Module astrapy.data.info.table_descriptor.table_columns

Classes

class TableAPISupportDescriptor (cql_definition: str, create_table: bool, insert: bool, filter: bool, read: bool)

Represents the additional support information returned by the Data API when describing columns of a table. Some columns indeed require a detailed description of what operations are supported on them - this includes, but is not limited to, columns created by means other than the Data API (e.g. CQL direct interaction with the database).

When the Data API reports these columns (in listing the tables and their metadata), it provides the information marshaled in this object to detail which level of support the column has (for instance, it can be a partial support whereby the column is readable by the API but not writable).

Attributes

cql_definition

a free-form string containing the CQL definition for the column.

create_table

whether a column of this nature can be used in API table creation.

insert

whether a column of this nature can be written through the API.

filter

whether a column of this nature can be used for filtering with API find.

read

whether a column of this nature can be read through the API.

Expand source code

@dataclass
class TableAPISupportDescriptor:
    """
    Represents the additional support information returned by the Data API when
    describing columns of a table. Some columns indeed require a detailed description
    of what operations are supported on them - this includes, but is not limited to,
    columns created by means other than the Data API (e.g. CQL direct interaction
    with the database).

    When the Data API reports these columns (in listing the tables and their metadata),
    it provides the information marshaled in this object to detail which level
    of support the column has (for instance, it can be a partial support whereby the
    column is readable by the API but not writable).

    Attributes:
        cql_definition: a free-form string containing the CQL definition for the column.
        create_table: whether a column of this nature can be used in API table creation.
        insert: whether a column of this nature can be written through the API.
        filter: whether a column of this nature can be used for filtering with API find.
        read: whether a column of this nature can be read through the API.
    """

    cql_definition: str
    create_table: bool
    insert: bool
    filter: bool
    read: bool

    def __repr__(self) -> str:
        desc = ", ".join(
            [
                f'"{self.cql_definition}"',
                f"create_table={self.create_table}",
                f"insert={self.insert}",
                f"filter={self.filter}",
                f"read={self.read}",
            ]
        )
        return f"{self.__class__.__name__}({desc})"

    def as_dict(self) -> dict[str, Any]:
        """Recast this object into a dictionary."""

        return {
            "cqlDefinition": self.cql_definition,
            "createTable": self.create_table,
            "insert": self.insert,
            "filter": self.filter,
            "read": self.read,
        }

    @classmethod
    def _from_dict(cls, raw_dict: dict[str, Any]) -> TableAPISupportDescriptor:
        """
        Create an instance of TableAPISupportDescriptor from a dictionary
        such as one from the Data API.
        """

        _warn_residual_keys(
            cls,
            raw_dict,
            {"cqlDefinition", "createTable", "insert", "filter", "read"},
        )
        return TableAPISupportDescriptor(
            cql_definition=raw_dict["cqlDefinition"],
            create_table=raw_dict["createTable"],
            insert=raw_dict["insert"],
            filter=raw_dict["filter"],
            read=raw_dict["read"],
        )

Class variables

var cql_definition : str

var create_table : bool

var filter : bool

var insert : bool

var read : bool

Methods

def as_dict(self) ‑> dict[str, typing.Any]

Recast this object into a dictionary.

Expand source code

def as_dict(self) -> dict[str, Any]:
    """Recast this object into a dictionary."""

    return {
        "cqlDefinition": self.cql_definition,
        "createTable": self.create_table,
        "insert": self.insert,
        "filter": self.filter,
        "read": self.read,
    }

class TableColumnTypeDescriptor (column_type: ColumnType | TableValuedColumnType | TableKeyValuedColumnType | TableVectorColumnType | TableUnsupportedColumnType | TableUDTColumnType | TablePassthroughColumnType, api_support: TableAPISupportDescriptor | None)

Represents and describes a column in a Table, with its type and any additional property.

This is an abstract class, whose concrete implementation are the various kinds of column descriptors such as TableScalarColumnTypeDescriptor, TableVectorColumnTypeDescriptor, TableValuedColumnTypeDescriptor, and so on.

Attributes

column_type

an instance of one of the various column-type classes, according to the type of the column. In other words, each subclass of TableColumnTypeDescriptor has an appropriate object as its column_type attributes. For example the column_type of TableValuedColumnTypeDescriptor is a TableValuedColumnType.

api_support

a TableAPISupportDescriptor object giving more details.

Expand source code

@dataclass
class TableColumnTypeDescriptor(ABC):
    """
    Represents and describes a column in a Table, with its type and any
    additional property.

    This is an abstract class, whose concrete implementation are the various
    kinds of column descriptors such as `TableScalarColumnTypeDescriptor`,
    `TableVectorColumnTypeDescriptor`, `TableValuedColumnTypeDescriptor`, and so on.

