neighbours" search). When running similarity search on a collection, no other sorting criteria can be specified. Moreover, there is an upper bound to the number of documents that can be returned. For details, see the Note about upper bounds and the Data API documentation.

vectorize

a string to be made into a vector to perform vector search. This can be supplied in (exclusive) alternative to vector, provided such a service is configured for the collection, and achieves the same effect. NOTE: This feature is under current development.

include_similarity

a boolean to request the numeric value of the similarity to be returned as an added "$similarity" key in each returned document. Can only be used for vector ANN search, i.e. when either vector is supplied or the sort parameter has the shape {"$vector": …}.

sort

with this dictionary parameter one can control the order the documents are returned. See the Note about sorting, as well as the one about upper bounds, for details.

max_time_ms

a timeout, in milliseconds, for each single one of the underlying HTTP requests used to fetch documents as the cursor is iterated over.

Returns

a Cursor object representing iterations over the matching documents

(see the Cursor object for how to use it. The simplest thing is to

run a for loop

for document in collection.sort(...):).

Examples

>>> filter = {"seq": {"$exists": True}}
>>> for doc in my_coll.find(filter, projection={"seq": True}, limit=5):
...     print(doc["seq"])
...
37
35
10
36
27
>>> cursor1 = my_coll.find(
...     {},
...     limit=4,
...     sort={"seq": astrapy.constants.SortDocuments.DESCENDING},
... )
>>> [doc["_id"] for doc in cursor1]
['97e85f81-...', '1581efe4-...', '...', '...']
>>> cursor2 = my_coll.find({}, limit=3)
>>> cursor2.distinct("seq")
[37, 35, 10]

>>> my_coll.insert_many([
...     {"tag": "A", "$vector": [4, 5]},
...     {"tag": "B", "$vector": [3, 4]},
...     {"tag": "C", "$vector": [3, 2]},
...     {"tag": "D", "$vector": [4, 1]},
...     {"tag": "E", "$vector": [2, 5]},
... ])
>>> ann_tags = [
...     document["tag"]
...     for document in my_coll.find(
...         {},
...         limit=3,
...         vector=[3, 3],
...     )
... ]
>>> ann_tags
['A', 'B', 'C']
# (assuming the collection has metric VectorMetric.COSINE)

Note

The following are example values for the sort parameter. When no particular order is required: sort={} # (default when parameter not provided) When sorting by a certain value in ascending/descending order: sort={"field": SortDocuments.ASCENDING} sort={"field": SortDocuments.DESCENDING} When sorting first by "field" and then by "subfield" (while modern Python versions preserve the order of dictionaries, it is suggested for clarity to employ a collections.OrderedDict in these cases): sort={ "field": SortDocuments.ASCENDING, "subfield": SortDocuments.ASCENDING, } When running a vector similarity (ANN) search: sort={"$vector": [0.4, 0.15, -0.5]}

Note

Some combinations of arguments impose an implicit upper bound on the number of documents that are returned by the Data API. More specifically: (a) Vector ANN searches cannot return more than a number of documents that at the time of writing is set to 1000 items. (b) When using a sort criterion of the ascending/descending type, the Data API will return a smaller number of documents, set to 20 at the time of writing, and stop there. The returned documents are the top results across the whole collection according to the requested criterion. These provisions should be kept in mind even when subsequently running a command such as .distinct() on a cursor.

Note

When not specifying sorting criteria at all (by vector or otherwise), the cursor can scroll through an arbitrary number of documents as the Data API and the client periodically exchange new chunks of documents. It should be noted that the behavior of the cursor in the case documents have been added/removed after the find was started depends on database internals and it is not guaranteed, nor excluded, that such "real-time" changes in the data would be picked up by the cursor.

Expand source code

def find(
    self,
    filter: Optional[FilterType] = None,
    *,
    projection: Optional[ProjectionType] = None,
    skip: Optional[int] = None,
    limit: Optional[int] = None,
    vector: Optional[VectorType] = None,
    vectorize: Optional[str] = None,
    include_similarity: Optional[bool] = None,
    sort: Optional[SortType] = None,
    max_time_ms: Optional[int] = None,
) -> Cursor:
    """
    Find documents on the collection, matching a certain provided filter.

    The method returns a Cursor that can then be iterated over. Depending
    on the method call pattern, the iteration over all documents can reflect
    collection mutations occurred since the `find` method was called, or not.
    In cases where the cursor reflects mutations in real-time, it will iterate
    over cursors in an approximate way (i.e. exhibiting occasional skipped
    or duplicate documents). This happens when making use of the `sort`
    option in a non-vector-search manner.

    Args:
        filter: a predicate expressed as a dictionary according to the
            Data API filter syntax. Examples are:
                {}
                {"name": "John"}
                {"price": {"$le": 100}}
                {"$and": [{"name": "John"}, {"price": {"$le": 100}}]}
            See the Data API documentation for the full set of operators.
        projection: used to select a subset of fields in the documents being
            returned. The projection can be: an iterable over the field names
            to return; a dictionary {field_name: True} to positively select
            certain fields; or a dictionary {field_name: False} if one wants
            to discard some fields from the response.
            The default is to return the whole documents.
        skip: with this integer parameter, what would be the first `skip`
            documents returned by the query are discarded, and the results
            start from the (skip+1)-th document.
            This parameter can be used only in conjunction with an explicit
            `sort` criterion of the ascending/descending type (i.e. it cannot
            be used when not sorting, nor with vector-based ANN search).
        limit: this (integer) parameter sets a limit over how many documents
            are returned. Once `limit` is reached (or the cursor is exhausted
            for lack of matching documents), nothing more is returned.
        vector: a suitable vector, i.e. a list of float numbers of the appropriate
            dimensionality, to perform vector search (i.e. ANN,
            or "approximate nearest-neighbours" search).
            When running similarity search on a collection, no other sorting
            criteria can be specified. Moreover, there is an upper bound
            to the number of documents that can be returned. For details,
            see the Note about upper bounds and the Data API documentation.
        vectorize: a string to be made into a vector to perform vector search.
            This can be supplied in (exclusive) alternative to `vector`,
            provided such a service is configured for the collection,
            and achieves the same effect.
            NOTE: This feature is under current development.
        include_similarity: a boolean to request the numeric value of the
            similarity to be returned as an added "$similarity" key in each
            returned document. Can only be used for vector ANN search, i.e.
            when either `vector` is supplied or the `sort` parameter has the
            shape {"$vector": ...}.
        sort: with this dictionary parameter one can control the order
            the documents are returned. See the Note about sorting, as well as
            the one about upper bounds, for details.
        max_time_ms: a timeout, in milliseconds, for each single one
            of the underlying HTTP requests used to fetch documents as the
            cursor is iterated over.

    Returns:
        a Cursor object representing iterations over the matching documents
        (see the Cursor object for how to use it. The simplest thing is to
        run a for loop: `for document in collection.sort(...):`).

    Examples:
        >>> filter = {"seq": {"$exists": True}}
        >>> for doc in my_coll.find(filter, projection={"seq": True}, limit=5):
        ...     print(doc["seq"])
        ...
        37
        35
        10
        36
        27
        >>> cursor1 = my_coll.find(
        ...     {},
        ...     limit=4,
        ...     sort={"seq": astrapy.constants.SortDocuments.DESCENDING},
        ... )
        >>> [doc["_id"] for doc in cursor1]
        ['97e85f81-...', '1581efe4-...', '...', '...']
        >>> cursor2 = my_coll.find({}, limit=3)
        >>> cursor2.distinct("seq")
        [37, 35, 10]

        >>> my_coll.insert_many([
        ...     {"tag": "A", "$vector": [4, 5]},
        ...     {"tag": "B", "$vector": [3, 4]},
        ...     {"tag": "C", "$vector": [3, 2]},
        ...     {"tag": "D", "$vector": [4, 1]},
        ...     {"tag": "E", "$vector": [2, 5]},
        ... ])
        >>> ann_tags = [
        ...     document["tag"]
        ...     for document in my_coll.find(
        ...         {},
        ...         limit=3,
        ...         vector=[3, 3],
        ...     )
        ... ]
        >>> ann_tags
        ['A', 'B', 'C']
        # (assuming the collection has metric VectorMetric.COSINE)

    Note:
        The following are example values for the `sort` parameter.
        When no particular order is required:
            sort={}  # (default when parameter not provided)
        When sorting by a certain value in ascending/descending order:
            sort={"field": SortDocuments.ASCENDING}
            sort={"field": SortDocuments.DESCENDING}
        When sorting first by "field" and then by "subfield"
        (while modern Python versions preserve the order of dictionaries,
        it is suggested for clarity to employ a `collections.OrderedDict`
        in these cases):
            sort={
                "field": SortDocuments.ASCENDING,
                "subfield": SortDocuments.ASCENDING,
            }
        When running a vector similarity (ANN) search:
            sort={"$vector": [0.4, 0.15, -0.5]}

    Note:
        Some combinations of arguments impose an implicit upper bound on the
        number of documents that are returned by the Data API. More specifically:
        (a) Vector ANN searches cannot return more than a number of documents
        that at the time of writing is set to 1000 items.
        (b) When using a sort criterion of the ascending/descending type,
        the Data API will return a smaller number of documents, set to 20
        at the time of writing, and stop there. The returned documents are
        the top results across the whole collection according to the requested
        criterion.
        These provisions should be kept in mind even when subsequently running
        a command such as `.distinct()` on a cursor.

    Note:
        When not specifying sorting criteria at all (by vector or otherwise),
        the cursor can scroll through an arbitrary number of documents as
        the Data API and the client periodically exchange new chunks of documents.
        It should be noted that the behavior of the cursor in the case documents
        have been added/removed after the `find` was started depends on database
        internals and it is not guaranteed, nor excluded, that such "real-time"
        changes in the data would be picked up by the cursor.
    """

    _sort = _collate_vector_to_sort(sort, vector, vectorize)
    if include_similarity is not None and not _is_vector_sort(_sort):
        raise ValueError(
            "Cannot use `include_similarity` when not searching through `vector`."
        )
    return (
        Cursor(
            collection=self,
            filter=filter,
            projection=projection,
            max_time_ms=max_time_ms,
            overall_max_time_ms=None,
        )
        .skip(skip)
        .limit(limit)
        .sort(_sort)
        .include_similarity(include_similarity)
    )

def find_one(self, filter: Optional[FilterType] = None, *, projection: Optional[ProjectionType] = None, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, include_similarity: Optional[bool] = None, sort: Optional[SortType] = None, max_time_ms: Optional[int] = None) ‑> Optional[Dict[str, Any]]

Run a search, returning the first document in the collection that matches provided filters, if any is found.

Args

filter

a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$le": 100}} {"$and": [{"name": "John"}, {"price": {"$le": 100}}]} See the Data API documentation for the full set of operators.

projection

used to select a subset of fields in the documents being returned. The projection can be: an iterable over the field names to return; a dictionary {field_name: True} to positively select certain fields; or a dictionary {field_name: False} if one wants to discard some fields from the response. The default is to return the whole documents.

vector

a suitable vector, i.e. a list of float numbers of the appropriate dimensionality, to perform vector search (i.e. ANN, or "approximate nearest-neighbours" search), extracting the most similar document in the collection matching the filter. This parameter cannot be used together with sort. See the find method for more details on this parameter.

vectorize

a string to be made into a vector to perform vector search. This can be supplied in (exclusive) alternative to vector, provided such a service is configured for the collection, and achieves the same effect. NOTE: This feature is under current development.

include_similarity

a boolean to request the numeric value of the similarity to be returned as an added "$similarity" key in the returned document. Can only be used for vector ANN search, i.e. when either vector is supplied or the sort parameter has the shape {"$vector": …}.

sort

with this dictionary parameter one can control the order the documents are returned. See the Note about sorting for details.

max_time_ms

a timeout, in milliseconds, for the underlying HTTP request.

Returns

a dictionary expressing the required document, otherwise None.

Examples

>>> my_coll.find_one({})
{'_id': '68d1e515-...', 'seq': 37}
>>> my_coll.find_one({"seq": 10})
{'_id': 'd560e217-...', 'seq': 10}
>>> my_coll.find_one({"seq": 1011})
>>> # (returns None for no matches)
>>> my_coll.find_one({}, projection={"seq": False})
{'_id': '68d1e515-...'}
>>> my_coll.find_one(
...     {},
...     sort={"seq": astrapy.constants.SortDocuments.DESCENDING},
... )
{'_id': '97e85f81-...', 'seq': 69}
>>> my_coll.find_one({}, vector=[1, 0])
{'_id': '...', 'tag': 'D', '$vector': [4.0, 1.0]}

Note

See the find method for more details on the accepted parameters (whereas skip and limit are not valid parameters for find_one).

Expand source code

def find_one(
    self,
    filter: Optional[FilterType] = None,
    *,
    projection: Optional[ProjectionType] = None,
    vector: Optional[VectorType] = None,
    vectorize: Optional[str] = None,
    include_similarity: Optional[bool] = None,
    sort: Optional[SortType] = None,
    max_time_ms: Optional[int] = None,
) -> Union[DocumentType, None]:
    """
    Run a search, returning the first document in the collection that matches
    provided filters, if any is found.

    Args:
        filter: a predicate expressed as a dictionary according to the
            Data API filter syntax. Examples are:
                {}
                {"name": "John"}
                {"price": {"$le": 100}}
                {"$and": [{"name": "John"}, {"price": {"$le": 100}}]}
            See the Data API documentation for the full set of operators.
        projection: used to select a subset of fields in the documents being
            returned. The projection can be: an iterable over the field names
            to return; a dictionary {field_name: True} to positively select
            certain fields; or a dictionary {field_name: False} if one wants
            to discard some fields from the response.
            The default is to return the whole documents.
        vector: a suitable vector, i.e. a list of float numbers of the appropriate
            dimensionality, to perform vector search (i.e. ANN,
            or "approximate nearest-neighbours" search), extracting the most
            similar document in the collection matching the filter.
            This parameter cannot be used together with `sort`.
            See the `find` method for more details on this parameter.
        vectorize: a string to be made into a vector to perform vector search.
            This can be supplied in (exclusive) alternative to `vector`,
            provided such a service is configured for the collection,
            and achieves the same effect.
            NOTE: This feature is under current development.
        include_similarity: a boolean to request the numeric value of the
            similarity to be returned as an added "$similarity" key in the
            returned document. Can only be used for vector ANN search, i.e.
            when either `vector` is supplied or the `sort` parameter has the
            shape {"$vector": ...}.
        sort: with this dictionary parameter one can control the order
            the documents are returned. See the Note about sorting for details.
        max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

    Returns:
        a dictionary expressing the required document, otherwise None.

    Examples:
        >>> my_coll.find_one({})
        {'_id': '68d1e515-...', 'seq': 37}
        >>> my_coll.find_one({"seq": 10})
        {'_id': 'd560e217-...', 'seq': 10}
        >>> my_coll.find_one({"seq": 1011})
        >>> # (returns None for no matches)
        >>> my_coll.find_one({}, projection={"seq": False})
        {'_id': '68d1e515-...'}
        >>> my_coll.find_one(
        ...     {},
        ...     sort={"seq": astrapy.constants.SortDocuments.DESCENDING},
        ... )
        {'_id': '97e85f81-...', 'seq': 69}
        >>> my_coll.find_one({}, vector=[1, 0])
        {'_id': '...', 'tag': 'D', '$vector': [4.0, 1.0]}

    Note:
        See the `find` method for more details on the accepted parameters
        (whereas `skip` and `limit` are not valid parameters for `find_one`).
    """

    fo_cursor = self.find(
        filter=filter,
        projection=projection,
        skip=None,
        limit=1,
        vector=vector,
        vectorize=vectorize,
        include_similarity=include_similarity,
        sort=sort,
        max_time_ms=max_time_ms,
    )
    try:
        document = fo_cursor.__next__()
        return document  # type: ignore[no-any-return]
    except StopIteration:
        return None

def find_one_and_delete(self, filter: Dict[str, Any], *, projection: Optional[ProjectionType] = None, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, sort: Optional[SortType] = None, max_time_ms: Optional[int] = None) ‑> Optional[Dict[str, Any]]

Find a document in the collection and delete it. The deleted document, however, is the return value of the method.

Args

filter

a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$le": 100}} {"$and": [{"name": "John"}, {"price": {"$le": 100}}]} See the Data API documentation for the full set of operators.

projection

used to select a subset of fields in the document being returned. The projection can be: an iterable over the field names to return; a dictionary {field_name: True} to positively select certain fields; or a dictionary {field_name: False} if one wants to discard some fields from the response. Note that the _id field will be returned with the document in any case, regardless of what the provided projection requires. The default is to return the whole documents.

vector

a suitable vector, i.e. a list of float numbers of the appropriate dimensionality, to use vector search (i.e. ANN, or "approximate nearest-neighbours" search), as the sorting criterion. In this way, the matched document (if any) will be the one that is most similar to the provided vector. This parameter cannot be used together with sort. See the find method for more details on this parameter.

vectorize

a string to be made into a vector to perform vector search. This can be supplied in (exclusive) alternative to vector, provided such a service is configured for the collection, and achieves the same effect. NOTE: This feature is under current development.

sort

with this dictionary parameter one can control the sorting order of the documents matching the filter, effectively determining what document will come first and hence be the deleted one. See the find method for more on sorting.

max_time_ms

a timeout, in milliseconds, for the underlying HTTP request.

Returns

Either the document (or a projection thereof, as requested), or None if no matches were found in the first place.

Example

>>> my_coll.insert_many(
...     [
...         {"species": "swan", "class": "Aves"},
...         {"species": "frog", "class": "Amphibia"},
...     ],
... )
InsertManyResult(...)
>>> my_coll.find_one_and_delete(
...     {"species": {"$ne": "frog"}},
...     projection=["species"],
... )
{'_id': '5997fb48-...', 'species': 'swan'}
>>> my_coll.find_one_and_delete({"species": {"$ne": "frog"}})
>>> # (returns None for no matches)

Expand source code

@recast_method_sync
def find_one_and_delete(
    self,
    filter: Dict[str, Any],
    *,
    projection: Optional[ProjectionType] = None,
    vector: Optional[VectorType] = None,
    vectorize: Optional[str] = None,
    sort: Optional[SortType] = None,
    max_time_ms: Optional[int] = None,
) -> Union[DocumentType, None]:
    """
    Find a document in the collection and delete it. The deleted document,
    however, is the return value of the method.

    Args:
        filter: a predicate expressed as a dictionary according to the
            Data API filter syntax. Examples are:
                {}
                {"name": "John"}
                {"price": {"$le": 100}}
                {"$and": [{"name": "John"}, {"price": {"$le": 100}}]}
            See the Data API documentation for the full set of operators.
        projection: used to select a subset of fields in the document being
            returned. The projection can be: an iterable over the field names
            to return; a dictionary {field_name: True} to positively select
            certain fields; or a dictionary {field_name: False} if one wants
            to discard some fields from the response.
            Note that the `_id` field will be returned with the document
            in any case, regardless of what the provided `projection` requires.
            The default is to return the whole documents.
        vector: a suitable vector, i.e. a list of float numbers of the appropriate
            dimensionality, to use vector search (i.e. ANN,
            or "approximate nearest-neighbours" search), as the sorting criterion.
            In this way, the matched document (if any) will be the one
            that is most similar to the provided vector.
            This parameter cannot be used together with `sort`.
            See the `find` method for more details on this parameter.
        vectorize: a string to be made into a vector to perform vector search.
            This can be supplied in (exclusive) alternative to `vector`,
            provided such a service is configured for the collection,
            and achieves the same effect.
            NOTE: This feature is under current development.
        sort: with this dictionary parameter one can control the sorting
            order of the documents matching the filter, effectively
            determining what document will come first and hence be the
            deleted one. See the `find` method for more on sorting.
        max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

    Returns:
        Either the document (or a projection thereof, as requested), or None
        if no matches were found in the first place.