    Attributes:
        column_type: an instance of one of the various column-type classes, according
            to the type of the column. In other words, each subclass of
            `TableColumnTypeDescriptor` has an appropriate object as its
            `column_type` attributes.
            For example the `column_type` of `TableValuedColumnTypeDescriptor`
            is a `TableValuedColumnType`.
        api_support: a `TableAPISupportDescriptor` object giving more details.
    """

    column_type: (
        ColumnType
        | TableValuedColumnType
        | TableKeyValuedColumnType
        | TableVectorColumnType
        | TableUnsupportedColumnType
        | TableUDTColumnType
        | TablePassthroughColumnType
    )
    api_support: TableAPISupportDescriptor | None

    @abstractmethod
    def as_dict(self) -> dict[str, Any]: ...

    def as_spec(self) -> dict[str, Any] | str:
        """
        Return a representation of this column type for use within schema
        description, preferring the short form ("INT") over the long form
        ({"type": "INT"}) when applicable.
        """

        return self.as_dict()

    @classmethod
    def _from_dict(cls, raw_dict: dict[str, Any]) -> TableColumnTypeDescriptor:
        """
        Create an instance of TableColumnTypeDescriptor from a dictionary
        such as one from the Data API.

        This method switches to the proper subclass depending on the input.
        """

        if "type" in raw_dict:
            if "keyType" in raw_dict and raw_dict["type"] in TableKeyValuedColumnType:
                return TableKeyValuedColumnTypeDescriptor._from_dict(raw_dict)
            elif "valueType" in raw_dict and raw_dict["type"] in TableValuedColumnType:
                return TableValuedColumnTypeDescriptor._from_dict(raw_dict)
            elif raw_dict["type"] in TableVectorColumnType:
                return TableVectorColumnTypeDescriptor._from_dict(raw_dict)
            elif raw_dict["type"] in ColumnType:
                return TableScalarColumnTypeDescriptor._from_dict(raw_dict)
            elif "udtName" in raw_dict and raw_dict["type"] in TableUDTColumnType:
                return TableUDTColumnDescriptor._from_dict(raw_dict)
            elif raw_dict["type"] in TableUnsupportedColumnType:
                return TableUnsupportedColumnTypeDescriptor._from_dict(raw_dict)
        # This catch-all case must not error, rather return a 'passthrough' column type
        # (future-proof for yet-unknown column types to come and incomplete info
        # such as e.g. 'map' without key/value type info because static).
        return TablePassthroughColumnTypeDescriptor._from_dict(raw_dict)

    @classmethod
    def coerce(
        cls, raw_input: ColumnType | TableColumnTypeDescriptor | dict[str, Any] | str
    ) -> TableColumnTypeDescriptor:
        """
        Normalize the input, whether an object already or a plain dictionary
        of the right structure, into a TableColumnTypeDescriptor.
        """

        if isinstance(raw_input, TableColumnTypeDescriptor):
            return raw_input
        elif isinstance(raw_input, ColumnType):
            return cls._from_dict({"type": raw_input.value})
        elif isinstance(raw_input, str):
            return cls._from_dict({"type": raw_input})
        else:
            return cls._from_dict(raw_input)

Ancestors

• abc.ABC

Subclasses

• TableKeyValuedColumnTypeDescriptor
• TablePassthroughColumnTypeDescriptor
• TableScalarColumnTypeDescriptor
• TableUDTColumnDescriptor
• TableUnsupportedColumnTypeDescriptor
• TableValuedColumnTypeDescriptor
• TableVectorColumnTypeDescriptor

Class variables

var api_support : TableAPISupportDescriptor | None

var column_type : ColumnType | TableValuedColumnType | TableKeyValuedColumnType | TableVectorColumnType | TableUnsupportedColumnType | TableUDTColumnType | TablePassthroughColumnType

Static methods

def coerce(raw_input: ColumnType | TableColumnTypeDescriptor | dict[str, Any] | str) ‑> TableColumnTypeDescriptor

Normalize the input, whether an object already or a plain dictionary of the right structure, into a TableColumnTypeDescriptor.

Expand source code

@classmethod
def coerce(
    cls, raw_input: ColumnType | TableColumnTypeDescriptor | dict[str, Any] | str
) -> TableColumnTypeDescriptor:
    """
    Normalize the input, whether an object already or a plain dictionary
    of the right structure, into a TableColumnTypeDescriptor.
    """

    if isinstance(raw_input, TableColumnTypeDescriptor):
        return raw_input
    elif isinstance(raw_input, ColumnType):
        return cls._from_dict({"type": raw_input.value})
    elif isinstance(raw_input, str):
        return cls._from_dict({"type": raw_input})
    else:
        return cls._from_dict(raw_input)

Methods

def as_dict(self) ‑> dict[str, typing.Any]

Expand source code

@abstractmethod
def as_dict(self) -> dict[str, Any]: ...

def as_spec(self) ‑> dict[str, typing.Any] | str

Return a representation of this column type for use within schema description, preferring the short form ("INT") over the long form ({"type": "INT"}) when applicable.

Expand source code

def as_spec(self) -> dict[str, Any] | str:
    """
    Return a representation of this column type for use within schema
    description, preferring the short form ("INT") over the long form
    ({"type": "INT"}) when applicable.
    """

    return self.as_dict()

class TableKeyValuedColumnTypeDescriptor (*, value_type: str | dict[str, Any] | ColumnType | TableColumnTypeDescriptor, key_type: str | dict[str, Any] | ColumnType | TableColumnTypeDescriptor, column_type: str | TableKeyValuedColumnType = TableKeyValuedColumnType.MAP, api_support: TableAPISupportDescriptor | None = None)

Represents and describes a column in a Table, of a 'key-value' type, that stores an associative map (essentially a dict) between keys of a certain scalar type and values of a certain scalar type. The only such kind of column is a "map".

Attributes

column_type

an instance of TableKeyValuedColumnType. When creating the object, this can be omitted as it only ever assumes the "MAP" value.

key_type

the type of the individual keys in the map column. This is a TableColumnTypeDescriptor, but when creating the object, equivalent dictionaries, as well as strings such as "TEXT" or "UUID" or ColumnType entries, are also accepted. Using a column type not eligible to be a key will return a Data API error.

value_type

the type of the individual items stored in the column. This is a TableColumnTypeDescriptor, but when creating the object, equivalent dictionaries, as well as strings such as "TEXT" or "UUID" or ColumnType entries, are also accepted.

api_support

a TableAPISupportDescriptor object giving more details.

Expand source code

@dataclass
class TableKeyValuedColumnTypeDescriptor(TableColumnTypeDescriptor):
    """
    Represents and describes a column in a Table, of a 'key-value' type, that stores
    an associative map (essentially a dict) between keys of a certain scalar type and
    values of a certain scalar type. The only such kind of column is a "map".

    Attributes:
        column_type: an instance of `TableKeyValuedColumnType`. When creating the
            object, this can be omitted as it only ever assumes the "MAP" value.
        key_type: the type of the individual keys in the map column.
            This is a `TableColumnTypeDescriptor`, but when creating the object,
            equivalent dictionaries, as well as strings such as "TEXT" or "UUID"
            or ColumnType entries, are also accepted. Using a column type not
            eligible to be a key will return a Data API error.
        value_type: the type of the individual items stored in the column.
            This is a `TableColumnTypeDescriptor`, but when creating the object,
            equivalent dictionaries, as well as strings such as "TEXT" or "UUID"
            or ColumnType entries, are also accepted.
        api_support: a `TableAPISupportDescriptor` object giving more details.
    """

    column_type: TableKeyValuedColumnType
    key_type: TableColumnTypeDescriptor
    value_type: TableColumnTypeDescriptor

    def __init__(
        self,
        *,
        value_type: str | dict[str, Any] | ColumnType | TableColumnTypeDescriptor,
        key_type: str | dict[str, Any] | ColumnType | TableColumnTypeDescriptor,
        column_type: str | TableKeyValuedColumnType = TableKeyValuedColumnType.MAP,
        api_support: TableAPISupportDescriptor | None = None,
    ) -> None:
        self.key_type = TableColumnTypeDescriptor.coerce(key_type)
        self.value_type = TableColumnTypeDescriptor.coerce(value_type)
        super().__init__(
            column_type=TableKeyValuedColumnType.coerce(column_type),
            api_support=api_support,
        )

    def __repr__(self) -> str:
        return (
            f"{self.__class__.__name__}({self.column_type.value}"
            f"<{self.key_type},{self.value_type}>)"
        )

    def as_dict(self) -> dict[str, Any]:
        """Recast this object into a dictionary."""

        return {
            k: v
            for k, v in {
                "type": self.column_type.value,
                "keyType": self.key_type.as_dict(),
                "valueType": self.value_type.as_dict(),
                "apiSupport": self.api_support.as_dict() if self.api_support else None,
            }.items()
            if v is not None
        }

    @override
    def as_spec(self) -> dict[str, Any]:
        return {
            k: v
            for k, v in {
                "type": self.column_type.value,
                "keyType": self.key_type.as_spec(),
                "valueType": self.value_type.as_spec(),
                "apiSupport": self.api_support.as_dict() if self.api_support else None,
            }.items()
            if v is not None
        }

    @classmethod
    def _from_dict(cls, raw_dict: dict[str, Any]) -> TableKeyValuedColumnTypeDescriptor:
        """
        Create an instance of TableKeyValuedColumnTypeDescriptor from a dictionary
        such as one from the Data API.
        """

        _warn_residual_keys(
            cls, raw_dict, {"type", "keyType", "valueType", "apiSupport"}
        )
        return TableKeyValuedColumnTypeDescriptor(
            column_type=raw_dict["type"],
            key_type=raw_dict["keyType"],
            value_type=raw_dict["valueType"],
            api_support=TableAPISupportDescriptor._from_dict(raw_dict["apiSupport"])
            if raw_dict.get("apiSupport")
            else None,
        )

Ancestors

• TableColumnTypeDescriptor
• abc.ABC

Class variables

var column_type : TableKeyValuedColumnType

var key_type : TableColumnTypeDescriptor

var value_type : TableColumnTypeDescriptor

Methods

def as_dict(self) ‑> dict[str, typing.Any]

Recast this object into a dictionary.