    Example:
        >>> my_coll.insert_many(
        ...     [
        ...         {"species": "swan", "class": "Aves"},
        ...         {"species": "frog", "class": "Amphibia"},
        ...     ],
        ... )
        InsertManyResult(...)
        >>> my_coll.find_one_and_delete(
        ...     {"species": {"$ne": "frog"}},
        ...     projection=["species"],
        ... )
        {'_id': '5997fb48-...', 'species': 'swan'}
        >>> my_coll.find_one_and_delete({"species": {"$ne": "frog"}})
        >>> # (returns None for no matches)
    """

    _sort = _collate_vector_to_sort(sort, vector, vectorize)
    _projection = normalize_optional_projection(projection)
    logger.info(f"calling find_one_and_delete on '{self.name}'")
    fo_response = self._astra_db_collection.find_one_and_delete(
        sort=_sort,
        filter=filter,
        projection=_projection,
        timeout_info=base_timeout_info(max_time_ms),
    )
    logger.info(f"finished calling find_one_and_delete on '{self.name}'")
    if "document" in fo_response.get("data", {}):
        document = fo_response["data"]["document"]
        return document  # type: ignore[no-any-return]
    else:
        deleted_count = fo_response.get("status", {}).get("deletedCount")
        if deleted_count == 0:
            return None
        else:
            raise DataAPIFaultyResponseException(
                text="Faulty response from find_one_and_delete API command.",
                raw_response=fo_response,
            )

def find_one_and_replace(self, filter: Dict[str, Any], replacement: DocumentType, *, projection: Optional[ProjectionType] = None, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, sort: Optional[SortType] = None, upsert: bool = False, return_document: str = 'before', max_time_ms: Optional[int] = None) ‑> Optional[Dict[str, Any]]

Find a document on the collection and replace it entirely with a new one, optionally inserting a new one if no match is found.

Args

filter

a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$le": 100}} {"$and": [{"name": "John"}, {"price": {"$le": 100}}]} See the Data API documentation for the full set of operators.

replacement

the new document to write into the collection.

projection

used to select a subset of fields in the document being returned. The projection can be: an iterable over the field names to return; a dictionary {field_name: True} to positively select certain fields; or a dictionary {field_name: False} if one wants to discard some fields from the response. The default is to return the whole documents.

vector

a suitable vector, i.e. a list of float numbers of the appropriate dimensionality, to use vector search (i.e. ANN, or "approximate nearest-neighbours" search), as the sorting criterion. In this way, the matched document (if any) will be the one that is most similar to the provided vector. This parameter cannot be used together with sort. See the find method for more details on this parameter.

vectorize

a string to be made into a vector to perform vector search. This can be supplied in (exclusive) alternative to vector, provided such a service is configured for the collection, and achieves the same effect. NOTE: This feature is under current development.

sort

with this dictionary parameter one can control the sorting order of the documents matching the filter, effectively determining what document will come first and hence be the replaced one. See the find method for more on sorting.

upsert

this parameter controls the behavior in absence of matches. If True, replacement is inserted as a new document if no matches are found on the collection. If False, the operation silently does nothing in case of no matches.

return_document

a flag controlling what document is returned: if set to ReturnDocument.BEFORE, or the string "before", the document found on database is returned; if set to ReturnDocument.AFTER, or the string "after", the new document is returned. The default is "before".

max_time_ms

a timeout, in milliseconds, for the underlying HTTP request.

Returns

A document (or a projection thereof, as required), either the one before the replace operation or the one after that. Alternatively, the method returns None to represent that no matching document was found, or that no replacement was inserted (depending on the return_document parameter).

Example

>>> my_coll.insert_one({"_id": "rule1", "text": "all animals are equal"})
InsertOneResult(...)
>>> my_coll.find_one_and_replace(
...     {"_id": "rule1"},
...     {"text": "some animals are more equal!"},
... )
{'_id': 'rule1', 'text': 'all animals are equal'}
>>> my_coll.find_one_and_replace(
...     {"text": "some animals are more equal!"},
...     {"text": "and the pigs are the rulers"},
...     return_document=astrapy.constants.ReturnDocument.AFTER,
... )
{'_id': 'rule1', 'text': 'and the pigs are the rulers'}
>>> my_coll.find_one_and_replace(
...     {"_id": "rule2"},
...     {"text": "F=ma^2"},
...     return_document=astrapy.constants.ReturnDocument.AFTER,
... )
>>> # (returns None for no matches)
>>> my_coll.find_one_and_replace(
...     {"_id": "rule2"},
...     {"text": "F=ma"},
...     upsert=True,
...     return_document=astrapy.constants.ReturnDocument.AFTER,
...     projection={"_id": False},
... )
{'text': 'F=ma'}

Expand source code

@recast_method_sync
def find_one_and_replace(
    self,
    filter: Dict[str, Any],
    replacement: DocumentType,
    *,
    projection: Optional[ProjectionType] = None,
    vector: Optional[VectorType] = None,
    vectorize: Optional[str] = None,
    sort: Optional[SortType] = None,
    upsert: bool = False,
    return_document: str = ReturnDocument.BEFORE,
    max_time_ms: Optional[int] = None,
) -> Union[DocumentType, None]:
    """
    Find a document on the collection and replace it entirely with a new one,
    optionally inserting a new one if no match is found.

    Args:
        filter: a predicate expressed as a dictionary according to the
            Data API filter syntax. Examples are:
                {}
                {"name": "John"}
                {"price": {"$le": 100}}
                {"$and": [{"name": "John"}, {"price": {"$le": 100}}]}
            See the Data API documentation for the full set of operators.
        replacement: the new document to write into the collection.
        projection: used to select a subset of fields in the document being
            returned. The projection can be: an iterable over the field names
            to return; a dictionary {field_name: True} to positively select
            certain fields; or a dictionary {field_name: False} if one wants
            to discard some fields from the response.
            The default is to return the whole documents.
        vector: a suitable vector, i.e. a list of float numbers of the appropriate
            dimensionality, to use vector search (i.e. ANN,
            or "approximate nearest-neighbours" search), as the sorting criterion.
            In this way, the matched document (if any) will be the one
            that is most similar to the provided vector.
            This parameter cannot be used together with `sort`.
            See the `find` method for more details on this parameter.
        vectorize: a string to be made into a vector to perform vector search.
            This can be supplied in (exclusive) alternative to `vector`,
            provided such a service is configured for the collection,
            and achieves the same effect.
            NOTE: This feature is under current development.
        sort: with this dictionary parameter one can control the sorting
            order of the documents matching the filter, effectively
            determining what document will come first and hence be the
            replaced one. See the `find` method for more on sorting.
        upsert: this parameter controls the behavior in absence of matches.
            If True, `replacement` is inserted as a new document
            if no matches are found on the collection. If False,
            the operation silently does nothing in case of no matches.
        return_document: a flag controlling what document is returned:
            if set to `ReturnDocument.BEFORE`, or the string "before",
            the document found on database is returned; if set to
            `ReturnDocument.AFTER`, or the string "after", the new
            document is returned. The default is "before".
        max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

    Returns:
        A document (or a projection thereof, as required), either the one
        before the replace operation or the one after that.
        Alternatively, the method returns None to represent
        that no matching document was found, or that no replacement
        was inserted (depending on the `return_document` parameter).

    Example:
        >>> my_coll.insert_one({"_id": "rule1", "text": "all animals are equal"})
        InsertOneResult(...)
        >>> my_coll.find_one_and_replace(
        ...     {"_id": "rule1"},
        ...     {"text": "some animals are more equal!"},
        ... )
        {'_id': 'rule1', 'text': 'all animals are equal'}
        >>> my_coll.find_one_and_replace(
        ...     {"text": "some animals are more equal!"},
        ...     {"text": "and the pigs are the rulers"},
        ...     return_document=astrapy.constants.ReturnDocument.AFTER,
        ... )
        {'_id': 'rule1', 'text': 'and the pigs are the rulers'}
        >>> my_coll.find_one_and_replace(
        ...     {"_id": "rule2"},
        ...     {"text": "F=ma^2"},
        ...     return_document=astrapy.constants.ReturnDocument.AFTER,
        ... )
        >>> # (returns None for no matches)
        >>> my_coll.find_one_and_replace(
        ...     {"_id": "rule2"},
        ...     {"text": "F=ma"},
        ...     upsert=True,
        ...     return_document=astrapy.constants.ReturnDocument.AFTER,
        ...     projection={"_id": False},
        ... )
        {'text': 'F=ma'}
    """

    _sort = _collate_vector_to_sort(sort, vector, vectorize)
    options = {
        "returnDocument": return_document,
        "upsert": upsert,
    }
    logger.info(f"calling find_one_and_replace on '{self.name}'")
    fo_response = self._astra_db_collection.find_one_and_replace(
        replacement=replacement,
        filter=filter,
        projection=normalize_optional_projection(projection),
        sort=_sort,
        options=options,
        timeout_info=base_timeout_info(max_time_ms),
    )
    logger.info(f"finished calling find_one_and_replace on '{self.name}'")
    if "document" in fo_response.get("data", {}):
        ret_document = fo_response.get("data", {}).get("document")
        if ret_document is None:
            return None
        else:
            return ret_document  # type: ignore[no-any-return]
    else:
        raise DataAPIFaultyResponseException(
            text="Faulty response from find_one_and_replace API command.",
            raw_response=fo_response,
        )

def find_one_and_update(self, filter: Dict[str, Any], update: Dict[str, Any], *, projection: Optional[ProjectionType] = None, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, sort: Optional[SortType] = None, upsert: bool = False, return_document: str = 'before', max_time_ms: Optional[int] = None) ‑> Optional[Dict[str, Any]]

Find a document on the collection and update it as requested, optionally inserting a new one if no match is found.

Args

filter

a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$le": 100}} {"$and": [{"name": "John"}, {"price": {"$le": 100}}]} See the Data API documentation for the full set of operators.

update

the update prescription to apply to the document, expressed as a dictionary as per Data API syntax. Examples are: {"$set": {"field": "value}} {"$inc": {"counter": 10}} {"$unset": {"field": ""}} See the Data API documentation for the full syntax.

projection

used to select a subset of fields in the document being returned. The projection can be: an iterable over the field names to return; a dictionary {field_name: True} to positively select certain fields; or a dictionary {field_name: False} if one wants to discard some fields from the response. The default is to return the whole documents.

vector

a suitable vector, i.e. a list of float numbers of the appropriate dimensionality, to use vector search (i.e. ANN, or "approximate nearest-neighbours" search), as the sorting criterion. In this way, the matched document (if any) will be the one that is most similar to the provided vector. This parameter cannot be used together with sort. See the find method for more details on this parameter.

vectorize

a string to be made into a vector to perform vector search. This can be supplied in (exclusive) alternative to vector, provided such a service is configured for the collection, and achieves the same effect. NOTE: This feature is under current development.

sort

with this dictionary parameter one can control the sorting order of the documents matching the filter, effectively determining what document will come first and hence be the updated one. See the find method for more on sorting.

upsert

this parameter controls the behavior in absence of matches. If True, a new document (resulting from applying the update to an empty document) is inserted if no matches are found on the collection. If False, the operation silently does nothing in case of no matches.

return_document

a flag controlling what document is returned: if set to ReturnDocument.BEFORE, or the string "before", the document found on database is returned; if set to ReturnDocument.AFTER, or the string "after", the new document is returned. The default is "before".

max_time_ms

a timeout, in milliseconds, for the underlying HTTP request.

Returns

A document (or a projection thereof, as required), either the one before the replace operation or the one after that. Alternatively, the method returns None to represent that no matching document was found, or that no update was applied (depending on the return_document parameter).

Example

>>> my_coll.insert_one({"Marco": "Polo"})
InsertOneResult(...)
>>> my_coll.find_one_and_update(
...     {"Marco": {"$exists": True}},
...     {"$set": {"title": "Mr."}},
... )
{'_id': 'a80106f2-...', 'Marco': 'Polo'}
>>> my_coll.find_one_and_update(
...     {"title": "Mr."},
...     {"$inc": {"rank": 3}},
...     projection=["title", "rank"],
...     return_document=astrapy.constants.ReturnDocument.AFTER,
... )
{'_id': 'a80106f2-...', 'title': 'Mr.', 'rank': 3}
>>> my_coll.find_one_and_update(
...     {"name": "Johnny"},
...     {"$set": {"rank": 0}},
...     return_document=astrapy.constants.ReturnDocument.AFTER,
... )
>>> # (returns None for no matches)
>>> my_coll.find_one_and_update(
...     {"name": "Johnny"},
...     {"$set": {"rank": 0}},
...     upsert=True,
...     return_document=astrapy.constants.ReturnDocument.AFTER,
... )
{'_id': 'cb4ef2ab-...', 'name': 'Johnny', 'rank': 0}

Expand source code

@recast_method_sync
def find_one_and_update(
    self,
    filter: Dict[str, Any],
    update: Dict[str, Any],
    *,
    projection: Optional[ProjectionType] = None,
    vector: Optional[VectorType] = None,
    vectorize: Optional[str] = None,
    sort: Optional[SortType] = None,
    upsert: bool = False,
    return_document: str = ReturnDocument.BEFORE,
    max_time_ms: Optional[int] = None,
) -> Union[DocumentType, None]:
    """
    Find a document on the collection and update it as requested,
    optionally inserting a new one if no match is found.

    Args:
        filter: a predicate expressed as a dictionary according to the
            Data API filter syntax. Examples are:
                {}
                {"name": "John"}
                {"price": {"$le": 100}}
                {"$and": [{"name": "John"}, {"price": {"$le": 100}}]}
            See the Data API documentation for the full set of operators.
        update: the update prescription to apply to the document, expressed
            as a dictionary as per Data API syntax. Examples are:
                {"$set": {"field": "value}}
                {"$inc": {"counter": 10}}
                {"$unset": {"field": ""}}
            See the Data API documentation for the full syntax.
        projection: used to select a subset of fields in the document being
            returned. The projection can be: an iterable over the field names
            to return; a dictionary {field_name: True} to positively select
            certain fields; or a dictionary {field_name: False} if one wants
            to discard some fields from the response.
            The default is to return the whole documents.
        vector: a suitable vector, i.e. a list of float numbers of the appropriate
            dimensionality, to use vector search (i.e. ANN,
            or "approximate nearest-neighbours" search), as the sorting criterion.
            In this way, the matched document (if any) will be the one
            that is most similar to the provided vector.
            This parameter cannot be used together with `sort`.
            See the `find` method for more details on this parameter.
        vectorize: a string to be made into a vector to perform vector search.
            This can be supplied in (exclusive) alternative to `vector`,
            provided such a service is configured for the collection,
            and achieves the same effect.
            NOTE: This feature is under current development.
        sort: with this dictionary parameter one can control the sorting
            order of the documents matching the filter, effectively
            determining what document will come first and hence be the
            updated one. See the `find` method for more on sorting.
        upsert: this parameter controls the behavior in absence of matches.
            If True, a new document (resulting from applying the `update`
            to an empty document) is inserted if no matches are found on
            the collection. If False, the operation silently does nothing
            in case of no matches.
        return_document: a flag controlling what document is returned:
            if set to `ReturnDocument.BEFORE`, or the string "before",
            the document found on database is returned; if set to
            `ReturnDocument.AFTER`, or the string "after", the new
            document is returned. The default is "before".
        max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

    Returns:
        A document (or a projection thereof, as required), either the one
        before the replace operation or the one after that.
        Alternatively, the method returns None to represent
        that no matching document was found, or that no update
        was applied (depending on the `return_document` parameter).

    Example:
        >>> my_coll.insert_one({"Marco": "Polo"})
        InsertOneResult(...)
        >>> my_coll.find_one_and_update(
        ...     {"Marco": {"$exists": True}},
        ...     {"$set": {"title": "Mr."}},
        ... )
        {'_id': 'a80106f2-...', 'Marco': 'Polo'}
        >>> my_coll.find_one_and_update(
        ...     {"title": "Mr."},
        ...     {"$inc": {"rank": 3}},
        ...     projection=["title", "rank"],
        ...     return_document=astrapy.constants.ReturnDocument.AFTER,
        ... )
        {'_id': 'a80106f2-...', 'title': 'Mr.', 'rank': 3}
        >>> my_coll.find_one_and_update(
        ...     {"name": "Johnny"},
        ...     {"$set": {"rank": 0}},
        ...     return_document=astrapy.constants.ReturnDocument.AFTER,
        ... )
        >>> # (returns None for no matches)
        >>> my_coll.find_one_and_update(
        ...     {"name": "Johnny"},
        ...     {"$set": {"rank": 0}},
        ...     upsert=True,
        ...     return_document=astrapy.constants.ReturnDocument.AFTER,
        ... )
        {'_id': 'cb4ef2ab-...', 'name': 'Johnny', 'rank': 0}
    """

    _sort = _collate_vector_to_sort(sort, vector, vectorize)
    options = {
        "returnDocument": return_document,
        "upsert": upsert,
    }
    logger.info(f"calling find_one_and_update on '{self.name}'")
    fo_response = self._astra_db_collection.find_one_and_update(
        update=update,
        filter=filter,
        projection=normalize_optional_projection(projection),
        sort=_sort,
        options=options,
        timeout_info=base_timeout_info(max_time_ms),
    )
    logger.info(f"finished calling find_one_and_update on '{self.name}'")
    if "document" in fo_response.get("data", {}):
        ret_document = fo_response.get("data", {}).get("document")
        if ret_document is None:
            return None
        else:
            return ret_document  # type: ignore[no-any-return]
    else:
        raise DataAPIFaultyResponseException(
            text="Faulty response from find_one_and_update API command.",
            raw_response=fo_response,
        )

def info(self) ‑> CollectionInfo

Information on the collection (name, location, database), in the form of a CollectionInfo object.

Not to be confused with the collection options method (related to the collection internal configuration).

Example

>>> my_coll.info().database_info.region
'eu-west-1'
>>> my_coll.info().full_name
'default_keyspace.my_v_collection'

Note

the returned CollectionInfo wraps, among other things, the database information: as such, calling this method triggers the same-named method of a Database object (which, in turn, performs a HTTP request to the DevOps API). See the documentation for Database.info() for more details.

Expand source code

def info(self) -> CollectionInfo:
    """
    Information on the collection (name, location, database), in the
    form of a CollectionInfo object.

    Not to be confused with the collection `options` method (related
    to the collection internal configuration).

    Example:
        >>> my_coll.info().database_info.region
        'eu-west-1'
        >>> my_coll.info().full_name
        'default_keyspace.my_v_collection'

    Note:
        the returned CollectionInfo wraps, among other things,
        the database information: as such, calling this method
        triggers the same-named method of a Database object (which, in turn,
        performs a HTTP request to the DevOps API).
        See the documentation for `Database.info()` for more details.
    """

    return CollectionInfo(
        database_info=self.database.info(),
        namespace=self.namespace,
        name=self.name,
        full_name=self.full_name,
    )

def insert_many(self, documents: Iterable[DocumentType], *, vectors: Optional[Iterable[Optional[VectorType]]] = None, vectorize: Optional[Iterable[Optional[str]]] = None, ordered: bool = True, chunk_size: Optional[int] = None, concurrency: Optional[int] = None, max_time_ms: Optional[int] = None) ‑> InsertManyResult

Insert a list of documents into the collection. This is not an atomic operation.

Args

documents

an iterable of dictionaries, each a document to insert. Documents may specify their _id field or leave it out, in which case it will be added automatically.

vectors

an optional list of vectors (as many vectors as the provided documents) to associate to the documents when inserting. Each vector is added to the corresponding document prior to insertion on database. The list can be a mixture of None and vectors, in which case some documents will not have a vector, unless it is specified in their "$vector" field already. Passing vectors this way is indeed equivalent to the "$vector" field of the documents, however the two are mutually exclusive.

vectorize

an optional list of strings to be made into as many vectors (one per document), if such a service is configured for the collection. Passing this parameter is equivalent to providing a $vectorize field in the documents themselves, however the two are mutually exclusive. For any given document, this parameter cannot coexist with the corresponding vector entry. NOTE: This feature is under current development.

ordered

if True (default), the insertions are processed sequentially. If False, they can occur in arbitrary order and possibly concurrently.

chunk_size

how many documents to include in a single API request. Exceeding the server maximum allowed value results in an error. Leave it unspecified (recommended) to use the system default.

concurrency

maximum number of concurrent requests to the API at a given time. It cannot be more than one for ordered insertions.

max_time_ms

a timeout, in milliseconds, for the operation.