Expand source code

def as_dict(self) -> dict[str, Any]:
    """Recast this object into a dictionary."""

    return {
        k: v
        for k, v in {
            "type": self.column_type.value,
            "keyType": self.key_type.as_dict(),
            "valueType": self.value_type.as_dict(),
            "apiSupport": self.api_support.as_dict() if self.api_support else None,
        }.items()
        if v is not None
    }

Inherited members

• TableColumnTypeDescriptor:
  • as_spec
  • coerce

class TablePassthroughColumnTypeDescriptor (*, column_type: str | TablePassthroughColumnType = TablePassthroughColumnType.PASSTHROUGH, raw_descriptor: dict[str, Any], api_support: TableAPISupportDescriptor | None = None)

Represents and describes a column in a Table wich, for lack of information or understanding from the client, needs to be conveyed to the caller as-is. This is used during certain (schema- or data-) read operations.

This class is not meant for direct instantiation by the client user. Note that the column_type attribute is not mapped to the "type" key in the corresponding dictionary form.

Attributes

column_type

a TablePassthroughColumnType value. This can be omitted when creating the object. It only ever assumes the "PASSTHROUGH" value.

raw_descriptor

a free-form dictionary expressing the complete column description as it comes from the Data API. Part of this information is available in api_support, if such is provided.

api_support

a TableAPISupportDescriptor object giving more details.

Expand source code

@dataclass
class TablePassthroughColumnTypeDescriptor(TableColumnTypeDescriptor):
    """
    Represents and describes a column in a Table wich, for lack of information
    or understanding from the client, needs to be conveyed to the caller as-is.
    This is used during certain (schema- or data-) read operations.

    This class is not meant for direct instantiation by the client user. Note that
    the `column_type` attribute is *not* mapped to the "type" key in the corresponding
    dictionary form.

    Attributes:
        column_type: a `TablePassthroughColumnType` value. This can be omitted when
            creating the object. It only ever assumes the "PASSTHROUGH" value.
        raw_descriptor: a free-form dictionary expressing the complete column
            description as it comes from the Data API. Part of this information
            is available in `api_support`, if such is provided.
        api_support: a `TableAPISupportDescriptor` object giving more details.
    """

    column_type: TablePassthroughColumnType
    raw_descriptor: dict[str, Any]
    api_support: TableAPISupportDescriptor

    def __init__(
        self,
        *,
        column_type: str
        | TablePassthroughColumnType = TablePassthroughColumnType.PASSTHROUGH,
        raw_descriptor: dict[str, Any],
        api_support: TableAPISupportDescriptor | None = None,
    ) -> None:
        self.raw_descriptor = raw_descriptor
        super().__init__(
            column_type=TablePassthroughColumnType.coerce(column_type),
            api_support=api_support,
        )

    def __repr__(self) -> str:
        return f"{self.__class__.__name__}({str(self.as_dict())})"

    def as_dict(self) -> dict[str, Any]:
        """Recast this object into a dictionary."""

        return self.raw_descriptor

    @classmethod
    def _from_dict(
        cls, raw_dict: dict[str, Any]
    ) -> TablePassthroughColumnTypeDescriptor:
        """
        Create an instance of TablePassthroughColumnTypeDescriptor from a dictionary
        such as one from the Data API.
        """

        return TablePassthroughColumnTypeDescriptor(
            raw_descriptor=raw_dict,
            api_support=TableAPISupportDescriptor._from_dict(raw_dict["apiSupport"])
            if raw_dict.get("apiSupport")
            else None,
        )

Ancestors

• TableColumnTypeDescriptor
• abc.ABC

Class variables

var api_support : TableAPISupportDescriptor

var column_type : TablePassthroughColumnType

var raw_descriptor : dict[str, typing.Any]

Methods

def as_dict(self) ‑> dict[str, typing.Any]

Recast this object into a dictionary.

Expand source code

def as_dict(self) -> dict[str, Any]:
    """Recast this object into a dictionary."""

    return self.raw_descriptor

Inherited members

• TableColumnTypeDescriptor:
  • as_spec
  • coerce

class TablePrimaryKeyDescriptor (partition_by: list[str], partition_sort: dict[str, int])

Represents the part of a table definition that describes the primary key.

See the docstring for class CreateTableDefinition for in-context usage examples.

Attributes

partition_by

a list of column names forming the partition key, i.e. the portion of primary key that determines physical grouping and storage of rows on the database. Rows with the same values for the partition_by columns are guaranteed to be stored next to each other. This list cannot be empty.

partition_sort

this defines how rows are to be sorted within a partition. It is a dictionary that specifies, for each column of the primary key not in the partition_by field, whether the sorting is ascending or descending (see the values in the SortMode constant). The sorting within a partition considers all columns in this dictionary, in a hierarchical way: hence, ordering in this dictionary is relevant.

Expand source code

@dataclass
class TablePrimaryKeyDescriptor:
    """
    Represents the part of a table definition that describes the primary key.

    See the docstring for class `CreateTableDefinition` for in-context usage examples.