Returns

an InsertManyResult object.

Examples

>>> my_coll.count_documents({}, upper_bound=10)
0
>>> my_coll.insert_many([{"a": 10}, {"a": 5}, {"b": [True, False, False]}])
InsertManyResult(raw_results=..., inserted_ids=['184bb06f-...', '...', '...'])
>>> my_coll.count_documents({}, upper_bound=100)
3
>>> my_coll.insert_many(
...     [{"seq": i} for i in range(50)],
...     ordered=False,
...     concurrency=5,
... )
InsertManyResult(raw_results=..., inserted_ids=[... ...])
>>> my_coll.count_documents({}, upper_bound=100)
53

The following are three equivalent statements:

>>> my_coll.insert_many(
...     [{"tag": "a"}, {"tag": "b"}],
...     vectors=[[1, 2], [3, 4]],
... )
InsertManyResult(...)
>>> my_coll.insert_many(
...     [{"tag": "a", "$vector": [1, 2]}, {"tag": "b"}],
...     vectors=[None, [3, 4]],
... )
InsertManyResult(...)
>>> my_coll.insert_many(
...     [
...         {"tag": "a", "$vector": [1, 2]},
...         {"tag": "b", "$vector": [3, 4]},
...     ]
... )
InsertManyResult(...)

Note

Unordered insertions are executed with some degree of concurrency, so it is usually better to prefer this mode unless the order in the document sequence is important.

Note

A failure mode for this command is related to certain faulty documents found among those to insert: a document may have the an _id already present on the collection, or its vector dimension may not match the collection setting.

For an ordered insertion, the method will raise an exception at the first such faulty document – nevertheless, all documents processed until then will end up being written to the database.

For unordered insertions, if the error stems from faulty documents the insertion proceeds until exhausting the input documents: then, an exception is raised – and all insertable documents will have been written to the database, including those "after" the troublesome ones.

If, on the other hand, there are errors not related to individual documents (such as a network connectivity error), the whole insert_many operation will stop in mid-way, an exception will be raised, and only a certain amount of the input documents will have made their way to the database.

Expand source code

@recast_method_sync
def insert_many(
    self,
    documents: Iterable[DocumentType],
    *,
    vectors: Optional[Iterable[Optional[VectorType]]] = None,
    vectorize: Optional[Iterable[Optional[str]]] = None,
    ordered: bool = True,
    chunk_size: Optional[int] = None,
    concurrency: Optional[int] = None,
    max_time_ms: Optional[int] = None,
) -> InsertManyResult:
    """
    Insert a list of documents into the collection.
    This is not an atomic operation.

    Args:
        documents: an iterable of dictionaries, each a document to insert.
            Documents may specify their `_id` field or leave it out, in which
            case it will be added automatically.
        vectors: an optional list of vectors (as many vectors as the provided
            documents) to associate to the documents when inserting.
            Each vector is added to the corresponding document prior to
            insertion on database. The list can be a mixture of None and vectors,
            in which case some documents will not have a vector, unless it is
            specified in their "$vector" field already.
            Passing vectors this way is indeed equivalent to the "$vector" field
            of the documents, however the two are mutually exclusive.
        vectorize: an optional list of strings to be made into as many vectors
            (one per document), if such a service is configured for the collection.
            Passing this parameter is equivalent to providing a `$vectorize`
            field in the documents themselves, however the two are mutually exclusive.
            For any given document, this parameter cannot coexist with the
            corresponding `vector` entry.
            NOTE: This feature is under current development.
        ordered: if True (default), the insertions are processed sequentially.
            If False, they can occur in arbitrary order and possibly concurrently.
        chunk_size: how many documents to include in a single API request.
            Exceeding the server maximum allowed value results in an error.
            Leave it unspecified (recommended) to use the system default.
        concurrency: maximum number of concurrent requests to the API at
            a given time. It cannot be more than one for ordered insertions.
        max_time_ms: a timeout, in milliseconds, for the operation.

    Returns:
        an InsertManyResult object.

    Examples:
        >>> my_coll.count_documents({}, upper_bound=10)
        0
        >>> my_coll.insert_many([{"a": 10}, {"a": 5}, {"b": [True, False, False]}])
        InsertManyResult(raw_results=..., inserted_ids=['184bb06f-...', '...', '...'])
        >>> my_coll.count_documents({}, upper_bound=100)
        3
        >>> my_coll.insert_many(
        ...     [{"seq": i} for i in range(50)],
        ...     ordered=False,
        ...     concurrency=5,
        ... )
        InsertManyResult(raw_results=..., inserted_ids=[... ...])
        >>> my_coll.count_documents({}, upper_bound=100)
        53

        # The following are three equivalent statements:
        >>> my_coll.insert_many(
        ...     [{"tag": "a"}, {"tag": "b"}],
        ...     vectors=[[1, 2], [3, 4]],
        ... )
        InsertManyResult(...)
        >>> my_coll.insert_many(
        ...     [{"tag": "a", "$vector": [1, 2]}, {"tag": "b"}],
        ...     vectors=[None, [3, 4]],
        ... )
        InsertManyResult(...)
        >>> my_coll.insert_many(
        ...     [
        ...         {"tag": "a", "$vector": [1, 2]},
        ...         {"tag": "b", "$vector": [3, 4]},
        ...     ]
        ... )
        InsertManyResult(...)

    Note:
        Unordered insertions are executed with some degree of concurrency,
        so it is usually better to prefer this mode unless the order in the
        document sequence is important.

    Note:
        A failure mode for this command is related to certain faulty documents
        found among those to insert: a document may have the an `_id` already
        present on the collection, or its vector dimension may not
        match the collection setting.

        For an ordered insertion, the method will raise an exception at
        the first such faulty document -- nevertheless, all documents processed
        until then will end up being written to the database.

        For unordered insertions, if the error stems from faulty documents
        the insertion proceeds until exhausting the input documents: then,
        an exception is raised -- and all insertable documents will have been
        written to the database, including those "after" the troublesome ones.

        If, on the other hand, there are errors not related to individual
        documents (such as a network connectivity error), the whole
        `insert_many` operation will stop in mid-way, an exception will be raised,
        and only a certain amount of the input documents will
        have made their way to the database.
    """

    if concurrency is None:
        if ordered:
            _concurrency = 1
        else:
            _concurrency = DEFAULT_INSERT_MANY_CONCURRENCY
    else:
        _concurrency = concurrency
    if _concurrency > 1 and ordered:
        raise ValueError("Cannot run ordered insert_many concurrently.")
    if chunk_size is None:
        _chunk_size = MAX_INSERT_NUM_DOCUMENTS
    else:
        _chunk_size = chunk_size
    _documents = _collate_vectors_to_documents(documents, vectors, vectorize)
    logger.info(f"inserting {len(_documents)} documents in '{self.name}'")
    raw_results: List[Dict[str, Any]] = []
    timeout_manager = MultiCallTimeoutManager(overall_max_time_ms=max_time_ms)
    if ordered:
        options = {"ordered": True}
        inserted_ids: List[Any] = []
        for i in range(0, len(_documents), _chunk_size):
            logger.info(f"inserting a chunk of documents in '{self.name}'")
            chunk_response = self._astra_db_collection.insert_many(
                documents=_documents[i : i + _chunk_size],
                options=options,
                partial_failures_allowed=True,
                timeout_info=timeout_manager.remaining_timeout_info(),
            )
            logger.info(f"finished inserting a chunk of documents in '{self.name}'")
            # accumulate the results in this call
            chunk_inserted_ids = (chunk_response.get("status") or {}).get(
                "insertedIds", []
            )
            inserted_ids += chunk_inserted_ids
            raw_results += [chunk_response]
            # if errors, quit early
            if chunk_response.get("errors", []):
                partial_result = InsertManyResult(
                    raw_results=raw_results,
                    inserted_ids=inserted_ids,
                )
                raise InsertManyException.from_response(
                    command=None,
                    raw_response=chunk_response,
                    partial_result=partial_result,
                )

        # return
        full_result = InsertManyResult(
            raw_results=raw_results,
            inserted_ids=inserted_ids,
        )
        logger.info(
            f"finished inserting {len(_documents)} documents in '{self.name}'"
        )
        return full_result

    else:
        # unordered: concurrent or not, do all of them and parse the results
        options = {"ordered": False}
        if _concurrency > 1:
            with ThreadPoolExecutor(max_workers=_concurrency) as executor:

                def _chunk_insertor(
                    document_chunk: List[Dict[str, Any]]
                ) -> Dict[str, Any]:
                    logger.info(f"inserting a chunk of documents in '{self.name}'")
                    im_response = self._astra_db_collection.insert_many(
                        documents=document_chunk,
                        options=options,
                        partial_failures_allowed=True,
                        timeout_info=timeout_manager.remaining_timeout_info(),
                    )
                    logger.info(
                        f"finished inserting a chunk of documents in '{self.name}'"
                    )
                    return im_response

                raw_results = list(
                    executor.map(
                        _chunk_insertor,
                        (
                            _documents[i : i + _chunk_size]
                            for i in range(0, len(_documents), _chunk_size)
                        ),
                    )
                )
        else:
            for i in range(0, len(_documents), _chunk_size):
                logger.info(f"inserting a chunk of documents in '{self.name}'")
                raw_results.append(
                    self._astra_db_collection.insert_many(
                        _documents[i : i + _chunk_size],
                        options=options,
                        partial_failures_allowed=True,
                        timeout_info=timeout_manager.remaining_timeout_info(),
                    )
                )
                logger.info(
                    f"finished inserting a chunk of documents in '{self.name}'"
                )
        # recast raw_results
        inserted_ids = [
            inserted_id
            for chunk_response in raw_results
            for inserted_id in (chunk_response.get("status") or {}).get(
                "insertedIds", []
            )
        ]

        # check-raise
        if any(
            [chunk_response.get("errors", []) for chunk_response in raw_results]
        ):
            partial_result = InsertManyResult(
                raw_results=raw_results,
                inserted_ids=inserted_ids,
            )
            raise InsertManyException.from_responses(
                commands=[None for _ in raw_results],
                raw_responses=raw_results,
                partial_result=partial_result,
            )

        # return
        full_result = InsertManyResult(
            raw_results=raw_results,
            inserted_ids=inserted_ids,
        )
        logger.info(
            f"finished inserting {len(_documents)} documents in '{self.name}'"
        )
        return full_result

def insert_one(self, document: DocumentType, *, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, max_time_ms: Optional[int] = None) ‑> InsertOneResult

Insert a single document in the collection in an atomic operation.

Args

document

the dictionary expressing the document to insert. The _id field of the document can be left out, in which case it will be created automatically.

vector

a vector (a list of numbers appropriate for the collection) for the document. Passing this parameter is equivalent to providing the vector in the "$vector" field of the document itself, however the two are mutually exclusive.

vectorize

a string to be made into a vector, if such a service is configured for the collection. Passing this parameter is equivalent to providing a $vectorize field in the document itself, however the two are mutually exclusive. Moreover, this parameter cannot coexist with vector. NOTE: This feature is under current development.

max_time_ms

a timeout, in milliseconds, for the underlying HTTP request.

Returns

an InsertOneResult object.

Examples

>>> my_coll.count_documents({}, upper_bound=10)
0
>>> my_coll.insert_one(
...     {
...         "age": 30,
...         "name": "Smith",
...         "food": ["pear", "peach"],
...         "likes_fruit": True,
...     },
... )
InsertOneResult(raw_results=..., inserted_id='ed4587a4-...-...-...')
>>> my_coll.insert_one({"_id": "user-123", "age": 50, "name": "Maccio"})
InsertOneResult(raw_results=..., inserted_id='user-123')
>>> my_coll.count_documents({}, upper_bound=10)
2

>>> my_coll.insert_one({"tag": v"}, vector=[10, 11])
InsertOneResult(...)

Note

If an _id is explicitly provided, which corresponds to a document that exists already in the collection, an error is raised and the insertion fails.

Expand source code

@recast_method_sync
def insert_one(
    self,
    document: DocumentType,
    *,
    vector: Optional[VectorType] = None,
    vectorize: Optional[str] = None,
    max_time_ms: Optional[int] = None,
) -> InsertOneResult:
    """
    Insert a single document in the collection in an atomic operation.

    Args:
        document: the dictionary expressing the document to insert.
            The `_id` field of the document can be left out, in which
            case it will be created automatically.
        vector: a vector (a list of numbers appropriate for the collection)
            for the document. Passing this parameter is equivalent to
            providing the vector in the "$vector" field of the document itself,
            however the two are mutually exclusive.
        vectorize: a string to be made into a vector, if such a service
            is configured for the collection. Passing this parameter is
            equivalent to providing a `$vectorize` field in the document itself,
            however the two are mutually exclusive.
            Moreover, this parameter cannot coexist with `vector`.
            NOTE: This feature is under current development.
        max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

    Returns:
        an InsertOneResult object.

    Examples:
        >>> my_coll.count_documents({}, upper_bound=10)
        0
        >>> my_coll.insert_one(
        ...     {
        ...         "age": 30,
        ...         "name": "Smith",
        ...         "food": ["pear", "peach"],
        ...         "likes_fruit": True,
        ...     },
        ... )
        InsertOneResult(raw_results=..., inserted_id='ed4587a4-...-...-...')
        >>> my_coll.insert_one({"_id": "user-123", "age": 50, "name": "Maccio"})
        InsertOneResult(raw_results=..., inserted_id='user-123')
        >>> my_coll.count_documents({}, upper_bound=10)
        2

        >>> my_coll.insert_one({"tag": v"}, vector=[10, 11])
        InsertOneResult(...)

    Note:
        If an `_id` is explicitly provided, which corresponds to a document
        that exists already in the collection, an error is raised and
        the insertion fails.
    """

    _document = _collate_vector_to_document(document, vector, vectorize)
    logger.info(f"inserting one document in '{self.name}'")
    io_response = self._astra_db_collection.insert_one(
        _document,
        timeout_info=base_timeout_info(max_time_ms),
    )
    logger.info(f"finished inserting one document in '{self.name}'")
    if "insertedIds" in io_response.get("status", {}):
        if io_response["status"]["insertedIds"]:
            inserted_id = io_response["status"]["insertedIds"][0]
            return InsertOneResult(
                raw_results=[io_response],
                inserted_id=inserted_id,
            )
        else:
            raise DataAPIFaultyResponseException(
                text="Faulty response from insert_one API command.",
                raw_response=io_response,
            )
    else:
        raise DataAPIFaultyResponseException(
            text="Faulty response from insert_one API command.",
            raw_response=io_response,
        )

def options(self, *, max_time_ms: Optional[int] = None) ‑> CollectionOptions

Get the collection options, i.e. its configuration as read from the database.

The method issues a request to the Data API each time is invoked, without caching mechanisms: this ensures up-to-date information for usages such as real-time collection validation by the application.

Args

max_time_ms

a timeout, in milliseconds, for the underlying HTTP request.

Returns

a CollectionOptions instance describing the collection. (See also the database list_collections method.)

Example

>>> my_coll.options()
CollectionOptions(vector=CollectionVectorOptions(dimension=3, metric='cosine'))

Expand source code

def options(self, *, max_time_ms: Optional[int] = None) -> CollectionOptions:
    """
    Get the collection options, i.e. its configuration as read from the database.

    The method issues a request to the Data API each time is invoked,
    without caching mechanisms: this ensures up-to-date information
    for usages such as real-time collection validation by the application.

    Args:
        max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

    Returns:
        a CollectionOptions instance describing the collection.
        (See also the database `list_collections` method.)

    Example:
        >>> my_coll.options()
        CollectionOptions(vector=CollectionVectorOptions(dimension=3, metric='cosine'))
    """

    logger.info(f"getting collections in search of '{self.name}'")
    self_descriptors = [
        coll_desc
        for coll_desc in self.database.list_collections(max_time_ms=max_time_ms)
        if coll_desc.name == self.name
    ]
    logger.info(f"finished getting collections in search of '{self.name}'")
    if self_descriptors:
        return self_descriptors[0].options  # type: ignore[no-any-return]
    else:
        raise CollectionNotFoundException(
            text=f"Collection {self.namespace}.{self.name} not found.",
            namespace=self.namespace,
            collection_name=self.name,
        )

def replace_one(self, filter: Dict[str, Any], replacement: DocumentType, *, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, sort: Optional[SortType] = None, upsert: bool = False, max_time_ms: Optional[int] = None) ‑> UpdateResult

Replace a single document on the collection with a new one, optionally inserting a new one if no match is found.

Args

filter

a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$le": 100}} {"$and": [{"name": "John"}, {"price": {"$le": 100}}]} See the Data API documentation for the full set of operators.

replacement

the new document to write into the collection.

vector

a suitable vector, i.e. a list of float numbers of the appropriate dimensionality, to use vector search (i.e. ANN, or "approximate nearest-neighbours" search), as the sorting criterion. In this way, the matched document (if any) will be the one that is most similar to the provided vector. This parameter cannot be used together with sort. See the find method for more details on this parameter.

vectorize

a string to be made into a vector to perform vector search. This can be supplied in (exclusive) alternative to vector, provided such a service is configured for the collection, and achieves the same effect. NOTE: This feature is under current development.

sort

with this dictionary parameter one can control the sorting order of the documents matching the filter, effectively determining what document will come first and hence be the replaced one. See the find method for more on sorting.

upsert

this parameter controls the behavior in absence of matches. If True, replacement is inserted as a new document if no matches are found on the collection. If False, the operation silently does nothing in case of no matches.

max_time_ms

a timeout, in milliseconds, for the underlying HTTP request.

Returns

an UpdateResult object summarizing the outcome of the replace operation.

Example

>>> my_coll.insert_one({"Marco": "Polo"})
InsertOneResult(...)
>>> my_coll.replace_one({"Marco": {"$exists": True}}, {"Buda": "Pest"})
UpdateResult(raw_results=..., update_info={'n': 1, 'updatedExisting': True, 'ok': 1.0, 'nModified': 1})
>>> my_coll.find_one({"Buda": "Pest"})
{'_id': '8424905a-...', 'Buda': 'Pest'}
>>> my_coll.replace_one({"Mirco": {"$exists": True}}, {"Oh": "yeah?"})
UpdateResult(raw_results=..., update_info={'n': 0, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0})
>>> my_coll.replace_one({"Mirco": {"$exists": True}}, {"Oh": "yeah?"}, upsert=True)
UpdateResult(raw_results=..., update_info={'n': 1, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0, 'upserted': '931b47d6-...'})

Expand source code

@recast_method_sync
def replace_one(
    self,
    filter: Dict[str, Any],
    replacement: DocumentType,
    *,
    vector: Optional[VectorType] = None,
    vectorize: Optional[str] = None,
    sort: Optional[SortType] = None,
    upsert: bool = False,
    max_time_ms: Optional[int] = None,
) -> UpdateResult:
    """
    Replace a single document on the collection with a new one,
    optionally inserting a new one if no match is found.

    Args:
        filter: a predicate expressed as a dictionary according to the
            Data API filter syntax. Examples are:
                {}
                {"name": "John"}
                {"price": {"$le": 100}}
                {"$and": [{"name": "John"}, {"price": {"$le": 100}}]}
            See the Data API documentation for the full set of operators.
        replacement: the new document to write into the collection.
        vector: a suitable vector, i.e. a list of float numbers of the appropriate
            dimensionality, to use vector search (i.e. ANN,
            or "approximate nearest-neighbours" search), as the sorting criterion.
            In this way, the matched document (if any) will be the one
            that is most similar to the provided vector.
            This parameter cannot be used together with `sort`.
            See the `find` method for more details on this parameter.
        vectorize: a string to be made into a vector to perform vector search.
            This can be supplied in (exclusive) alternative to `vector`,
            provided such a service is configured for the collection,
            and achieves the same effect.
            NOTE: This feature is under current development.
        sort: with this dictionary parameter one can control the sorting
            order of the documents matching the filter, effectively
            determining what document will come first and hence be the
            replaced one. See the `find` method for more on sorting.
        upsert: this parameter controls the behavior in absence of matches.
            If True, `replacement` is inserted as a new document
            if no matches are found on the collection. If False,
            the operation silently does nothing in case of no matches.
        max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

    Returns:
        an UpdateResult object summarizing the outcome of the replace operation.