    Attributes:
        partition_by: a list of column names forming the partition key, i.e.
            the portion of primary key that determines physical grouping and storage
            of rows on the database. Rows with the same values for the partition_by
            columns are guaranteed to be stored next to each other. This list
            cannot be empty.
        partition_sort: this defines how rows are to be sorted within a partition.
            It is a dictionary that specifies, for each column of the primary key
            not in the `partition_by` field, whether the sorting is ascending
            or descending (see the values in the `SortMode` constant).
            The sorting within a partition considers all columns in this dictionary,
            in a hierarchical way: hence, ordering in this dictionary is relevant.
    """

    partition_by: list[str]
    partition_sort: dict[str, int]

    def __repr__(self) -> str:
        partition_key_block = ",".join(self.partition_by)
        clustering_block = ",".join(
            f"{clu_col_name}:{'a' if clu_col_sort > 0 else 'd'}"
            for clu_col_name, clu_col_sort in self.partition_sort.items()
        )
        pk_block = f"({partition_key_block}){clustering_block}"
        return f"{self.__class__.__name__}[{pk_block}]"

    def as_dict(self) -> dict[str, Any]:
        """Recast this object into a dictionary."""

        return {
            k: v
            for k, v in {
                "partitionBy": self.partition_by,
                "partitionSort": dict(self.partition_sort.items()),
            }.items()
            if v is not None
        }

    @classmethod
    def _from_dict(cls, raw_dict: dict[str, Any]) -> TablePrimaryKeyDescriptor:
        """
        Create an instance of TablePrimaryKeyDescriptor from a dictionary
        such as one from the Data API.
        """

        _warn_residual_keys(cls, raw_dict, {"partitionBy", "partitionSort"})
        return TablePrimaryKeyDescriptor(
            partition_by=raw_dict["partitionBy"],
            partition_sort=raw_dict["partitionSort"],
        )

    @classmethod
    def coerce(
        cls, raw_input: TablePrimaryKeyDescriptor | dict[str, Any] | str
    ) -> TablePrimaryKeyDescriptor:
        """
        Normalize the input, whether an object already or a plain dictionary
        of the right structure, into a TablePrimaryKeyDescriptor.
        """

        if isinstance(raw_input, TablePrimaryKeyDescriptor):
            return raw_input
        elif isinstance(raw_input, str):
            return cls._from_dict({"partitionBy": [raw_input], "partitionSort": {}})
        else:
            return cls._from_dict(raw_input)

Class variables

var partition_by : list[str]

var partition_sort : dict[str, int]

Static methods

def coerce(raw_input: TablePrimaryKeyDescriptor | dict[str, Any] | str) ‑> TablePrimaryKeyDescriptor

Normalize the input, whether an object already or a plain dictionary of the right structure, into a TablePrimaryKeyDescriptor.

Expand source code

@classmethod
def coerce(
    cls, raw_input: TablePrimaryKeyDescriptor | dict[str, Any] | str
) -> TablePrimaryKeyDescriptor:
    """
    Normalize the input, whether an object already or a plain dictionary
    of the right structure, into a TablePrimaryKeyDescriptor.
    """

    if isinstance(raw_input, TablePrimaryKeyDescriptor):
        return raw_input
    elif isinstance(raw_input, str):
        return cls._from_dict({"partitionBy": [raw_input], "partitionSort": {}})
    else:
        return cls._from_dict(raw_input)

Methods

def as_dict(self) ‑> dict[str, typing.Any]

Recast this object into a dictionary.

Expand source code

def as_dict(self) -> dict[str, Any]:
    """Recast this object into a dictionary."""

    return {
        k: v
        for k, v in {
            "partitionBy": self.partition_by,
            "partitionSort": dict(self.partition_sort.items()),
        }.items()
        if v is not None
    }

class TableScalarColumnTypeDescriptor (column_type: str | ColumnType, api_support: TableAPISupportDescriptor | None = None)

Represents and describes a column in a Table, of scalar type, i.e. which contains a single simple value.

See the docstring for class CreateTableDefinition for in-context usage examples.

Attributes

column_type

a ColumnType value. When creating the object, simple strings such as "TEXT" or "UUID" are also accepted.

api_support

a TableAPISupportDescriptor object giving more details.

Expand source code

@dataclass
class TableScalarColumnTypeDescriptor(TableColumnTypeDescriptor):
    """
    Represents and describes a column in a Table, of scalar type, i.e. which contains
    a single simple value.

    See the docstring for class `CreateTableDefinition` for in-context usage examples.

    Attributes:
        column_type: a `ColumnType` value. When creating the object,
            simple strings such as "TEXT" or "UUID" are also accepted.
        api_support: a `TableAPISupportDescriptor` object giving more details.
    """

    column_type: ColumnType

    def __init__(
        self,
        column_type: str | ColumnType,
        api_support: TableAPISupportDescriptor | None = None,
    ) -> None:
        super().__init__(
            column_type=ColumnType.coerce(column_type),
            api_support=api_support,
        )

    def __repr__(self) -> str:
        return f"{self.__class__.__name__}({self.column_type.value})"

    def as_dict(self) -> dict[str, Any]:
        """Recast this object into a dictionary."""

        return {
            k: v
            for k, v in {
                "type": self.column_type.value,
                "apiSupport": self.api_support.as_dict() if self.api_support else None,
            }.items()
            if v is not None
        }