    Example:
        >>> my_coll.insert_one({"Marco": "Polo"})
        InsertOneResult(...)
        >>> my_coll.replace_one({"Marco": {"$exists": True}}, {"Buda": "Pest"})
        UpdateResult(raw_results=..., update_info={'n': 1, 'updatedExisting': True, 'ok': 1.0, 'nModified': 1})
        >>> my_coll.find_one({"Buda": "Pest"})
        {'_id': '8424905a-...', 'Buda': 'Pest'}
        >>> my_coll.replace_one({"Mirco": {"$exists": True}}, {"Oh": "yeah?"})
        UpdateResult(raw_results=..., update_info={'n': 0, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0})
        >>> my_coll.replace_one({"Mirco": {"$exists": True}}, {"Oh": "yeah?"}, upsert=True)
        UpdateResult(raw_results=..., update_info={'n': 1, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0, 'upserted': '931b47d6-...'})
    """

    _sort = _collate_vector_to_sort(sort, vector, vectorize)
    options = {
        "upsert": upsert,
    }
    logger.info(f"calling find_one_and_replace on '{self.name}'")
    fo_response = self._astra_db_collection.find_one_and_replace(
        replacement=replacement,
        filter=filter,
        sort=_sort,
        options=options,
        timeout_info=base_timeout_info(max_time_ms),
    )
    logger.info(f"finished calling find_one_and_replace on '{self.name}'")
    if "document" in fo_response.get("data", {}):
        fo_status = fo_response.get("status") or {}
        _update_info = _prepare_update_info([fo_status])
        return UpdateResult(
            raw_results=[fo_response],
            update_info=_update_info,
        )
    else:
        raise DataAPIFaultyResponseException(
            text="Faulty response from find_one_and_replace API command.",
            raw_response=fo_response,
        )

def set_caller(self, caller_name: Optional[str] = None, caller_version: Optional[str] = None) ‑> None

Set a new identity for the application/framework on behalf of which the Data API calls are performed (the "caller").

Args

caller_name

name of the application, or framework, on behalf of which the Data API calls are performed. This ends up in the request user-agent.

caller_version

version of the caller.

Example

>>> my_coll.set_caller(caller_name="the_caller", caller_version="0.1.0")

Expand source code

def set_caller(
    self,
    caller_name: Optional[str] = None,
    caller_version: Optional[str] = None,
) -> None:
    """
    Set a new identity for the application/framework on behalf of which
    the Data API calls are performed (the "caller").

    Args:
        caller_name: name of the application, or framework, on behalf of which
            the Data API calls are performed. This ends up in the request user-agent.
        caller_version: version of the caller.

    Example:
        >>> my_coll.set_caller(caller_name="the_caller", caller_version="0.1.0")
    """

    logger.info(f"setting caller to {caller_name}/{caller_version}")
    self._astra_db_collection.set_caller(
        caller_name=caller_name,
        caller_version=caller_version,
    )

def to_async(self, *, database: Optional[AsyncDatabase] = None, name: Optional[str] = None, namespace: Optional[str] = None, caller_name: Optional[str] = None, caller_version: Optional[str] = None) ‑> AsyncCollection

Create an AsyncCollection from this one. Save for the arguments explicitly provided as overrides, everything else is kept identical to this collection in the copy (the database is converted into an async object).

Args

database

an AsyncDatabase object, instantiated earlier. This represents the database the new collection belongs to.

name

the collection name. This parameter should match an existing collection on the database.

namespace

this is the namespace to which the collection belongs. If not specified, the database's working namespace is used.

caller_name

name of the application, or framework, on behalf of which the Data API calls are performed. This ends up in the request user-agent.

caller_version

version of the caller.

Returns

the new copy, an AsyncCollection instance.

Example

>>> asyncio.run(my_coll.to_async().count_documents({},upper_bound=100))
77

Expand source code

def to_async(
    self,
    *,
    database: Optional[AsyncDatabase] = None,
    name: Optional[str] = None,
    namespace: Optional[str] = None,
    caller_name: Optional[str] = None,
    caller_version: Optional[str] = None,
) -> AsyncCollection:
    """
    Create an AsyncCollection from this one. Save for the arguments
    explicitly provided as overrides, everything else is kept identical
    to this collection in the copy (the database is converted into
    an async object).

    Args:
        database: an AsyncDatabase object, instantiated earlier.
            This represents the database the new collection belongs to.
        name: the collection name. This parameter should match an existing
            collection on the database.
        namespace: this is the namespace to which the collection belongs.
            If not specified, the database's working namespace is used.
        caller_name: name of the application, or framework, on behalf of which
            the Data API calls are performed. This ends up in the request user-agent.
        caller_version: version of the caller.

    Returns:
        the new copy, an AsyncCollection instance.

    Example:
        >>> asyncio.run(my_coll.to_async().count_documents({},upper_bound=100))
        77
    """

    return AsyncCollection(
        database=database or self.database.to_async(),
        name=name or self.name,
        namespace=namespace or self.namespace,
        caller_name=caller_name or self._astra_db_collection.caller_name,
        caller_version=caller_version or self._astra_db_collection.caller_version,
    )

def update_many(self, filter: Dict[str, Any], update: Dict[str, Any], *, upsert: bool = False, max_time_ms: Optional[int] = None) ‑> UpdateResult

Apply an update operations to all documents matching a condition, optionally inserting one documents in absence of matches.

Args

filter

a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$le": 100}} {"$and": [{"name": "John"}, {"price": {"$le": 100}}]} See the Data API documentation for the full set of operators.

update

the update prescription to apply to the documents, expressed as a dictionary as per Data API syntax. Examples are: {"$set": {"field": "value}} {"$inc": {"counter": 10}} {"$unset": {"field": ""}} See the Data API documentation for the full syntax.

upsert

this parameter controls the behavior in absence of matches. If True, a single new document (resulting from applying update to an empty document) is inserted if no matches are found on the collection. If False, the operation silently does nothing in case of no matches.

max_time_ms

a timeout, in milliseconds, for the operation.

Returns

an UpdateResult object summarizing the outcome of the update operation.

Example

>>> my_coll.insert_many([{"c": "red"}, {"c": "green"}, {"c": "blue"}])
InsertManyResult(...)
>>> my_coll.update_many({"c": {"$ne": "green"}}, {"$set": {"nongreen": True}})
UpdateResult(raw_results=..., update_info={'n': 2, 'updatedExisting': True, 'ok': 1.0, 'nModified': 2})
>>> my_coll.update_many({"c": "orange"}, {"$set": {"is_also_fruit": True}})
UpdateResult(raw_results=..., update_info={'n': 0, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0})
>>> my_coll.update_many(
...     {"c": "orange"},
...     {"$set": {"is_also_fruit": True}},
...     upsert=True,
... )
UpdateResult(raw_results=..., update_info={'n': 1, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0, 'upserted': '46643050-...'})

Note

Similarly to the case of find (see its docstring for more details), running this command while, at the same time, another process is inserting new documents which match the filter of the update_many can result in an unpredictable fraction of these documents being updated. In other words, it cannot be easily predicted whether a given newly-inserted document will be picked up by the update_many command or not.

Expand source code

@recast_method_sync
def update_many(
    self,
    filter: Dict[str, Any],
    update: Dict[str, Any],
    *,
    upsert: bool = False,
    max_time_ms: Optional[int] = None,
) -> UpdateResult:
    """
    Apply an update operations to all documents matching a condition,
    optionally inserting one documents in absence of matches.

    Args:
        filter: a predicate expressed as a dictionary according to the
            Data API filter syntax. Examples are:
                {}
                {"name": "John"}
                {"price": {"$le": 100}}
                {"$and": [{"name": "John"}, {"price": {"$le": 100}}]}
            See the Data API documentation for the full set of operators.
        update: the update prescription to apply to the documents, expressed
            as a dictionary as per Data API syntax. Examples are:
                {"$set": {"field": "value}}
                {"$inc": {"counter": 10}}
                {"$unset": {"field": ""}}
            See the Data API documentation for the full syntax.
        upsert: this parameter controls the behavior in absence of matches.
            If True, a single new document (resulting from applying `update`
            to an empty document) is inserted if no matches are found on
            the collection. If False, the operation silently does nothing
            in case of no matches.
        max_time_ms: a timeout, in milliseconds, for the operation.

    Returns:
        an UpdateResult object summarizing the outcome of the update operation.

    Example:
        >>> my_coll.insert_many([{"c": "red"}, {"c": "green"}, {"c": "blue"}])
        InsertManyResult(...)
        >>> my_coll.update_many({"c": {"$ne": "green"}}, {"$set": {"nongreen": True}})
        UpdateResult(raw_results=..., update_info={'n': 2, 'updatedExisting': True, 'ok': 1.0, 'nModified': 2})
        >>> my_coll.update_many({"c": "orange"}, {"$set": {"is_also_fruit": True}})
        UpdateResult(raw_results=..., update_info={'n': 0, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0})
        >>> my_coll.update_many(
        ...     {"c": "orange"},
        ...     {"$set": {"is_also_fruit": True}},
        ...     upsert=True,
        ... )
        UpdateResult(raw_results=..., update_info={'n': 1, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0, 'upserted': '46643050-...'})

    Note:
        Similarly to the case of `find` (see its docstring for more details),
        running this command while, at the same time, another process is
        inserting new documents which match the filter of the `update_many`
        can result in an unpredictable fraction of these documents being updated.
        In other words, it cannot be easily predicted whether a given
        newly-inserted document will be picked up by the update_many command or not.
    """

    base_options = {
        "upsert": upsert,
    }
    page_state_options: Dict[str, str] = {}
    um_responses: List[Dict[str, Any]] = []
    um_statuses: List[Dict[str, Any]] = []
    must_proceed = True
    timeout_manager = MultiCallTimeoutManager(overall_max_time_ms=max_time_ms)
    logger.info(f"starting update_many on '{self.name}'")
    while must_proceed:
        options = {**base_options, **page_state_options}
        logger.info(f"calling update_many on '{self.name}'")
        this_um_response = self._astra_db_collection.update_many(
            update=update,
            filter=filter,
            options=options,
            timeout_info=timeout_manager.remaining_timeout_info(),
        )
        logger.info(f"finished calling update_many on '{self.name}'")
        this_um_status = this_um_response.get("status") or {}
        #
        # if errors, quit early
        if this_um_response.get("errors", []):
            partial_update_info = _prepare_update_info(um_statuses)
            partial_result = UpdateResult(
                raw_results=um_responses,
                update_info=partial_update_info,
            )
            all_um_responses = um_responses + [this_um_response]
            raise UpdateManyException.from_responses(
                commands=[None for _ in all_um_responses],
                raw_responses=all_um_responses,
                partial_result=partial_result,
            )
        else:
            if "status" not in this_um_response:
                raise DataAPIFaultyResponseException(
                    text="Faulty response from update_many API command.",
                    raw_response=this_um_response,
                )
            um_responses.append(this_um_response)
            um_statuses.append(this_um_status)
            next_page_state = this_um_status.get("nextPageState")
            if next_page_state is not None:
                must_proceed = True
                page_state_options = {"pageState": next_page_state}
            else:
                must_proceed = False
                page_state_options = {}

    update_info = _prepare_update_info(um_statuses)
    logger.info(f"finished update_many on '{self.name}'")
    return UpdateResult(
        raw_results=um_responses,
        update_info=update_info,
    )

def update_one(self, filter: Dict[str, Any], update: Dict[str, Any], *, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, sort: Optional[SortType] = None, upsert: bool = False, max_time_ms: Optional[int] = None) ‑> UpdateResult

Update a single document on the collection as requested, optionally inserting a new one if no match is found.

Args

filter

a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$le": 100}} {"$and": [{"name": "John"}, {"price": {"$le": 100}}]} See the Data API documentation for the full set of operators.

update

the update prescription to apply to the document, expressed as a dictionary as per Data API syntax. Examples are: {"$set": {"field": "value}} {"$inc": {"counter": 10}} {"$unset": {"field": ""}} See the Data API documentation for the full syntax.

vector

a suitable vector, i.e. a list of float numbers of the appropriate dimensionality, to use vector search (i.e. ANN, or "approximate nearest-neighbours" search), as the sorting criterion. In this way, the matched document (if any) will be the one that is most similar to the provided vector. This parameter cannot be used together with sort. See the find method for more details on this parameter.

vectorize

a string to be made into a vector to perform vector search. This can be supplied in (exclusive) alternative to vector, provided such a service is configured for the collection, and achieves the same effect. NOTE: This feature is under current development.

sort

with this dictionary parameter one can control the sorting order of the documents matching the filter, effectively determining what document will come first and hence be the updated one. See the find method for more on sorting.

upsert

this parameter controls the behavior in absence of matches. If True, a new document (resulting from applying the update to an empty document) is inserted if no matches are found on the collection. If False, the operation silently does nothing in case of no matches.

max_time_ms

a timeout, in milliseconds, for the underlying HTTP request.

Returns

an UpdateResult object summarizing the outcome of the update operation.

Example

>>> my_coll.insert_one({"Marco": "Polo"})
InsertOneResult(...)
>>> my_coll.update_one({"Marco": {"$exists": True}}, {"$inc": {"rank": 3}})
UpdateResult(raw_results=..., update_info={'n': 1, 'updatedExisting': True, 'ok': 1.0, 'nModified': 1})
>>> my_coll.update_one({"Mirko": {"$exists": True}}, {"$inc": {"rank": 3}})
UpdateResult(raw_results=..., update_info={'n': 0, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0})
>>> my_coll.update_one({"Mirko": {"$exists": True}}, {"$inc": {"rank": 3}}, upsert=True)
UpdateResult(raw_results=..., update_info={'n': 1, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0, 'upserted': '2a45ff60-...'})

Expand source code

@recast_method_sync
def update_one(
    self,
    filter: Dict[str, Any],
    update: Dict[str, Any],
    *,
    vector: Optional[VectorType] = None,
    vectorize: Optional[str] = None,
    sort: Optional[SortType] = None,
    upsert: bool = False,
    max_time_ms: Optional[int] = None,
) -> UpdateResult:
    """
    Update a single document on the collection as requested,
    optionally inserting a new one if no match is found.

    Args:
        filter: a predicate expressed as a dictionary according to the
            Data API filter syntax. Examples are:
                {}
                {"name": "John"}
                {"price": {"$le": 100}}
                {"$and": [{"name": "John"}, {"price": {"$le": 100}}]}
            See the Data API documentation for the full set of operators.
        update: the update prescription to apply to the document, expressed
            as a dictionary as per Data API syntax. Examples are:
                {"$set": {"field": "value}}
                {"$inc": {"counter": 10}}
                {"$unset": {"field": ""}}
            See the Data API documentation for the full syntax.
        vector: a suitable vector, i.e. a list of float numbers of the appropriate
            dimensionality, to use vector search (i.e. ANN,
            or "approximate nearest-neighbours" search), as the sorting criterion.
            In this way, the matched document (if any) will be the one
            that is most similar to the provided vector.
            This parameter cannot be used together with `sort`.
            See the `find` method for more details on this parameter.
        vectorize: a string to be made into a vector to perform vector search.
            This can be supplied in (exclusive) alternative to `vector`,
            provided such a service is configured for the collection,
            and achieves the same effect.
            NOTE: This feature is under current development.
        sort: with this dictionary parameter one can control the sorting
            order of the documents matching the filter, effectively
            determining what document will come first and hence be the
            updated one. See the `find` method for more on sorting.
        upsert: this parameter controls the behavior in absence of matches.
            If True, a new document (resulting from applying the `update`
            to an empty document) is inserted if no matches are found on
            the collection. If False, the operation silently does nothing
            in case of no matches.
        max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

    Returns:
        an UpdateResult object summarizing the outcome of the update operation.

    Example:
        >>> my_coll.insert_one({"Marco": "Polo"})
        InsertOneResult(...)
        >>> my_coll.update_one({"Marco": {"$exists": True}}, {"$inc": {"rank": 3}})
        UpdateResult(raw_results=..., update_info={'n': 1, 'updatedExisting': True, 'ok': 1.0, 'nModified': 1})
        >>> my_coll.update_one({"Mirko": {"$exists": True}}, {"$inc": {"rank": 3}})
        UpdateResult(raw_results=..., update_info={'n': 0, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0})
        >>> my_coll.update_one({"Mirko": {"$exists": True}}, {"$inc": {"rank": 3}}, upsert=True)
        UpdateResult(raw_results=..., update_info={'n': 1, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0, 'upserted': '2a45ff60-...'})
    """

    _sort = _collate_vector_to_sort(sort, vector, vectorize)
    options = {
        "upsert": upsert,
    }
    logger.info(f"calling find_one_and_update on '{self.name}'")
    fo_response = self._astra_db_collection.find_one_and_update(
        update=update,
        sort=_sort,
        filter=filter,
        options=options,
        timeout_info=base_timeout_info(max_time_ms),
    )
    logger.info(f"finished calling find_one_and_update on '{self.name}'")
    if "document" in fo_response.get("data", {}):
        fo_status = fo_response.get("status") or {}
        _update_info = _prepare_update_info([fo_status])
        return UpdateResult(
            raw_results=[fo_response],
            update_info=_update_info,
        )
    else:
        raise DataAPIFaultyResponseException(
            text="Faulty response from find_one_and_update API command.",
            raw_response=fo_response,
        )

def with_options(self, *, name: Optional[str] = None, caller_name: Optional[str] = None, caller_version: Optional[str] = None) ‑> Collection

Create a clone of this collection with some changed attributes.

Args

name

the name of the collection. This parameter is useful to quickly spawn Collection instances each pointing to a different collection existing in the same namespace.

caller_name

name of the application, or framework, on behalf of which the Data API calls are performed. This ends up in the request user-agent.

caller_version

version of the caller.

Returns

a new Collection instance.

Example

>>> my_other_coll = my_coll.with_options(
...     name="the_other_coll",
...     caller_name="caller_identity",
... )

Expand source code

def with_options(
    self,
    *,
    name: Optional[str] = None,
    caller_name: Optional[str] = None,
    caller_version: Optional[str] = None,
) -> Collection:
    """
    Create a clone of this collection with some changed attributes.

    Args:
        name: the name of the collection. This parameter is useful to
            quickly spawn Collection instances each pointing to a different
            collection existing in the same namespace.
        caller_name: name of the application, or framework, on behalf of which
            the Data API calls are performed. This ends up in the request user-agent.
        caller_version: version of the caller.

    Returns:
        a new Collection instance.

    Example:
        >>> my_other_coll = my_coll.with_options(
        ...     name="the_other_coll",
        ...     caller_name="caller_identity",
        ... )
    """

    return self._copy(
        name=name,
        caller_name=caller_name,
        caller_version=caller_version,
    )

>>> from astrapy import DataAPIClient
>>> my_client = DataAPIClient("AstraCS:...")
>>> my_db0 = my_client.get_database_by_api_endpoint(
...     "https://01234567-....apps.astra.datastax.com"
... )
>>> my_coll = my_db0.create_collection("movies", dimension=512)
>>> my_coll.insert_one({"title": "The Title"}, vector=...)
>>> my_db1 = my_client.get_database("01234567-...")
>>> my_db2 = my_client.get_database("01234567-...", region="us-east1")
>>> my_adm0 = my_client.get_admin()
>>> my_adm1 = my_client.get_admin(token=more_powerful_token_override)
>>> database_list = my_adm0.list_databases()

class DataAPIClient:
    """
    A client for using the Data API. This is the main entry point and sits
    at the top of the conceptual "client -> database -> collection" hierarchy.

    The client is created by passing a suitable Access Token. Starting from the
    client:
        - databases (Database and AsyncDatabase) are created for working with data
        - AstraDBAdmin objects can be created for admin-level work

    Args:
        token: an Access Token to the database. Example: `"AstraCS:xyz..."`.
        environment: a string representing the target Astra environment.
            It can be left unspecified for the default value of `Environment.PROD`;
            other values are `Environment.DEV` and `Environment.TEST`.
        caller_name: name of the application, or framework, on behalf of which
            the Data API and DevOps API calls are performed. This ends up in
            the request user-agent.
        caller_version: version of the caller.

    Example:
        >>> from astrapy import DataAPIClient
        >>> my_client = DataAPIClient("AstraCS:...")
        >>> my_db0 = my_client.get_database_by_api_endpoint(
        ...     "https://01234567-....apps.astra.datastax.com"
        ... )
        >>> my_coll = my_db0.create_collection("movies", dimension=512)
        >>> my_coll.insert_one({"title": "The Title"}, vector=...)
        >>> my_db1 = my_client.get_database("01234567-...")
        >>> my_db2 = my_client.get_database("01234567-...", region="us-east1")
        >>> my_adm0 = my_client.get_admin()
        >>> my_adm1 = my_client.get_admin(token=more_powerful_token_override)
        >>> database_list = my_adm0.list_databases()
    """

    def __init__(
        self,
        token: str,
        *,
        environment: Optional[str] = None,
        caller_name: Optional[str] = None,
        caller_version: Optional[str] = None,
    ) -> None:
        self.token = token
        self.environment = environment or Environment.PROD
        self._caller_name = caller_name
        self._caller_version = caller_version

    def __repr__(self) -> str:
        env_desc: str
        if self.environment == Environment.PROD:
            env_desc = ""
        else:
            env_desc = f', environment="{self.environment}"'
        return f'{self.__class__.__name__}("{self.token[:12]}..."{env_desc})'

    def __eq__(self, other: Any) -> bool:
        if isinstance(other, DataAPIClient):
            return all(
                [
                    self.token == other.token,
                    self.environment == other.environment,
                    self._caller_name == other._caller_name,
                    self._caller_version == other._caller_version,
                ]
            )
        else:
            return False

    def __getitem__(self, database_id_or_api_endpoint: str) -> Database:
        if re.match(database_id_matcher, database_id_or_api_endpoint):
            return self.get_database(database_id_or_api_endpoint)
        elif re.match(api_endpoint_parser, database_id_or_api_endpoint):
            return self.get_database_by_api_endpoint(database_id_or_api_endpoint)
        else:
            raise ValueError(
                "The provided input does not look like either a database ID "
                f"or an API endpoint ('{database_id_or_api_endpoint}')."
            )

    def _copy(
        self,
        *,
        token: Optional[str] = None,
        environment: Optional[str] = None,
        caller_name: Optional[str] = None,
        caller_version: Optional[str] = None,
    ) -> DataAPIClient:
        return DataAPIClient(
            token=token or self.token,
            environment=environment or self.environment,
            caller_name=caller_name or self._caller_name,
            caller_version=caller_version or self._caller_version,
        )

    def with_options(
        self,
        *,
        token: Optional[str] = None,
        caller_name: Optional[str] = None,
        caller_version: Optional[str] = None,
    ) -> DataAPIClient:
        """
        Create a clone of this DataAPIClient with some changed attributes.

        Args:
            token: an Access Token to the database. Example: `"AstraCS:xyz..."`.
            caller_name: name of the application, or framework, on behalf of which
                the Data API and DevOps API calls are performed. This ends up in
                the request user-agent.
            caller_version: version of the caller.

        Returns:
            a new DataAPIClient instance.

        Example:
            >>> another_client = my_client.with_options(
            ...     caller_name="caller_identity",
            ...     caller_version="1.2.0",
            ... )
        """

        return self._copy(
            token=token,
            caller_name=caller_name,
            caller_version=caller_version,
        )

    def set_caller(
        self,
        caller_name: Optional[str] = None,
        caller_version: Optional[str] = None,
    ) -> None:
        """
        Set a new identity for the application/framework on behalf of which
        the API calls will be performed (the "caller").

        New objects spawned from this client afterwards will inherit the new settings.

        Args:
            caller_name: name of the application, or framework, on behalf of which
                the API API calls are performed. This ends up in the request user-agent.
            caller_version: version of the caller.

        Example:
            >>> my_client.set_caller(caller_name="the_caller", caller_version="0.1.0")
        """

        logger.info(f"setting caller to {caller_name}/{caller_version}")
        self._caller_name = caller_name
        self._caller_version = caller_version

    def get_database(
        self,
        id: str,
        *,
        token: Optional[str] = None,
        namespace: Optional[str] = None,
        region: Optional[str] = None,
        api_path: Optional[str] = None,
        api_version: Optional[str] = None,
        max_time_ms: Optional[int] = None,
    ) -> Database:
        """
        Get a Database object from this client, for doing data-related work.

        Args:
            id: the target database ID. The database must exist for the resulting
                object to be effectively used; in other words, this invocation
                does not create the database, just the object instance.
                Actual admin work can be achieved by using the AstraDBAdmin object.
            token: if supplied, is passed to the Database instead of the client token.
            namespace: if provided, is passed to the Database
                (it is left to the default otherwise).
            region: the region to use for connecting to the database. The
                database must be located in that region.
                Note that if this parameter is not passed, an additional
                DevOps API request is made to determine the default region
                and use it subsequently.
            api_path: path to append to the API Endpoint. In typical usage, this
                should be left to its default of "/api/json".
            api_version: version specifier to append to the API path. In typical
                usage, this should be left to its default of "v1".
            max_time_ms: a timeout, in milliseconds, for the DevOps API
                HTTP request should it be necessary (see the `region` argument).

        Returns:
            a Database object with which to work on Data API collections.

        Example:
            >>> my_db0 = my_client.get_database("01234567-...")
            >>> my_db1 = my_client.get_database("01234567-...", token="AstraCS:...")
            >>> my_db2 = my_client.get_database("01234567-...", region="us-west1")
            >>> my_coll = my_db0.create_collection("movies", dimension=512)
            >>> my_coll.insert_one({"title": "The Title"}, vector=...)

        Note:
            This method does not perform any admin-level operation through
            the DevOps API. For actual creation of a database, see the
            `create_database` method of class AstraDBAdmin.
        """

        # lazy importing here to avoid circular dependency
        from astrapy import Database

        # need to inspect for values?
        this_db_info: Optional[Dict[str, Any]] = None
        # handle overrides. Only region is needed (namespace can stay empty)
        if region:
            _region = region
        else:
            if this_db_info is None:
                logger.info(f"fetching raw database info for {id}")
                this_db_info = fetch_raw_database_info_from_id_token(
                    id=id,
                    token=self.token,
                    environment=self.environment,
                    max_time_ms=max_time_ms,
                )
                logger.info(f"finished fetching raw database info for {id}")
            _region = this_db_info["info"]["region"]

        _token = token or self.token
        _api_endpoint = build_api_endpoint(
            environment=self.environment,
            database_id=id,
            region=_region,
        )
        return Database(
            api_endpoint=_api_endpoint,
            token=_token,
            namespace=namespace,
            caller_name=self._caller_name,
            caller_version=self._caller_version,
            api_path=api_path,
            api_version=api_version,
        )

    def get_async_database(
        self,
        id: str,
        *,
        token: Optional[str] = None,
        namespace: Optional[str] = None,
        region: Optional[str] = None,
        api_path: Optional[str] = None,
        api_version: Optional[str] = None,
        max_time_ms: Optional[int] = None,
    ) -> AsyncDatabase:
        """
        Get an AsyncDatabase object from this client.

        This method has identical behavior and signature as the sync
        counterpart `get_database`: please see that one for more details.
        """

        return self.get_database(
            id=id,
            token=token,
            namespace=namespace,
            region=region,
            api_path=api_path,
            api_version=api_version,
            max_time_ms=max_time_ms,
        ).to_async()

    def get_database_by_api_endpoint(
        self,
        api_endpoint: str,
        *,
        token: Optional[str] = None,
        namespace: Optional[str] = None,
        api_path: Optional[str] = None,
        api_version: Optional[str] = None,
    ) -> Database:
        """
        Get a Database object from this client, for doing data-related work.
        The Database is specified by an API Endpoint instead of the ID and a region.

        Args:
            api_endpoint: the full "API Endpoint" string used to reach the Data API.
                Example: "https://DATABASE_ID-REGION.apps.astra.datastax.com"
            token: if supplied, is passed to the Database instead of the client token.
            namespace: if provided, is passed to the Database
                (it is left to the default otherwise).
            api_path: path to append to the API Endpoint. In typical usage, this
                should be left to its default of "/api/json".
            api_version: version specifier to append to the API path. In typical
                usage, this should be left to its default of "v1".

        Returns:
            a Database object with which to work on Data API collections.

        Example:
            >>> my_db0 = my_client.get_database_by_api_endpoint("01234567-...")
            >>> my_db1 = my_client.get_database_by_api_endpoint(
            ...     "https://01234567-....apps.astra.datastax.com",
            ...     token="AstraCS:...",
            ... )
            >>> my_db2 = my_client.get_database_by_api_endpoint(
            ...     "https://01234567-....apps.astra.datastax.com",
            ...     namespace="the_other_namespace",
            ... )
            >>> my_coll = my_db0.create_collection("movies", dimension=512)
            >>> my_coll.insert_one({"title": "The Title"}, vector=...)

        Note:
            This method does not perform any admin-level operation through
            the DevOps API. For actual creation of a database, see the
            `create_database` method of class AstraDBAdmin.
        """

        # lazy importing here to avoid circular dependency
        from astrapy import Database

        parsed_api_endpoint = parse_api_endpoint(api_endpoint)
        if parsed_api_endpoint is not None:
            if parsed_api_endpoint.environment != self.environment:
                raise ValueError(
                    "Environment mismatch between client and provided "
                    "API endpoint. You can try adding "
                    f'`environment="{parsed_api_endpoint.environment}"` '
                    "to the DataAPIClient creation statement."
                )
            _token = token or self.token
            return Database(
                api_endpoint=api_endpoint,
                token=_token,
                namespace=namespace,
                caller_name=self._caller_name,
                caller_version=self._caller_version,
                api_path=api_path,
                api_version=api_version,
            )
        else:
            raise ValueError("Cannot parse the provided API endpoint.")

    def get_async_database_by_api_endpoint(
        self,
        api_endpoint: str,
        *,
        token: Optional[str] = None,
        namespace: Optional[str] = None,
        api_path: Optional[str] = None,
        api_version: Optional[str] = None,
    ) -> AsyncDatabase:
        """
        Get an AsyncDatabase object from this client, for doing data-related work.
        The Database is specified by an API Endpoint instead of the ID and a region.

        This method has identical behavior and signature as the sync
        counterpart `get_database_by_api_endpoint`: please see that one
        for more details.
        """

        return self.get_database_by_api_endpoint(
            api_endpoint=api_endpoint,
            token=token,
            namespace=namespace,
            api_path=api_path,
            api_version=api_version,
        ).to_async()

    def get_admin(
        self,
        *,
        token: Optional[str] = None,
        dev_ops_url: Optional[str] = None,
        dev_ops_api_version: Optional[str] = None,
    ) -> AstraDBAdmin:
        """
        Get an AstraDBAdmin instance corresponding to this client, for
        admin work such as managing databases.

        Args:
            token: if supplied, is passed to the Astra DB Admin instead of the
                client token. This may be useful when switching to a more powerful,
                admin-capable permission set.
            dev_ops_url: in case of custom deployments, this can be used to specify
                the URL to the DevOps API, such as "https://api.astra.datastax.com".
                Generally it can be omitted. The environment (prod/dev/...) is
                determined from the API Endpoint.
            dev_ops_api_version: this can specify a custom version of the DevOps API
                (such as "v2"). Generally not needed.

        Returns:
            An AstraDBAdmin instance, wich which to perform management at the
            database level.

        Example:
            >>> my_adm0 = my_client.get_admin()
            >>> my_adm1 = my_client.get_admin(token=more_powerful_token_override)
            >>> database_list = my_adm0.list_databases()
            >>> my_db_admin = my_adm0.create_database(
            ...     "the_other_database",
            ...     cloud_provider="AWS",
            ...     region="eu-west-1",
            ... )
            >>> my_db_admin.list_namespaces()
            ['default_keyspace', 'that_other_one']
        """

        # lazy importing here to avoid circular dependency
        from astrapy.admin import AstraDBAdmin

        return AstraDBAdmin(
            token=token or self.token,
            environment=self.environment,
            caller_name=self._caller_name,
            caller_version=self._caller_version,
            dev_ops_url=dev_ops_url,
            dev_ops_api_version=dev_ops_api_version,
        )

Methods

def get_admin(self, *, token: Optional[str] = None, dev_ops_url: Optional[str] = None, dev_ops_api_version: Optional[str] = None) ‑> AstraDBAdmin

Get an AstraDBAdmin instance corresponding to this client, for admin work such as managing databases.

Args

token

if supplied, is passed to the Astra DB Admin instead of the client token. This may be useful when switching to a more powerful, admin-capable permission set.

dev_ops_url

in case of custom deployments, this can be used to specify the URL to the DevOps API, such as "https://api.astra.datastax.com". Generally it can be omitted. The environment (prod/dev/…) is determined from the API Endpoint.

dev_ops_api_version

this can specify a custom version of the DevOps API (such as "v2"). Generally not needed.

Returns

An AstraDBAdmin instance, wich which to perform management at the database level.

Example

>>> my_adm0 = my_client.get_admin()
>>> my_adm1 = my_client.get_admin(token=more_powerful_token_override)
>>> database_list = my_adm0.list_databases()
>>> my_db_admin = my_adm0.create_database(
...     "the_other_database",
...     cloud_provider="AWS",
...     region="eu-west-1",
... )
>>> my_db_admin.list_namespaces()
['default_keyspace', 'that_other_one']

Expand source code

def get_admin(
    self,
    *,
    token: Optional[str] = None,
    dev_ops_url: Optional[str] = None,
    dev_ops_api_version: Optional[str] = None,
) -> AstraDBAdmin:
    """
    Get an AstraDBAdmin instance corresponding to this client, for
    admin work such as managing databases.

    Args:
        token: if supplied, is passed to the Astra DB Admin instead of the
            client token. This may be useful when switching to a more powerful,
            admin-capable permission set.
        dev_ops_url: in case of custom deployments, this can be used to specify
            the URL to the DevOps API, such as "https://api.astra.datastax.com".
            Generally it can be omitted. The environment (prod/dev/...) is
            determined from the API Endpoint.
        dev_ops_api_version: this can specify a custom version of the DevOps API
            (such as "v2"). Generally not needed.

    Returns:
        An AstraDBAdmin instance, wich which to perform management at the
        database level.

    Example:
        >>> my_adm0 = my_client.get_admin()
        >>> my_adm1 = my_client.get_admin(token=more_powerful_token_override)
        >>> database_list = my_adm0.list_databases()
        >>> my_db_admin = my_adm0.create_database(
        ...     "the_other_database",
        ...     cloud_provider="AWS",
        ...     region="eu-west-1",
        ... )
        >>> my_db_admin.list_namespaces()
        ['default_keyspace', 'that_other_one']
    """

    # lazy importing here to avoid circular dependency
    from astrapy.admin import AstraDBAdmin

    return AstraDBAdmin(
        token=token or self.token,
        environment=self.environment,
        caller_name=self._caller_name,
        caller_version=self._caller_version,
        dev_ops_url=dev_ops_url,
        dev_ops_api_version=dev_ops_api_version,
    )

def get_async_database(self, id: str, *, token: Optional[str] = None, namespace: Optional[str] = None, region: Optional[str] = None, api_path: Optional[str] = None, api_version: Optional[str] = None, max_time_ms: Optional[int] = None) ‑> AsyncDatabase

Get an AsyncDatabase object from this client.

This method has identical behavior and signature as the sync counterpart get_database: please see that one for more details.

Expand source code

def get_async_database(
    self,
    id: str,
    *,
    token: Optional[str] = None,
    namespace: Optional[str] = None,
    region: Optional[str] = None,
    api_path: Optional[str] = None,
    api_version: Optional[str] = None,
    max_time_ms: Optional[int] = None,
) -> AsyncDatabase:
    """
    Get an AsyncDatabase object from this client.

    This method has identical behavior and signature as the sync
    counterpart `get_database`: please see that one for more details.
    """

    return self.get_database(
        id=id,
        token=token,
        namespace=namespace,
        region=region,
        api_path=api_path,
        api_version=api_version,
        max_time_ms=max_time_ms,
    ).to_async()

def get_async_database_by_api_endpoint(self, api_endpoint: str, *, token: Optional[str] = None, namespace: Optional[str] = None, api_path: Optional[str] = None, api_version: Optional[str] = None) ‑> AsyncDatabase

Get an AsyncDatabase object from this client, for doing data-related work. The Database is specified by an API Endpoint instead of the ID and a region.

This method has identical behavior and signature as the sync counterpart get_database_by_api_endpoint: please see that one for more details.

Expand source code

def get_async_database_by_api_endpoint(
    self,
    api_endpoint: str,
    *,
    token: Optional[str] = None,
    namespace: Optional[str] = None,
    api_path: Optional[str] = None,
    api_version: Optional[str] = None,
) -> AsyncDatabase:
    """
    Get an AsyncDatabase object from this client, for doing data-related work.
    The Database is specified by an API Endpoint instead of the ID and a region.

    This method has identical behavior and signature as the sync
    counterpart `get_database_by_api_endpoint`: please see that one
    for more details.
    """

    return self.get_database_by_api_endpoint(
        api_endpoint=api_endpoint,
        token=token,
        namespace=namespace,
        api_path=api_path,
        api_version=api_version,
    ).to_async()

def get_database(self, id: str, *, token: Optional[str] = None, namespace: Optional[str] = None, region: Optional[str] = None, api_path: Optional[str] = None, api_version: Optional[str] = None, max_time_ms: Optional[int] = None) ‑> Database

Get a Database object from this client, for doing data-related work.

Args

id

the target database ID. The database must exist for the resulting object to be effectively used; in other words, this invocation does not create the database, just the object instance. Actual admin work can be achieved by using the AstraDBAdmin object.

token

if supplied, is passed to the Database instead of the client token.

namespace

if provided, is passed to the Database (it is left to the default otherwise).

region

the region to use for connecting to the database. The database must be located in that region. Note that if this parameter is not passed, an additional DevOps API request is made to determine the default region and use it subsequently.

api_path

path to append to the API Endpoint. In typical usage, this should be left to its default of "/api/json".

api_version

version specifier to append to the API path. In typical usage, this should be left to its default of "v1".

max_time_ms

a timeout, in milliseconds, for the DevOps API HTTP request should it be necessary (see the region argument).

Returns

a Database object with which to work on Data API collections.

Example

>>> my_db0 = my_client.get_database("01234567-...")
>>> my_db1 = my_client.get_database("01234567-...", token="AstraCS:...")
>>> my_db2 = my_client.get_database("01234567-...", region="us-west1")
>>> my_coll = my_db0.create_collection("movies", dimension=512)
>>> my_coll.insert_one({"title": "The Title"}, vector=...)

Note

This method does not perform any admin-level operation through the DevOps API. For actual creation of a database, see the create_database method of class AstraDBAdmin.

Expand source code

def get_database(
    self,
    id: str,
    *,
    token: Optional[str] = None,
    namespace: Optional[str] = None,
    region: Optional[str] = None,
    api_path: Optional[str] = None,
    api_version: Optional[str] = None,
    max_time_ms: Optional[int] = None,
) -> Database:
    """
    Get a Database object from this client, for doing data-related work.