    @override
    def as_spec(self) -> dict[str, Any] | str:
        if self.api_support is None:
            return self.column_type.value
        else:
            return self.as_dict()

    @classmethod
    def _from_dict(cls, raw_dict: dict[str, Any]) -> TableScalarColumnTypeDescriptor:
        """
        Create an instance of TableScalarColumnTypeDescriptor from a dictionary
        such as one from the Data API.
        """

        _warn_residual_keys(cls, raw_dict, {"type", "apiSupport"})
        return TableScalarColumnTypeDescriptor(
            column_type=raw_dict["type"],
            api_support=TableAPISupportDescriptor._from_dict(raw_dict["apiSupport"])
            if raw_dict.get("apiSupport")
            else None,
        )

Ancestors

• TableColumnTypeDescriptor
• abc.ABC

Class variables

var column_type : ColumnType

Methods

def as_dict(self) ‑> dict[str, typing.Any]

Recast this object into a dictionary.

Expand source code

def as_dict(self) -> dict[str, Any]:
    """Recast this object into a dictionary."""

    return {
        k: v
        for k, v in {
            "type": self.column_type.value,
            "apiSupport": self.api_support.as_dict() if self.api_support else None,
        }.items()
        if v is not None
    }

Inherited members

• TableColumnTypeDescriptor:
  • as_spec
  • coerce

class TableUDTColumnDescriptor (*, udt_name: str, column_type: str | TableUDTColumnType = TableUDTColumnType.USERDEFINED, api_support: TableAPISupportDescriptor | None = None, definition: CreateTypeDefinition | None = None)

Represents and describes a column in a Table, of a user-defined type (UDT) type, i.e. a previously-defined set of named fields, each with its type.

See the docstring for class CreateTableDefinition for in-context usage examples.

Attributes

column_type

a TableUDTColumnType value. This can be omitted when creating the object. It only ever assumes the "USERDEFINED" value.

udt_name

the name of the user-defined type for this column.

definition

a full type definition in the form of an object of type CreateTypeDefinition object. This attribute is optional, and as a matter of fact is only present in the context of data reads, to provide a complete schema for the data returned from the Data API within the 'projectionSchema' out-of-band information coming with the read.

api_support

a TableAPISupportDescriptor object giving more details.

Expand source code

@dataclass
class TableUDTColumnDescriptor(TableColumnTypeDescriptor):
    """
    Represents and describes a column in a Table, of a user-defined type (UDT) type,
    i.e. a previously-defined set of named fields, each with its type.

    See the docstring for class `CreateTableDefinition` for in-context usage examples.

    Attributes:
        column_type: a `TableUDTColumnType` value. This can be omitted when
            creating the object. It only ever assumes the "USERDEFINED" value.
        udt_name: the name of the user-defined type for this column.
        definition: a full type definition in the form of an object of type
            `astrapy.info.CreateTypeDefinition` object. This attribute is optional,
            and as a matter of fact is only present in the context of data reads,
            to provide a complete schema for the data returned from the Data API
            within the 'projectionSchema' out-of-band information coming with the read.
        api_support: a `TableAPISupportDescriptor` object giving more details.
    """

    column_type: TableUDTColumnType
    definition: CreateTypeDefinition | None
    udt_name: str

    def __init__(
        self,
        *,
        udt_name: str,
        column_type: str | TableUDTColumnType = TableUDTColumnType.USERDEFINED,
        api_support: TableAPISupportDescriptor | None = None,
        definition: CreateTypeDefinition | None = None,
    ) -> None:
        # lazy-import here to avoid circular import issues
        from astrapy.data.info.table_descriptor.type_creation import (
            CreateTypeDefinition,
        )

        self.udt_name = udt_name
        self.definition = (
            None if definition is None else CreateTypeDefinition.coerce(definition)
        )
        super().__init__(
            column_type=TableUDTColumnType.coerce(column_type),
            api_support=api_support,
        )

    def __repr__(self) -> str:
        return f"{self.__class__.__name__}({self.udt_name})"

    def as_dict(self) -> dict[str, Any]:
        """Recast this object into a dictionary."""

        return {
            k: v
            for k, v in {
                "type": self.column_type.value,
                "udtName": self.udt_name,
                "definition": self.definition.as_dict() if self.definition else None,
                "apiSupport": self.api_support.as_dict() if self.api_support else None,
            }.items()
            if v is not None
        }

    @classmethod
    def _from_dict(cls, raw_dict: dict[str, Any]) -> TableUDTColumnDescriptor:
        """
        Create an instance of TableUDTColumnDescriptor from a dictionary
        such as one from the Data API.
        """

        # lazy-import here to avoid circular import issues
        from astrapy.data.info.table_descriptor.type_creation import (
            CreateTypeDefinition,
        )

        _warn_residual_keys(
            cls,
            raw_dict,
            {"type", "udtName", "apiSupport", "definition"},
        )
        return TableUDTColumnDescriptor(
            column_type=raw_dict["type"],
            udt_name=raw_dict["udtName"],
            definition=CreateTypeDefinition._from_dict(raw_dict["definition"])
            if raw_dict.get("definition")
            else None,
            api_support=TableAPISupportDescriptor._from_dict(raw_dict["apiSupport"])
            if raw_dict.get("apiSupport")
            else None,
        )

Ancestors

• TableColumnTypeDescriptor
• abc.ABC

Class variables

var column_type : TableUDTColumnType

var definition : CreateTypeDefinition | None

var udt_name : str

Methods

def as_dict(self) ‑> dict[str, typing.Any]

Recast this object into a dictionary.