    Args:
        id: the target database ID. The database must exist for the resulting
            object to be effectively used; in other words, this invocation
            does not create the database, just the object instance.
            Actual admin work can be achieved by using the AstraDBAdmin object.
        token: if supplied, is passed to the Database instead of the client token.
        namespace: if provided, is passed to the Database
            (it is left to the default otherwise).
        region: the region to use for connecting to the database. The
            database must be located in that region.
            Note that if this parameter is not passed, an additional
            DevOps API request is made to determine the default region
            and use it subsequently.
        api_path: path to append to the API Endpoint. In typical usage, this
            should be left to its default of "/api/json".
        api_version: version specifier to append to the API path. In typical
            usage, this should be left to its default of "v1".
        max_time_ms: a timeout, in milliseconds, for the DevOps API
            HTTP request should it be necessary (see the `region` argument).

    Returns:
        a Database object with which to work on Data API collections.

    Example:
        >>> my_db0 = my_client.get_database("01234567-...")
        >>> my_db1 = my_client.get_database("01234567-...", token="AstraCS:...")
        >>> my_db2 = my_client.get_database("01234567-...", region="us-west1")
        >>> my_coll = my_db0.create_collection("movies", dimension=512)
        >>> my_coll.insert_one({"title": "The Title"}, vector=...)

    Note:
        This method does not perform any admin-level operation through
        the DevOps API. For actual creation of a database, see the
        `create_database` method of class AstraDBAdmin.
    """

    # lazy importing here to avoid circular dependency
    from astrapy import Database

    # need to inspect for values?
    this_db_info: Optional[Dict[str, Any]] = None
    # handle overrides. Only region is needed (namespace can stay empty)
    if region:
        _region = region
    else:
        if this_db_info is None:
            logger.info(f"fetching raw database info for {id}")
            this_db_info = fetch_raw_database_info_from_id_token(
                id=id,
                token=self.token,
                environment=self.environment,
                max_time_ms=max_time_ms,
            )
            logger.info(f"finished fetching raw database info for {id}")
        _region = this_db_info["info"]["region"]

    _token = token or self.token
    _api_endpoint = build_api_endpoint(
        environment=self.environment,
        database_id=id,
        region=_region,
    )
    return Database(
        api_endpoint=_api_endpoint,
        token=_token,
        namespace=namespace,
        caller_name=self._caller_name,
        caller_version=self._caller_version,
        api_path=api_path,
        api_version=api_version,
    )

def get_database_by_api_endpoint(self, api_endpoint: str, *, token: Optional[str] = None, namespace: Optional[str] = None, api_path: Optional[str] = None, api_version: Optional[str] = None) ‑> Database

Get a Database object from this client, for doing data-related work. The Database is specified by an API Endpoint instead of the ID and a region.

Args

api_endpoint

the full "API Endpoint" string used to reach the Data API. Example: "https://DATABASE_ID-REGION.apps.astra.datastax.com"

token

if supplied, is passed to the Database instead of the client token.

namespace

if provided, is passed to the Database (it is left to the default otherwise).

api_path

path to append to the API Endpoint. In typical usage, this should be left to its default of "/api/json".

api_version

version specifier to append to the API path. In typical usage, this should be left to its default of "v1".

Returns

a Database object with which to work on Data API collections.

Example

>>> my_db0 = my_client.get_database_by_api_endpoint("01234567-...")
>>> my_db1 = my_client.get_database_by_api_endpoint(
...     "https://01234567-....apps.astra.datastax.com",
...     token="AstraCS:...",
... )
>>> my_db2 = my_client.get_database_by_api_endpoint(
...     "https://01234567-....apps.astra.datastax.com",
...     namespace="the_other_namespace",
... )
>>> my_coll = my_db0.create_collection("movies", dimension=512)
>>> my_coll.insert_one({"title": "The Title"}, vector=...)

Note

This method does not perform any admin-level operation through the DevOps API. For actual creation of a database, see the create_database method of class AstraDBAdmin.

Expand source code

def get_database_by_api_endpoint(
    self,
    api_endpoint: str,
    *,
    token: Optional[str] = None,
    namespace: Optional[str] = None,
    api_path: Optional[str] = None,
    api_version: Optional[str] = None,
) -> Database:
    """
    Get a Database object from this client, for doing data-related work.
    The Database is specified by an API Endpoint instead of the ID and a region.

    Args:
        api_endpoint: the full "API Endpoint" string used to reach the Data API.
            Example: "https://DATABASE_ID-REGION.apps.astra.datastax.com"
        token: if supplied, is passed to the Database instead of the client token.
        namespace: if provided, is passed to the Database
            (it is left to the default otherwise).
        api_path: path to append to the API Endpoint. In typical usage, this
            should be left to its default of "/api/json".
        api_version: version specifier to append to the API path. In typical
            usage, this should be left to its default of "v1".

    Returns:
        a Database object with which to work on Data API collections.

    Example:
        >>> my_db0 = my_client.get_database_by_api_endpoint("01234567-...")
        >>> my_db1 = my_client.get_database_by_api_endpoint(
        ...     "https://01234567-....apps.astra.datastax.com",
        ...     token="AstraCS:...",
        ... )
        >>> my_db2 = my_client.get_database_by_api_endpoint(
        ...     "https://01234567-....apps.astra.datastax.com",
        ...     namespace="the_other_namespace",
        ... )
        >>> my_coll = my_db0.create_collection("movies", dimension=512)
        >>> my_coll.insert_one({"title": "The Title"}, vector=...)

    Note:
        This method does not perform any admin-level operation through
        the DevOps API. For actual creation of a database, see the
        `create_database` method of class AstraDBAdmin.
    """

    # lazy importing here to avoid circular dependency
    from astrapy import Database

    parsed_api_endpoint = parse_api_endpoint(api_endpoint)
    if parsed_api_endpoint is not None:
        if parsed_api_endpoint.environment != self.environment:
            raise ValueError(
                "Environment mismatch between client and provided "
                "API endpoint. You can try adding "
                f'`environment="{parsed_api_endpoint.environment}"` '
                "to the DataAPIClient creation statement."
            )
        _token = token or self.token
        return Database(
            api_endpoint=api_endpoint,
            token=_token,
            namespace=namespace,
            caller_name=self._caller_name,
            caller_version=self._caller_version,
            api_path=api_path,
            api_version=api_version,
        )
    else:
        raise ValueError("Cannot parse the provided API endpoint.")

def set_caller(self, caller_name: Optional[str] = None, caller_version: Optional[str] = None) ‑> None

Set a new identity for the application/framework on behalf of which the API calls will be performed (the "caller").

New objects spawned from this client afterwards will inherit the new settings.

Args

caller_name

name of the application, or framework, on behalf of which the API API calls are performed. This ends up in the request user-agent.

caller_version

version of the caller.

Example

>>> my_client.set_caller(caller_name="the_caller", caller_version="0.1.0")

Expand source code

def set_caller(
    self,
    caller_name: Optional[str] = None,
    caller_version: Optional[str] = None,
) -> None:
    """
    Set a new identity for the application/framework on behalf of which
    the API calls will be performed (the "caller").

    New objects spawned from this client afterwards will inherit the new settings.

    Args:
        caller_name: name of the application, or framework, on behalf of which
            the API API calls are performed. This ends up in the request user-agent.
        caller_version: version of the caller.

    Example:
        >>> my_client.set_caller(caller_name="the_caller", caller_version="0.1.0")
    """

    logger.info(f"setting caller to {caller_name}/{caller_version}")
    self._caller_name = caller_name
    self._caller_version = caller_version

def with_options(self, *, token: Optional[str] = None, caller_name: Optional[str] = None, caller_version: Optional[str] = None) ‑> DataAPIClient

Create a clone of this DataAPIClient with some changed attributes.

Args

token

an Access Token to the database. Example: "AstraCS:xyz...".

caller_name

name of the application, or framework, on behalf of which the Data API and DevOps API calls are performed. This ends up in the request user-agent.

caller_version

version of the caller.

Returns

a new DataAPIClient instance.

Example

>>> another_client = my_client.with_options(
...     caller_name="caller_identity",
...     caller_version="1.2.0",
... )

Expand source code

def with_options(
    self,
    *,
    token: Optional[str] = None,
    caller_name: Optional[str] = None,
    caller_version: Optional[str] = None,
) -> DataAPIClient:
    """
    Create a clone of this DataAPIClient with some changed attributes.

    Args:
        token: an Access Token to the database. Example: `"AstraCS:xyz..."`.
        caller_name: name of the application, or framework, on behalf of which
            the Data API and DevOps API calls are performed. This ends up in
            the request user-agent.
        caller_version: version of the caller.

    Returns:
        a new DataAPIClient instance.

    Example:
        >>> another_client = my_client.with_options(
        ...     caller_name="caller_identity",
        ...     caller_version="1.2.0",
        ... )
    """

    return self._copy(
        token=token,
        caller_name=caller_name,
        caller_version=caller_version,
    )

>>> from astrapy import DataAPIClient
>>> my_client = astrapy.DataAPIClient("AstraCS:...")
>>> my_db = my_client.get_database_by_api_endpoint(
...    "https://01234567-....apps.astra.datastax.com"
... )

class Database:
    """
    A Data API database. This is the entry-point object for doing database-level
    DML, such as creating/deleting collections, and for obtaining Collection
    objects themselves. This class has a synchronous interface.

    A Database comes with an "API Endpoint", which implies a Database object
    instance reaches a specific region (relevant point in case of multi-region
    databases).

    Args:
        api_endpoint: the full "API Endpoint" string used to reach the Data API.
            Example: "https://<database_id>-<region>.apps.astra.datastax.com"
        token: an Access Token to the database. Example: "AstraCS:xyz..."
        namespace: this is the namespace all method calls will target, unless
            one is explicitly specified in the call. If no namespace is supplied
            when creating a Database, the name "default_namespace" is set.
        caller_name: name of the application, or framework, on behalf of which
            the Data API calls are performed. This ends up in the request user-agent.
        caller_version: version of the caller.
        api_path: path to append to the API Endpoint. In typical usage, this
            should be left to its default of "/api/json".
        api_version: version specifier to append to the API path. In typical
            usage, this should be left to its default of "v1".

    Example:
        >>> from astrapy import DataAPIClient
        >>> my_client = astrapy.DataAPIClient("AstraCS:...")
        >>> my_db = my_client.get_database_by_api_endpoint(
        ...    "https://01234567-....apps.astra.datastax.com"
        ... )

    Note:
        creating an instance of Database does not trigger actual creation
        of the database itself, which should exist beforehand. To create databases,
        see the AstraDBAdmin class.
    """

    def __init__(
        self,
        api_endpoint: str,
        token: str,
        *,
        namespace: Optional[str] = None,
        caller_name: Optional[str] = None,
        caller_version: Optional[str] = None,
        api_path: Optional[str] = None,
        api_version: Optional[str] = None,
    ) -> None:
        self._astra_db = AstraDB(
            token=token,
            api_endpoint=api_endpoint,
            api_path=api_path,
            api_version=api_version,
            namespace=namespace,
            caller_name=caller_name,
            caller_version=caller_version,
        )
        self._name: Optional[str] = None

    def __getattr__(self, collection_name: str) -> Collection:
        return self.get_collection(name=collection_name)

    def __getitem__(self, collection_name: str) -> Collection:
        return self.get_collection(name=collection_name)

    def __repr__(self) -> str:
        return (
            f'{self.__class__.__name__}(api_endpoint="{self._astra_db.api_endpoint}", '
            f'token="{self._astra_db.token[:12]}...", namespace="{self._astra_db.namespace}")'
        )

    def __eq__(self, other: Any) -> bool:
        if isinstance(other, Database):
            return self._astra_db == other._astra_db
        else:
            return False

    def _copy(
        self,
        *,
        api_endpoint: Optional[str] = None,
        token: Optional[str] = None,
        namespace: Optional[str] = None,
        caller_name: Optional[str] = None,
        caller_version: Optional[str] = None,
        api_path: Optional[str] = None,
        api_version: Optional[str] = None,
    ) -> Database:
        return Database(
            api_endpoint=api_endpoint or self._astra_db.api_endpoint,
            token=token or self._astra_db.token,
            namespace=namespace or self._astra_db.namespace,
            caller_name=caller_name or self._astra_db.caller_name,
            caller_version=caller_version or self._astra_db.caller_version,
            api_path=api_path or self._astra_db.api_path,
            api_version=api_version or self._astra_db.api_version,
        )

    def with_options(
        self,
        *,
        namespace: Optional[str] = None,
        caller_name: Optional[str] = None,
        caller_version: Optional[str] = None,
    ) -> Database:
        """
        Create a clone of this database with some changed attributes.

        Args:
            namespace: this is the namespace all method calls will target, unless
                one is explicitly specified in the call. If no namespace is supplied
                when creating a Database, the name "default_namespace" is set.
            caller_name: name of the application, or framework, on behalf of which
                the Data API calls are performed. This ends up in the request user-agent.
            caller_version: version of the caller.

        Returns:
            a new `Database` instance.

        Example:
            >>> my_db_2 = my_db.with_options(
            ...     namespace="the_other_namespace",
            ...     caller_name="the_caller",
            ...     caller_version="0.1.0",
            ... )
        """

        return self._copy(
            namespace=namespace,
            caller_name=caller_name,
            caller_version=caller_version,
        )

    def to_async(
        self,
        *,
        api_endpoint: Optional[str] = None,
        token: Optional[str] = None,
        namespace: Optional[str] = None,
        caller_name: Optional[str] = None,
        caller_version: Optional[str] = None,
        api_path: Optional[str] = None,
        api_version: Optional[str] = None,
    ) -> AsyncDatabase:
        """
        Create an AsyncDatabase from this one. Save for the arguments
        explicitly provided as overrides, everything else is kept identical
        to this database in the copy.

        Args:
            api_endpoint: the full "API Endpoint" string used to reach the Data API.
                Example: "https://<database_id>-<region>.apps.astra.datastax.com"
            token: an Access Token to the database. Example: "AstraCS:xyz..."
            namespace: this is the namespace all method calls will target, unless
                one is explicitly specified in the call. If no namespace is supplied
                when creating a Database, the name "default_namespace" is set.
            caller_name: name of the application, or framework, on behalf of which
                the Data API calls are performed. This ends up in the request user-agent.
            caller_version: version of the caller.
            api_path: path to append to the API Endpoint. In typical usage, this
                should be left to its default of "/api/json".
            api_version: version specifier to append to the API path. In typical
                usage, this should be left to its default of "v1".

        Returns:
            the new copy, an `AsyncDatabase` instance.

        Example:
            >>> my_async_db = my_db.to_async()
            >>> asyncio.run(my_async_db.list_collection_names())
        """

        return AsyncDatabase(
            api_endpoint=api_endpoint or self._astra_db.api_endpoint,
            token=token or self._astra_db.token,
            namespace=namespace or self._astra_db.namespace,
            caller_name=caller_name or self._astra_db.caller_name,
            caller_version=caller_version or self._astra_db.caller_version,
            api_path=api_path or self._astra_db.api_path,
            api_version=api_version or self._astra_db.api_version,
        )

    def set_caller(
        self,
        caller_name: Optional[str] = None,
        caller_version: Optional[str] = None,
    ) -> None:
        """
        Set a new identity for the application/framework on behalf of which
        the Data API calls are performed (the "caller").

        Args:
            caller_name: name of the application, or framework, on behalf of which
                the Data API calls are performed. This ends up in the request user-agent.
            caller_version: version of the caller.

        Example:
            >>> my_db.set_caller(caller_name="the_caller", caller_version="0.1.0")
        """

        logger.info(f"setting caller to {caller_name}/{caller_version}")
        self._astra_db.set_caller(
            caller_name=caller_name,
            caller_version=caller_version,
        )

    def info(self) -> DatabaseInfo:
        """
        Additional information on the database as a DatabaseInfo instance.

        Some of the returned properties are dynamic throughout the lifetime
        of the database (such as raw_info["keyspaces"]). For this reason,
        each invocation of this method triggers a new request to the DevOps API.

        Example:
            >>> my_db.info().region
            'eu-west-1'

            >>> my_db.info().raw_info['datacenters'][0]['dateCreated']
            '2023-01-30T12:34:56Z'

        Note:
            see the DatabaseInfo documentation for a caveat about the difference
            between the `region` and the `raw_info["region"]` attributes.
        """

        logger.info("getting database info")
        database_info = fetch_database_info(
            self._astra_db.api_endpoint,
            token=self._astra_db.token,
            namespace=self.namespace,
        )
        if database_info is not None:
            logger.info("finished getting database info")
            return database_info
        else:
            raise DevOpsAPIException(
                "Database is not in a supported environment for this operation."
            )

    @property
    def id(self) -> str:
        """
        The ID of this database.

        Example:
            >>> my_db.id
            '01234567-89ab-cdef-0123-456789abcdef'
        """

        parsed_api_endpoint = parse_api_endpoint(self._astra_db.api_endpoint)
        if parsed_api_endpoint is not None:
            return parsed_api_endpoint.database_id
        else:
            raise DevOpsAPIException(
                "Database is not in a supported environment for this operation."
            )

    def name(self) -> str:
        """
        The name of this database. Note that this bears no unicity guarantees.

        Calling this method the first time involves a request
        to the DevOps API (the resulting database name is then cached).
        See the `info()` method for more details.

        Example:
            >>> my_db.name()
            'the_application_database'
        """

        if self._name is None:
            self._name = self.info().name
        return self._name

    @property
    def namespace(self) -> str:
        """
        The namespace this database uses as target for all commands when
        no method-call-specific namespace is specified.

        Example:
            >>> my_db.namespace
            'the_keyspace'
        """

        return self._astra_db.namespace

    def get_collection(
        self, name: str, *, namespace: Optional[str] = None
    ) -> Collection:
        """
        Spawn a `Collection` object instance representing a collection
        on this database.

        Creating a `Collection` instance does not have any effect on the
        actual state of the database: in other words, for the created
        `Collection` instance to be used meaningfully, the collection
        must exist already (for instance, it should have been created
        previously by calling the `create_collection` method).

        Args:
            name: the name of the collection.
            namespace: the namespace containing the collection. If no namespace
                is specified, the general setting for this database is used.

        Returns:
            a `Collection` instance, representing the desired collection
                (but without any form of validation).

        Example:
            >>> my_col = my_db.get_collection("my_collection")
            >>> my_col.count_documents({}, upper_bound=100)
            41

        Note:
            The attribute and indexing syntax forms achieve the same effect
            as this method. In other words, the following are equivalent:
                my_db.get_collection("coll_name")
                my_db.coll_name
                my_db["coll_name"]
        """

        # lazy importing here against circular-import error
        from astrapy.collection import Collection

        _namespace = namespace or self._astra_db.namespace
        return Collection(self, name, namespace=_namespace)

    @recast_method_sync
    def create_collection(
        self,
        name: str,
        *,
        namespace: Optional[str] = None,
        dimension: Optional[int] = None,
        metric: Optional[str] = None,
        service: Optional[Union[CollectionVectorServiceOptions, Dict[str, Any]]] = None,
        indexing: Optional[Dict[str, Any]] = None,
        default_id_type: Optional[str] = None,
        additional_options: Optional[Dict[str, Any]] = None,
        check_exists: Optional[bool] = None,
        max_time_ms: Optional[int] = None,
    ) -> Collection:
        """
        Creates a collection on the database and return the Collection
        instance that represents it.

        This is a blocking operation: the method returns when the collection
        is ready to be used. As opposed to the `get_collection` instance,
        this method triggers causes the collection to be actually created on DB.

        Args:
            name: the name of the collection.
            namespace: the namespace where the collection is to be created.
                If not specified, the general setting for this database is used.
            dimension: for vector collections, the dimension of the vectors
                (i.e. the number of their components).
            metric: the similarity metric used for vector searches.
                Allowed values are `VectorMetric.DOT_PRODUCT`, `VectorMetric.EUCLIDEAN`
                or `VectorMetric.COSINE` (default).
            service: a dictionary describing a service for
                embedding computation, e.g. `{"provider": "ab", "modelName": "xy"}`.
                Alternatively, a CollectionVectorServiceOptions object to the same effect.
                NOTE: This feature is under current development.
            indexing: optional specification of the indexing options for
                the collection, in the form of a dictionary such as
                    {"deny": [...]}
                or
                    {"allow": [...]}
            default_id_type: this sets what type of IDs the API server will
                generate when inserting documents that do not specify their
                `_id` field explicitly. Can be set to any of the values
                `DefaultIdType.UUID`, `DefaultIdType.OBJECTID`,
                `DefaultIdType.UUIDV6`, `DefaultIdType.UUIDV7`,
                `DefaultIdType.DEFAULT`.
            additional_options: any further set of key-value pairs that will
                be added to the "options" part of the payload when sending
                the Data API command to create a collection.
            check_exists: whether to run an existence check for the collection
                name before attempting to create the collection:
                If check_exists is True, an error is raised when creating
                an existing collection.
                If it is False, the creation is attempted. In this case, for
                preexisting collections, the command will succeed or fail
                depending on whether the options match or not.
            max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

        Returns:
            a (synchronous) `Collection` instance, representing the
            newly-created collection.

        Example:
            >>> new_col = my_db.create_collection("my_v_col", dimension=3)
            >>> new_col.insert_one({"name": "the_row"}, vector=[0.4, 0.5, 0.7])
            InsertOneResult(raw_results=..., inserted_id='e22dd65e-...-...-...')

        Note:
            A collection is considered a vector collection if at least one of
            `dimension` or `service` are provided and not null. In that case,
            and only in that case, is `metric` an accepted parameter.
            Note, moreover, that if passing both these parameters, then
            the dimension must be compatible with the chosen service.
        """

        _validate_create_collection_options(
            dimension=dimension,
            metric=metric,
            service=service,
            indexing=indexing,
            default_id_type=default_id_type,
            additional_options=additional_options,
        )
        _options = {
            **(additional_options or {}),
            **({"indexing": indexing} if indexing else {}),
            **({"defaultId": {"type": default_id_type}} if default_id_type else {}),
        }

        timeout_manager = MultiCallTimeoutManager(overall_max_time_ms=max_time_ms)

        if check_exists is None:
            _check_exists = True
        else:
            _check_exists = check_exists
        existing_names: List[str]
        if _check_exists:
            logger.info(f"checking collection existence for '{name}'")
            existing_names = self.list_collection_names(
                namespace=namespace,
                max_time_ms=timeout_manager.remaining_timeout_ms(),
            )
        else:
            existing_names = []

        driver_db = self._astra_db.copy(namespace=namespace)
        if name in existing_names:
            raise CollectionAlreadyExistsException(
                text=f"CollectionInvalid: collection {name} already exists",
                namespace=driver_db.namespace,
                collection_name=name,
            )

        service_dict: Optional[Dict[str, Any]]
        if service is not None:
            service_dict = service if isinstance(service, dict) else service.as_dict()
        else:
            service_dict = None

        logger.info(f"creating collection '{name}'")
        driver_db.create_collection(
            name,
            options=_options,
            dimension=dimension,
            metric=metric,
            service_dict=service_dict,
            timeout_info=timeout_manager.remaining_timeout_info(),
        )
        logger.info(f"finished creating collection '{name}'")
        return self.get_collection(name, namespace=namespace)

    @recast_method_sync
    def drop_collection(
        self,
        name_or_collection: Union[str, Collection],
        *,
        max_time_ms: Optional[int] = None,
    ) -> Dict[str, Any]:
        """
        Drop a collection from the database, along with all documents therein.

        Args:
            name_or_collection: either the name of a collection or
                a `Collection` instance.
            max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

        Returns:
            a dictionary in the form {"ok": 1} if the command succeeds.

        Example:
            >>> my_db.list_collection_names()
            ['a_collection', 'my_v_col', 'another_col']
            >>> my_db.drop_collection("my_v_col")
            {'ok': 1}
            >>> my_db.list_collection_names()
            ['a_collection', 'another_col']

        Note:
            when providing a collection name, it is assumed that the collection
            is to be found in the namespace set at database instance level.
        """

        # lazy importing here against circular-import error
        from astrapy.collection import Collection

        if isinstance(name_or_collection, Collection):
            _namespace = name_or_collection.namespace
            _name: str = name_or_collection.name
            logger.info(f"dropping collection '{_name}'")
            dc_response = self._astra_db.copy(namespace=_namespace).delete_collection(
                _name,
                timeout_info=base_timeout_info(max_time_ms),
            )
            logger.info(f"finished dropping collection '{_name}'")
            return dc_response.get("status", {})  # type: ignore[no-any-return]
        else:
            logger.info(f"dropping collection '{name_or_collection}'")
            dc_response = self._astra_db.delete_collection(
                name_or_collection,
                timeout_info=base_timeout_info(max_time_ms),
            )
            logger.info(f"finished dropping collection '{name_or_collection}'")
            return dc_response.get("status", {})  # type: ignore[no-any-return]

    @recast_method_sync
    def list_collections(
        self,
        *,
        namespace: Optional[str] = None,
        max_time_ms: Optional[int] = None,
    ) -> CommandCursor[CollectionDescriptor]:
        """
        List all collections in a given namespace for this database.

        Args:
            namespace: the namespace to be inspected. If not specified,
                the general setting for this database is assumed.
            max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

        Returns:
            a `CommandCursor` to iterate over CollectionDescriptor instances,
            each corresponding to a collection.

        Example:
            >>> ccur = my_db.list_collections()
            >>> ccur
            <astrapy.cursors.CommandCursor object at ...>
            >>> list(ccur)
            [CollectionDescriptor(name='my_v_col', options=CollectionOptions())]
            >>> for coll_dict in my_db.list_collections():
            ...     print(coll_dict)
            ...
            CollectionDescriptor(name='my_v_col', options=CollectionOptions())
        """

        if namespace:
            _client = self._astra_db.copy(namespace=namespace)
        else:
            _client = self._astra_db
        logger.info("getting collections")
        gc_response = _client.get_collections(
            options={"explain": True}, timeout_info=base_timeout_info(max_time_ms)
        )
        if "collections" not in gc_response.get("status", {}):
            raise DataAPIFaultyResponseException(
                text="Faulty response from get_collections API command.",
                raw_response=gc_response,
            )
        else:
            # we know this is a list of dicts, to marshal into "descriptors"
            logger.info("finished getting collections")
            return CommandCursor(
                address=self._astra_db.base_url,
                items=[
                    CollectionDescriptor.from_dict(col_dict)
                    for col_dict in gc_response["status"]["collections"]
                ],
            )

    @recast_method_sync
    def list_collection_names(
        self,
        *,
        namespace: Optional[str] = None,
        max_time_ms: Optional[int] = None,
    ) -> List[str]:
        """
        List the names of all collections in a given namespace of this database.

        Args:
            namespace: the namespace to be inspected. If not specified,
                the general setting for this database is assumed.
            max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

        Returns:
            a list of the collection names as strings, in no particular order.

        Example:
            >>> my_db.list_collection_names()
            ['a_collection', 'another_col']
        """

        logger.info("getting collection names")
        gc_response = self._astra_db.copy(namespace=namespace).get_collections(
            timeout_info=base_timeout_info(max_time_ms)
        )
        if "collections" not in gc_response.get("status", {}):
            raise DataAPIFaultyResponseException(
                text="Faulty response from get_collections API command.",
                raw_response=gc_response,
            )
        else:
            # we know this is a list of strings
            logger.info("finished getting collection names")
            return gc_response["status"]["collections"]  # type: ignore[no-any-return]

    @recast_method_sync
    def command(
        self,
        body: Dict[str, Any],
        *,
        namespace: Optional[str] = None,
        collection_name: Optional[str] = None,
        max_time_ms: Optional[int] = None,
    ) -> Dict[str, Any]:
        """
        Send a POST request to the Data API for this database with
        an arbitrary, caller-provided payload.

        Args:
            body: a JSON-serializable dictionary, the payload of the request.
            namespace: the namespace to use. Requests always target a namespace:
                if not specified, the general setting for this database is assumed.
            collection_name: if provided, the collection name is appended at the end
                of the endpoint. In this way, this method allows collection-level
                arbitrary POST requests as well.
            max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

        Returns:
            a dictionary with the response of the HTTP request.

        Example:
            >>> my_db.command({"findCollections": {}})
            {'status': {'collections': ['my_coll']}}
            >>> my_db.command({"countDocuments": {}}, collection_name="my_coll")
            {'status': {'count': 123}}
        """

        if namespace:
            _client = self._astra_db.copy(namespace=namespace)
        else:
            _client = self._astra_db
        if collection_name:
            _collection = _client.collection(collection_name)
            logger.info(f"issuing custom command to API (on '{collection_name}')")
            req_response = _collection.post_raw_request(
                body=body,
                timeout_info=base_timeout_info(max_time_ms),
            )
            logger.info(
                f"finished issuing custom command to API (on '{collection_name}')"
            )
            return req_response
        else:
            logger.info("issuing custom command to API")
            req_response = _client.post_raw_request(
                body=body,
                timeout_info=base_timeout_info(max_time_ms),
            )
            logger.info("finished issuing custom command to API")
            return req_response

    def get_database_admin(
        self,
        *,
        token: Optional[str] = None,
        dev_ops_url: Optional[str] = None,
        dev_ops_api_version: Optional[str] = None,
    ) -> AstraDBDatabaseAdmin:
        """
        Return an AstraDBDatabaseAdmin object corresponding to this database, for
        use in admin tasks such as managing namespaces.

        Args:
            token: an access token with enough permission on the database to
                perform the desired tasks. If omitted (as it can generally be done),
                the token of this Database is used.
            dev_ops_url: in case of custom deployments, this can be used to specify
                the URL to the DevOps API, such as "https://api.astra.datastax.com".
                Generally it can be omitted. The environment (prod/dev/...) is
                determined from the API Endpoint.
            dev_ops_api_version: this can specify a custom version of the DevOps API
                (such as "v2"). Generally not needed.

        Returns:
            An AstraDBDatabaseAdmin instance targeting this database.

        Example:
            >>> my_db_admin = my_db.get_database_admin()
            >>> if "new_namespace" not in my_db_admin.list_namespaces():
            ...     my_db_admin.create_namespace("new_namespace")
            >>> my_db_admin.list_namespaces()
            ['default_keyspace', 'new_namespace']
        """

        # lazy importing here to avoid circular dependency
        from astrapy.admin import AstraDBDatabaseAdmin

        return AstraDBDatabaseAdmin.from_api_endpoint(
            api_endpoint=self._astra_db.api_endpoint,
            token=token or self._astra_db.token,
            caller_name=self._astra_db.caller_name,
            caller_version=self._astra_db.caller_version,
            dev_ops_url=dev_ops_url,
            dev_ops_api_version=dev_ops_api_version,
        )

Instance variables

var id : str

The ID of this database.

Example

>>> my_db.id
'01234567-89ab-cdef-0123-456789abcdef'

Expand source code

@property
def id(self) -> str:
    """
    The ID of this database.

    Example:
        >>> my_db.id
        '01234567-89ab-cdef-0123-456789abcdef'
    """

    parsed_api_endpoint = parse_api_endpoint(self._astra_db.api_endpoint)
    if parsed_api_endpoint is not None:
        return parsed_api_endpoint.database_id
    else:
        raise DevOpsAPIException(
            "Database is not in a supported environment for this operation."
        )

var namespace : str

The namespace this database uses as target for all commands when no method-call-specific namespace is specified.

Example

>>> my_db.namespace
'the_keyspace'

Expand source code

@property
def namespace(self) -> str:
    """
    The namespace this database uses as target for all commands when
    no method-call-specific namespace is specified.

    Example:
        >>> my_db.namespace
        'the_keyspace'
    """

    return self._astra_db.namespace

Methods

def command(self, body: Dict[str, Any], *, namespace: Optional[str] = None, collection_name: Optional[str] = None, max_time_ms: Optional[int] = None) ‑> Dict[str, Any]

Send a POST request to the Data API for this database with an arbitrary, caller-provided payload.

Args

body

a JSON-serializable dictionary, the payload of the request.

namespace

the namespace to use. Requests always target a namespace: if not specified, the general setting for this database is assumed.

collection_name

if provided, the collection name is appended at the end of the endpoint. In this way, this method allows collection-level arbitrary POST requests as well.

max_time_ms

a timeout, in milliseconds, for the underlying HTTP request.

Returns

a dictionary with the response of the HTTP request.

Example

>>> my_db.command({"findCollections": {}})
{'status': {'collections': ['my_coll']}}
>>> my_db.command({"countDocuments": {}}, collection_name="my_coll")
{'status': {'count': 123}}

Expand source code

@recast_method_sync
def command(
    self,
    body: Dict[str, Any],
    *,
    namespace: Optional[str] = None,
    collection_name: Optional[str] = None,
    max_time_ms: Optional[int] = None,
) -> Dict[str, Any]:
    """
    Send a POST request to the Data API for this database with
    an arbitrary, caller-provided payload.

    Args:
        body: a JSON-serializable dictionary, the payload of the request.
        namespace: the namespace to use. Requests always target a namespace:
            if not specified, the general setting for this database is assumed.
        collection_name: if provided, the collection name is appended at the end
            of the endpoint. In this way, this method allows collection-level
            arbitrary POST requests as well.
        max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

    Returns:
        a dictionary with the response of the HTTP request.

    Example:
        >>> my_db.command({"findCollections": {}})
        {'status': {'collections': ['my_coll']}}
        >>> my_db.command({"countDocuments": {}}, collection_name="my_coll")
        {'status': {'count': 123}}
    """

    if namespace:
        _client = self._astra_db.copy(namespace=namespace)
    else:
        _client = self._astra_db
    if collection_name:
        _collection = _client.collection(collection_name)
        logger.info(f"issuing custom command to API (on '{collection_name}')")
        req_response = _collection.post_raw_request(
            body=body,
            timeout_info=base_timeout_info(max_time_ms),
        )
        logger.info(
            f"finished issuing custom command to API (on '{collection_name}')"
        )
        return req_response
    else:
        logger.info("issuing custom command to API")
        req_response = _client.post_raw_request(
            body=body,
            timeout_info=base_timeout_info(max_time_ms),
        )
        logger.info("finished issuing custom command to API")
        return req_response

def create_collection(self, name: str, *, namespace: Optional[str] = None, dimension: Optional[int] = None, metric: Optional[str] = None, service: Optional[Union[CollectionVectorServiceOptions, Dict[str, Any]]] = None, indexing: Optional[Dict[str, Any]] = None, default_id_type: Optional[str] = None, additional_options: Optional[Dict[str, Any]] = None, check_exists: Optional[bool] = None, max_time_ms: Optional[int] = None) ‑> Collection

Creates a collection on the database and return the Collection instance that represents it.

This is a blocking operation: the method returns when the collection is ready to be used. As opposed to the get_collection instance, this method triggers causes the collection to be actually created on DB.

Args

name

the name of the collection.

namespace

the namespace where the collection is to be created. If not specified, the general setting for this database is used.

dimension

for vector collections, the dimension of the vectors (i.e. the number of their components).

metric

the similarity metric used for vector searches. Allowed values are VectorMetric.DOT_PRODUCT, VectorMetric.EUCLIDEAN or VectorMetric.COSINE (default).

service

a dictionary describing a service for embedding computation, e.g. {"provider": "ab", "modelName": "xy"}. Alternatively, a CollectionVectorServiceOptions object to the same effect. NOTE: This feature is under current development.

indexing

optional specification of the indexing options for the collection, in the form of a dictionary such as {"deny": […]} or

default_id_type

this sets what type of IDs the API server will generate when inserting documents that do not specify their _id field explicitly. Can be set to any of the values DefaultIdType.UUID, DefaultIdType.OBJECTID, DefaultIdType.UUIDV6, DefaultIdType.UUIDV7, DefaultIdType.DEFAULT.

additional_options

any further set of key-value pairs that will be added to the "options" part of the payload when sending the Data API command to create a collection.

check_exists

whether to run an existence check for the collection name before attempting to create the collection: If check_exists is True, an error is raised when creating an existing collection. If it is False, the creation is attempted. In this case, for preexisting collections, the command will succeed or fail depending on whether the options match or not.

max_time_ms

a timeout, in milliseconds, for the underlying HTTP request.

Returns

a (synchronous) Collection instance, representing the newly-created collection.

Example

>>> new_col = my_db.create_collection("my_v_col", dimension=3)
>>> new_col.insert_one({"name": "the_row"}, vector=[0.4, 0.5, 0.7])
InsertOneResult(raw_results=..., inserted_id='e22dd65e-...-...-...')

Note

A collection is considered a vector collection if at least one of dimension or service are provided and not null. In that case, and only in that case, is metric an accepted parameter. Note, moreover, that if passing both these parameters, then the dimension must be compatible with the chosen service.

Expand source code

@recast_method_sync
def create_collection(
    self,
    name: str,
    *,
    namespace: Optional[str] = None,
    dimension: Optional[int] = None,
    metric: Optional[str] = None,
    service: Optional[Union[CollectionVectorServiceOptions, Dict[str, Any]]] = None,
    indexing: Optional[Dict[str, Any]] = None,
    default_id_type: Optional[str] = None,
    additional_options: Optional[Dict[str, Any]] = None,
    check_exists: Optional[bool] = None,
    max_time_ms: Optional[int] = None,
) -> Collection:
    """
    Creates a collection on the database and return the Collection
    instance that represents it.

    This is a blocking operation: the method returns when the collection
    is ready to be used. As opposed to the `get_collection` instance,
    this method triggers causes the collection to be actually created on DB.

    Args:
        name: the name of the collection.
        namespace: the namespace where the collection is to be created.
            If not specified, the general setting for this database is used.
        dimension: for vector collections, the dimension of the vectors
            (i.e. the number of their components).
        metric: the similarity metric used for vector searches.
            Allowed values are `VectorMetric.DOT_PRODUCT`, `VectorMetric.EUCLIDEAN`
            or `VectorMetric.COSINE` (default).
        service: a dictionary describing a service for
            embedding computation, e.g. `{"provider": "ab", "modelName": "xy"}`.
            Alternatively, a CollectionVectorServiceOptions object to the same effect.
            NOTE: This feature is under current development.
        indexing: optional specification of the indexing options for
            the collection, in the form of a dictionary such as
                {"deny": [...]}
            or
                {"allow": [...]}
        default_id_type: this sets what type of IDs the API server will
            generate when inserting documents that do not specify their
            `_id` field explicitly. Can be set to any of the values
            `DefaultIdType.UUID`, `DefaultIdType.OBJECTID`,
            `DefaultIdType.UUIDV6`, `DefaultIdType.UUIDV7`,
            `DefaultIdType.DEFAULT`.
        additional_options: any further set of key-value pairs that will
            be added to the "options" part of the payload when sending
            the Data API command to create a collection.
        check_exists: whether to run an existence check for the collection
            name before attempting to create the collection:
            If check_exists is True, an error is raised when creating
            an existing collection.
            If it is False, the creation is attempted. In this case, for
            preexisting collections, the command will succeed or fail
            depending on whether the options match or not.
        max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

    Returns:
        a (synchronous) `Collection` instance, representing the
        newly-created collection.

    Example:
        >>> new_col = my_db.create_collection("my_v_col", dimension=3)
        >>> new_col.insert_one({"name": "the_row"}, vector=[0.4, 0.5, 0.7])
        InsertOneResult(raw_results=..., inserted_id='e22dd65e-...-...-...')