Expand source code

def as_dict(self) -> dict[str, Any]:
    """Recast this object into a dictionary."""

    return {
        k: v
        for k, v in {
            "type": self.column_type.value,
            "udtName": self.udt_name,
            "definition": self.definition.as_dict() if self.definition else None,
            "apiSupport": self.api_support.as_dict() if self.api_support else None,
        }.items()
        if v is not None
    }

Inherited members

• TableColumnTypeDescriptor:
  • as_spec
  • coerce

class TableUnsupportedColumnTypeDescriptor (*, column_type: TableUnsupportedColumnType | str, api_support: TableAPISupportDescriptor)

Represents and describes a column in a Table, of unsupported type.

Note that this column type descriptor cannot be used in table creation, rather it can only be returned when listing the tables or getting their metadata by the API.

Attributes

column_type

an instance of TableUnsupportedColumnType.

api_support

a TableAPISupportDescriptor object giving more details.

This class has no coerce method, since it is always only found in API responses.

Expand source code

@dataclass
class TableUnsupportedColumnTypeDescriptor(TableColumnTypeDescriptor):
    """
    Represents and describes a column in a Table, of unsupported type.

    Note that this column type descriptor cannot be used in table creation,
    rather it can only be returned when listing the tables or getting their
    metadata by the API.

    Attributes:
        column_type: an instance of `TableUnsupportedColumnType`.
        api_support: a `TableAPISupportDescriptor` object giving more details.

    This class has no `coerce` method, since it is always only found in API responses.
    """

    column_type: TableUnsupportedColumnType
    api_support: TableAPISupportDescriptor

    def __init__(
        self,
        *,
        column_type: TableUnsupportedColumnType | str,
        api_support: TableAPISupportDescriptor,
    ) -> None:
        super().__init__(
            column_type=TableUnsupportedColumnType.coerce(column_type),
            api_support=api_support,
        )

    def __repr__(self) -> str:
        return f"{self.__class__.__name__}({self.api_support.cql_definition})"

    def as_dict(self) -> dict[str, Any]:
        """Recast this object into a dictionary."""

        return {
            k: v
            for k, v in {
                "type": self.column_type.value,
                "apiSupport": self.api_support.as_dict(),
            }.items()
            if v is not None
        }

    @classmethod
    def _from_dict(
        cls, raw_dict: dict[str, Any]
    ) -> TableUnsupportedColumnTypeDescriptor:
        """
        Create an instance of TableUnsupportedColumnTypeDescriptor from a dictionary
        such as one from the Data API.
        """

        _warn_residual_keys(cls, raw_dict, {"type", "apiSupport"})
        return TableUnsupportedColumnTypeDescriptor(
            column_type=raw_dict["type"],
            api_support=TableAPISupportDescriptor._from_dict(raw_dict["apiSupport"]),
        )

Ancestors

• TableColumnTypeDescriptor
• abc.ABC

Class variables

var api_support : TableAPISupportDescriptor

var column_type : TableUnsupportedColumnType

Methods

def as_dict(self) ‑> dict[str, typing.Any]

Recast this object into a dictionary.

Expand source code

def as_dict(self) -> dict[str, Any]:
    """Recast this object into a dictionary."""

    return {
        k: v
        for k, v in {
            "type": self.column_type.value,
            "apiSupport": self.api_support.as_dict(),
        }.items()
        if v is not None
    }

Inherited members

• TableColumnTypeDescriptor:
  • as_spec
  • coerce

class TableValuedColumnTypeDescriptor (*, column_type: str | TableValuedColumnType, value_type: str | dict[Any, str] | ColumnType | TableColumnTypeDescriptor, api_support: TableAPISupportDescriptor | None = None)

Represents and describes a column in a Table, of a 'valued' type that stores multiple values. This means either a list or a set of homogeneous items.

See the docstring for class CreateTableDefinition for in-context usage examples.

Attributes

column_type

an instance of TableValuedColumnType. When creating the object, simple strings such as "list" or "set" are also accepted.

value_type

the type of the individual items stored in the column. This is a TableColumnTypeDescriptor, but when creating the object, equivalent dictionaries, as well as strings such as "TEXT" or "UUID" or ColumnType entries, are also accepted.

api_support

a TableAPISupportDescriptor object giving more details.

Expand source code

@dataclass
class TableValuedColumnTypeDescriptor(TableColumnTypeDescriptor):
    """
    Represents and describes a column in a Table, of a 'valued' type that stores
    multiple values. This means either a list or a set of homogeneous items.

    See the docstring for class `CreateTableDefinition` for in-context usage examples.