    Note:
        A collection is considered a vector collection if at least one of
        `dimension` or `service` are provided and not null. In that case,
        and only in that case, is `metric` an accepted parameter.
        Note, moreover, that if passing both these parameters, then
        the dimension must be compatible with the chosen service.
    """

    _validate_create_collection_options(
        dimension=dimension,
        metric=metric,
        service=service,
        indexing=indexing,
        default_id_type=default_id_type,
        additional_options=additional_options,
    )
    _options = {
        **(additional_options or {}),
        **({"indexing": indexing} if indexing else {}),
        **({"defaultId": {"type": default_id_type}} if default_id_type else {}),
    }

    timeout_manager = MultiCallTimeoutManager(overall_max_time_ms=max_time_ms)

    if check_exists is None:
        _check_exists = True
    else:
        _check_exists = check_exists
    existing_names: List[str]
    if _check_exists:
        logger.info(f"checking collection existence for '{name}'")
        existing_names = self.list_collection_names(
            namespace=namespace,
            max_time_ms=timeout_manager.remaining_timeout_ms(),
        )
    else:
        existing_names = []

    driver_db = self._astra_db.copy(namespace=namespace)
    if name in existing_names:
        raise CollectionAlreadyExistsException(
            text=f"CollectionInvalid: collection {name} already exists",
            namespace=driver_db.namespace,
            collection_name=name,
        )

    service_dict: Optional[Dict[str, Any]]
    if service is not None:
        service_dict = service if isinstance(service, dict) else service.as_dict()
    else:
        service_dict = None

    logger.info(f"creating collection '{name}'")
    driver_db.create_collection(
        name,
        options=_options,
        dimension=dimension,
        metric=metric,
        service_dict=service_dict,
        timeout_info=timeout_manager.remaining_timeout_info(),
    )
    logger.info(f"finished creating collection '{name}'")
    return self.get_collection(name, namespace=namespace)

def drop_collection(self, name_or_collection: Union[str, Collection], *, max_time_ms: Optional[int] = None) ‑> Dict[str, Any]

Drop a collection from the database, along with all documents therein.

Args

name_or_collection

either the name of a collection or a Collection instance.

max_time_ms

a timeout, in milliseconds, for the underlying HTTP request.

Returns

a dictionary in the form {"ok": 1} if the command succeeds.

Example

>>> my_db.list_collection_names()
['a_collection', 'my_v_col', 'another_col']
>>> my_db.drop_collection("my_v_col")
{'ok': 1}
>>> my_db.list_collection_names()
['a_collection', 'another_col']

Note

when providing a collection name, it is assumed that the collection is to be found in the namespace set at database instance level.

Expand source code

@recast_method_sync
def drop_collection(
    self,
    name_or_collection: Union[str, Collection],
    *,
    max_time_ms: Optional[int] = None,
) -> Dict[str, Any]:
    """
    Drop a collection from the database, along with all documents therein.

    Args:
        name_or_collection: either the name of a collection or
            a `Collection` instance.
        max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

    Returns:
        a dictionary in the form {"ok": 1} if the command succeeds.

    Example:
        >>> my_db.list_collection_names()
        ['a_collection', 'my_v_col', 'another_col']
        >>> my_db.drop_collection("my_v_col")
        {'ok': 1}
        >>> my_db.list_collection_names()
        ['a_collection', 'another_col']

    Note:
        when providing a collection name, it is assumed that the collection
        is to be found in the namespace set at database instance level.
    """

    # lazy importing here against circular-import error
    from astrapy.collection import Collection

    if isinstance(name_or_collection, Collection):
        _namespace = name_or_collection.namespace
        _name: str = name_or_collection.name
        logger.info(f"dropping collection '{_name}'")
        dc_response = self._astra_db.copy(namespace=_namespace).delete_collection(
            _name,
            timeout_info=base_timeout_info(max_time_ms),
        )
        logger.info(f"finished dropping collection '{_name}'")
        return dc_response.get("status", {})  # type: ignore[no-any-return]
    else:
        logger.info(f"dropping collection '{name_or_collection}'")
        dc_response = self._astra_db.delete_collection(
            name_or_collection,
            timeout_info=base_timeout_info(max_time_ms),
        )
        logger.info(f"finished dropping collection '{name_or_collection}'")
        return dc_response.get("status", {})  # type: ignore[no-any-return]

def get_collection(self, name: str, *, namespace: Optional[str] = None) ‑> Collection

Spawn a Collection object instance representing a collection on this database.

Creating a Collection instance does not have any effect on the actual state of the database: in other words, for the created Collection instance to be used meaningfully, the collection must exist already (for instance, it should have been created previously by calling the create_collection method).

Args

name

the name of the collection.

namespace

the namespace containing the collection. If no namespace is specified, the general setting for this database is used.

Returns

a Collection instance, representing the desired collection (but without any form of validation).

Example

>>> my_col = my_db.get_collection("my_collection")
>>> my_col.count_documents({}, upper_bound=100)
41

Note

The attribute and indexing syntax forms achieve the same effect as this method. In other words, the following are equivalent: my_db.get_collection("coll_name") my_db.coll_name my_db["coll_name"]

Expand source code

def get_collection(
    self, name: str, *, namespace: Optional[str] = None
) -> Collection:
    """
    Spawn a `Collection` object instance representing a collection
    on this database.

    Creating a `Collection` instance does not have any effect on the
    actual state of the database: in other words, for the created
    `Collection` instance to be used meaningfully, the collection
    must exist already (for instance, it should have been created
    previously by calling the `create_collection` method).

    Args:
        name: the name of the collection.
        namespace: the namespace containing the collection. If no namespace
            is specified, the general setting for this database is used.

    Returns:
        a `Collection` instance, representing the desired collection
            (but without any form of validation).

    Example:
        >>> my_col = my_db.get_collection("my_collection")
        >>> my_col.count_documents({}, upper_bound=100)
        41

    Note:
        The attribute and indexing syntax forms achieve the same effect
        as this method. In other words, the following are equivalent:
            my_db.get_collection("coll_name")
            my_db.coll_name
            my_db["coll_name"]
    """

    # lazy importing here against circular-import error
    from astrapy.collection import Collection

    _namespace = namespace or self._astra_db.namespace
    return Collection(self, name, namespace=_namespace)

def get_database_admin(self, *, token: Optional[str] = None, dev_ops_url: Optional[str] = None, dev_ops_api_version: Optional[str] = None) ‑> AstraDBDatabaseAdmin

Return an AstraDBDatabaseAdmin object corresponding to this database, for use in admin tasks such as managing namespaces.

Args

token

an access token with enough permission on the database to perform the desired tasks. If omitted (as it can generally be done), the token of this Database is used.

dev_ops_url

in case of custom deployments, this can be used to specify the URL to the DevOps API, such as "https://api.astra.datastax.com". Generally it can be omitted. The environment (prod/dev/…) is determined from the API Endpoint.

dev_ops_api_version

this can specify a custom version of the DevOps API (such as "v2"). Generally not needed.

Returns

An AstraDBDatabaseAdmin instance targeting this database.

Example

>>> my_db_admin = my_db.get_database_admin()
>>> if "new_namespace" not in my_db_admin.list_namespaces():
...     my_db_admin.create_namespace("new_namespace")
>>> my_db_admin.list_namespaces()
['default_keyspace', 'new_namespace']

Expand source code

def get_database_admin(
    self,
    *,
    token: Optional[str] = None,
    dev_ops_url: Optional[str] = None,
    dev_ops_api_version: Optional[str] = None,
) -> AstraDBDatabaseAdmin:
    """
    Return an AstraDBDatabaseAdmin object corresponding to this database, for
    use in admin tasks such as managing namespaces.

    Args:
        token: an access token with enough permission on the database to
            perform the desired tasks. If omitted (as it can generally be done),
            the token of this Database is used.
        dev_ops_url: in case of custom deployments, this can be used to specify
            the URL to the DevOps API, such as "https://api.astra.datastax.com".
            Generally it can be omitted. The environment (prod/dev/...) is
            determined from the API Endpoint.
        dev_ops_api_version: this can specify a custom version of the DevOps API
            (such as "v2"). Generally not needed.

    Returns:
        An AstraDBDatabaseAdmin instance targeting this database.

    Example:
        >>> my_db_admin = my_db.get_database_admin()
        >>> if "new_namespace" not in my_db_admin.list_namespaces():
        ...     my_db_admin.create_namespace("new_namespace")
        >>> my_db_admin.list_namespaces()
        ['default_keyspace', 'new_namespace']
    """

    # lazy importing here to avoid circular dependency
    from astrapy.admin import AstraDBDatabaseAdmin

    return AstraDBDatabaseAdmin.from_api_endpoint(
        api_endpoint=self._astra_db.api_endpoint,
        token=token or self._astra_db.token,
        caller_name=self._astra_db.caller_name,
        caller_version=self._astra_db.caller_version,
        dev_ops_url=dev_ops_url,
        dev_ops_api_version=dev_ops_api_version,
    )

def info(self) ‑> DatabaseInfo

Additional information on the database as a DatabaseInfo instance.

Some of the returned properties are dynamic throughout the lifetime of the database (such as raw_info["keyspaces"]). For this reason, each invocation of this method triggers a new request to the DevOps API.

Example

>>> my_db.info().region
'eu-west-1'

>>> my_db.info().raw_info['datacenters'][0]['dateCreated']
'2023-01-30T12:34:56Z'

Note

see the DatabaseInfo documentation for a caveat about the difference between the region and the raw_info["region"] attributes.

Expand source code

def info(self) -> DatabaseInfo:
    """
    Additional information on the database as a DatabaseInfo instance.

    Some of the returned properties are dynamic throughout the lifetime
    of the database (such as raw_info["keyspaces"]). For this reason,
    each invocation of this method triggers a new request to the DevOps API.

    Example:
        >>> my_db.info().region
        'eu-west-1'

        >>> my_db.info().raw_info['datacenters'][0]['dateCreated']
        '2023-01-30T12:34:56Z'

    Note:
        see the DatabaseInfo documentation for a caveat about the difference
        between the `region` and the `raw_info["region"]` attributes.
    """

    logger.info("getting database info")
    database_info = fetch_database_info(
        self._astra_db.api_endpoint,
        token=self._astra_db.token,
        namespace=self.namespace,
    )
    if database_info is not None:
        logger.info("finished getting database info")
        return database_info
    else:
        raise DevOpsAPIException(
            "Database is not in a supported environment for this operation."
        )

def list_collection_names(self, *, namespace: Optional[str] = None, max_time_ms: Optional[int] = None) ‑> List[str]

List the names of all collections in a given namespace of this database.

Args

namespace

the namespace to be inspected. If not specified, the general setting for this database is assumed.

max_time_ms

a timeout, in milliseconds, for the underlying HTTP request.

Returns

a list of the collection names as strings, in no particular order.

Example

>>> my_db.list_collection_names()
['a_collection', 'another_col']

Expand source code

@recast_method_sync
def list_collection_names(
    self,
    *,
    namespace: Optional[str] = None,
    max_time_ms: Optional[int] = None,
) -> List[str]:
    """
    List the names of all collections in a given namespace of this database.

    Args:
        namespace: the namespace to be inspected. If not specified,
            the general setting for this database is assumed.
        max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

    Returns:
        a list of the collection names as strings, in no particular order.

    Example:
        >>> my_db.list_collection_names()
        ['a_collection', 'another_col']
    """

    logger.info("getting collection names")
    gc_response = self._astra_db.copy(namespace=namespace).get_collections(
        timeout_info=base_timeout_info(max_time_ms)
    )
    if "collections" not in gc_response.get("status", {}):
        raise DataAPIFaultyResponseException(
            text="Faulty response from get_collections API command.",
            raw_response=gc_response,
        )
    else:
        # we know this is a list of strings
        logger.info("finished getting collection names")
        return gc_response["status"]["collections"]  # type: ignore[no-any-return]

def list_collections(self, *, namespace: Optional[str] = None, max_time_ms: Optional[int] = None) ‑> CommandCursor[CollectionDescriptor]

List all collections in a given namespace for this database.

Args

namespace

the namespace to be inspected. If not specified, the general setting for this database is assumed.

max_time_ms

a timeout, in milliseconds, for the underlying HTTP request.

Returns

a CommandCursor to iterate over CollectionDescriptor instances, each corresponding to a collection.

Example

>>> ccur = my_db.list_collections()
>>> ccur
<astrapy.cursors.CommandCursor object at ...>
>>> list(ccur)
[CollectionDescriptor(name='my_v_col', options=CollectionOptions())]
>>> for coll_dict in my_db.list_collections():
...     print(coll_dict)
...
CollectionDescriptor(name='my_v_col', options=CollectionOptions())

Expand source code

@recast_method_sync
def list_collections(
    self,
    *,
    namespace: Optional[str] = None,
    max_time_ms: Optional[int] = None,
) -> CommandCursor[CollectionDescriptor]:
    """
    List all collections in a given namespace for this database.

    Args:
        namespace: the namespace to be inspected. If not specified,
            the general setting for this database is assumed.
        max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.

    Returns:
        a `CommandCursor` to iterate over CollectionDescriptor instances,
        each corresponding to a collection.

    Example:
        >>> ccur = my_db.list_collections()
        >>> ccur
        <astrapy.cursors.CommandCursor object at ...>
        >>> list(ccur)
        [CollectionDescriptor(name='my_v_col', options=CollectionOptions())]
        >>> for coll_dict in my_db.list_collections():
        ...     print(coll_dict)
        ...
        CollectionDescriptor(name='my_v_col', options=CollectionOptions())
    """

    if namespace:
        _client = self._astra_db.copy(namespace=namespace)
    else:
        _client = self._astra_db
    logger.info("getting collections")
    gc_response = _client.get_collections(
        options={"explain": True}, timeout_info=base_timeout_info(max_time_ms)
    )
    if "collections" not in gc_response.get("status", {}):
        raise DataAPIFaultyResponseException(
            text="Faulty response from get_collections API command.",
            raw_response=gc_response,
        )
    else:
        # we know this is a list of dicts, to marshal into "descriptors"
        logger.info("finished getting collections")
        return CommandCursor(
            address=self._astra_db.base_url,
            items=[
                CollectionDescriptor.from_dict(col_dict)
                for col_dict in gc_response["status"]["collections"]
            ],
        )

def name(self) ‑> str

The name of this database. Note that this bears no unicity guarantees.

Calling this method the first time involves a request to the DevOps API (the resulting database name is then cached). See the astrapy.info method for more details.

Example

>>> my_db.name()
'the_application_database'

Expand source code

def name(self) -> str:
    """
    The name of this database. Note that this bears no unicity guarantees.

    Calling this method the first time involves a request
    to the DevOps API (the resulting database name is then cached).
    See the `info()` method for more details.

    Example:
        >>> my_db.name()
        'the_application_database'
    """

    if self._name is None:
        self._name = self.info().name
    return self._name

def set_caller(self, caller_name: Optional[str] = None, caller_version: Optional[str] = None) ‑> None

Set a new identity for the application/framework on behalf of which the Data API calls are performed (the "caller").

Args

caller_name

name of the application, or framework, on behalf of which the Data API calls are performed. This ends up in the request user-agent.

caller_version

version of the caller.

Example

>>> my_db.set_caller(caller_name="the_caller", caller_version="0.1.0")

Expand source code

def set_caller(
    self,
    caller_name: Optional[str] = None,
    caller_version: Optional[str] = None,
) -> None:
    """
    Set a new identity for the application/framework on behalf of which
    the Data API calls are performed (the "caller").

    Args:
        caller_name: name of the application, or framework, on behalf of which
            the Data API calls are performed. This ends up in the request user-agent.
        caller_version: version of the caller.

    Example:
        >>> my_db.set_caller(caller_name="the_caller", caller_version="0.1.0")
    """

    logger.info(f"setting caller to {caller_name}/{caller_version}")
    self._astra_db.set_caller(
        caller_name=caller_name,
        caller_version=caller_version,
    )

def to_async(self, *, api_endpoint: Optional[str] = None, token: Optional[str] = None, namespace: Optional[str] = None, caller_name: Optional[str] = None, caller_version: Optional[str] = None, api_path: Optional[str] = None, api_version: Optional[str] = None) ‑> AsyncDatabase

Create an AsyncDatabase from this one. Save for the arguments explicitly provided as overrides, everything else is kept identical to this database in the copy.

Args

api_endpoint

the full "API Endpoint" string used to reach the Data API. Example: "https://-.apps.astra.datastax.com"

token

an Access Token to the database. Example: "AstraCS:xyz…"

namespace

this is the namespace all method calls will target, unless one is explicitly specified in the call. If no namespace is supplied when creating a Database, the name "default_namespace" is set.

caller_name

name of the application, or framework, on behalf of which the Data API calls are performed. This ends up in the request user-agent.

caller_version

version of the caller.

api_path

path to append to the API Endpoint. In typical usage, this should be left to its default of "/api/json".

api_version

version specifier to append to the API path. In typical usage, this should be left to its default of "v1".

Returns

the new copy, an AsyncDatabase instance.

Example

>>> my_async_db = my_db.to_async()
>>> asyncio.run(my_async_db.list_collection_names())

Expand source code

def to_async(
    self,
    *,
    api_endpoint: Optional[str] = None,
    token: Optional[str] = None,
    namespace: Optional[str] = None,
    caller_name: Optional[str] = None,
    caller_version: Optional[str] = None,
    api_path: Optional[str] = None,
    api_version: Optional[str] = None,
) -> AsyncDatabase:
    """
    Create an AsyncDatabase from this one. Save for the arguments
    explicitly provided as overrides, everything else is kept identical
    to this database in the copy.

    Args:
        api_endpoint: the full "API Endpoint" string used to reach the Data API.
            Example: "https://<database_id>-<region>.apps.astra.datastax.com"
        token: an Access Token to the database. Example: "AstraCS:xyz..."
        namespace: this is the namespace all method calls will target, unless
            one is explicitly specified in the call. If no namespace is supplied
            when creating a Database, the name "default_namespace" is set.
        caller_name: name of the application, or framework, on behalf of which
            the Data API calls are performed. This ends up in the request user-agent.
        caller_version: version of the caller.
        api_path: path to append to the API Endpoint. In typical usage, this
            should be left to its default of "/api/json".
        api_version: version specifier to append to the API path. In typical
            usage, this should be left to its default of "v1".

    Returns:
        the new copy, an `AsyncDatabase` instance.

    Example:
        >>> my_async_db = my_db.to_async()
        >>> asyncio.run(my_async_db.list_collection_names())
    """

    return AsyncDatabase(
        api_endpoint=api_endpoint or self._astra_db.api_endpoint,
        token=token or self._astra_db.token,
        namespace=namespace or self._astra_db.namespace,
        caller_name=caller_name or self._astra_db.caller_name,
        caller_version=caller_version or self._astra_db.caller_version,
        api_path=api_path or self._astra_db.api_path,
        api_version=api_version or self._astra_db.api_version,
    )

def with_options(self, *, namespace: Optional[str] = None, caller_name: Optional[str] = None, caller_version: Optional[str] = None) ‑> Database

Create a clone of this database with some changed attributes.

Args

namespace

this is the namespace all method calls will target, unless one is explicitly specified in the call. If no namespace is supplied when creating a Database, the name "default_namespace" is set.

caller_name

name of the application, or framework, on behalf of which the Data API calls are performed. This ends up in the request user-agent.

caller_version

version of the caller.

Returns

a new Database instance.

Example

>>> my_db_2 = my_db.with_options(
...     namespace="the_other_namespace",
...     caller_name="the_caller",
...     caller_version="0.1.0",
... )

Expand source code

def with_options(
    self,
    *,
    namespace: Optional[str] = None,
    caller_name: Optional[str] = None,
    caller_version: Optional[str] = None,
) -> Database:
    """
    Create a clone of this database with some changed attributes.

    Args:
        namespace: this is the namespace all method calls will target, unless
            one is explicitly specified in the call. If no namespace is supplied
            when creating a Database, the name "default_namespace" is set.
        caller_name: name of the application, or framework, on behalf of which
            the Data API calls are performed. This ends up in the request user-agent.
        caller_version: version of the caller.

    Returns:
        a new `Database` instance.

    Example:
        >>> my_db_2 = my_db.with_options(
        ...     namespace="the_other_namespace",
        ...     caller_name="the_caller",
        ...     caller_version="0.1.0",
        ... )
    """

    return self._copy(
        namespace=namespace,
        caller_name=caller_name,
        caller_version=caller_version,
    )