    Attributes:
        column_type: an instance of `TableValuedColumnType`. When creating the
            object, simple strings such as "list" or "set" are also accepted.
        value_type: the type of the individual items stored in the column.
            This is a `TableColumnTypeDescriptor`, but when creating the object,
            equivalent dictionaries, as well as strings such as "TEXT" or "UUID"
            or ColumnType entries, are also accepted.
        api_support: a `TableAPISupportDescriptor` object giving more details.
    """

    column_type: TableValuedColumnType
    value_type: TableColumnTypeDescriptor

    def __init__(
        self,
        *,
        column_type: str | TableValuedColumnType,
        value_type: str | dict[Any, str] | ColumnType | TableColumnTypeDescriptor,
        api_support: TableAPISupportDescriptor | None = None,
    ) -> None:
        self.value_type = TableColumnTypeDescriptor.coerce(value_type)
        super().__init__(
            column_type=TableValuedColumnType.coerce(column_type),
            api_support=api_support,
        )

    def __repr__(self) -> str:
        return f"{self.__class__.__name__}({self.column_type.value}<{self.value_type}>)"

    def as_dict(self) -> dict[str, Any]:
        """Recast this object into a dictionary."""

        return {
            k: v
            for k, v in {
                "type": self.column_type.value,
                "valueType": self.value_type.as_dict(),
                "apiSupport": self.api_support.as_dict() if self.api_support else None,
            }.items()
            if v is not None
        }

    @override
    def as_spec(self) -> dict[str, Any] | str:
        return {
            k: v
            for k, v in {
                "type": self.column_type.value,
                "valueType": self.value_type.as_spec(),
                "apiSupport": self.api_support.as_dict() if self.api_support else None,
            }.items()
            if v is not None
        }

    @classmethod
    def _from_dict(cls, raw_dict: dict[str, Any]) -> TableValuedColumnTypeDescriptor:
        """
        Create an instance of TableValuedColumnTypeDescriptor from a dictionary
        such as one from the Data API.
        """

        _warn_residual_keys(cls, raw_dict, {"type", "valueType", "apiSupport"})
        return TableValuedColumnTypeDescriptor(
            column_type=raw_dict["type"],
            value_type=raw_dict["valueType"],
            api_support=TableAPISupportDescriptor._from_dict(raw_dict["apiSupport"])
            if raw_dict.get("apiSupport")
            else None,
        )

Ancestors

• TableColumnTypeDescriptor
• abc.ABC

Class variables

var column_type : TableValuedColumnType

var value_type : TableColumnTypeDescriptor

Methods

def as_dict(self) ‑> dict[str, typing.Any]

Recast this object into a dictionary.

Expand source code

def as_dict(self) -> dict[str, Any]:
    """Recast this object into a dictionary."""

    return {
        k: v
        for k, v in {
            "type": self.column_type.value,
            "valueType": self.value_type.as_dict(),
            "apiSupport": self.api_support.as_dict() if self.api_support else None,
        }.items()
        if v is not None
    }

Inherited members

• TableColumnTypeDescriptor:
  • as_spec
  • coerce

class TableVectorColumnTypeDescriptor (*, column_type: str | TableVectorColumnType = TableVectorColumnType.VECTOR, dimension: int | None, service: VectorServiceOptions | None = None, api_support: TableAPISupportDescriptor | None = None)

Represents and describes a column in a Table, of vector type, i.e. which contains a list of dimension floats that is treated specially as a "vector".

See the docstring for class CreateTableDefinition for in-context usage examples.

Attributes

column_type

a TableVectorColumnType value. This can be omitted when creating the object. It only ever assumes the "VECTOR" value.

dimension

an integer, the number of components (numbers) in the vectors. This can be left unspecified in some cases of vectorize-enabled columns.

service

an optional VectorServiceOptions object defining the vectorize settings (i.e. server-side embedding computation) for the column.

api_support

a TableAPISupportDescriptor object giving more details.

Expand source code

@dataclass
class TableVectorColumnTypeDescriptor(TableColumnTypeDescriptor):
    """
    Represents and describes a column in a Table, of vector type, i.e. which contains
    a list of `dimension` floats that is treated specially as a "vector".

    See the docstring for class `CreateTableDefinition` for in-context usage examples.

    Attributes:
        column_type: a `TableVectorColumnType` value. This can be omitted when
            creating the object. It only ever assumes the "VECTOR" value.
        dimension: an integer, the number of components (numbers) in the vectors.
            This can be left unspecified in some cases of vectorize-enabled columns.
        service: an optional `VectorServiceOptions` object defining the vectorize
            settings (i.e. server-side embedding computation) for the column.
        api_support: a `TableAPISupportDescriptor` object giving more details.
    """

    column_type: TableVectorColumnType
    dimension: int | None
    service: VectorServiceOptions | None

    def __init__(
        self,
        *,
        column_type: str | TableVectorColumnType = TableVectorColumnType.VECTOR,
        dimension: int | None,
        service: VectorServiceOptions | None = None,
        api_support: TableAPISupportDescriptor | None = None,
    ) -> None:
        self.dimension = dimension
        self.service = service
        super().__init__(
            column_type=TableVectorColumnType.coerce(column_type),
            api_support=api_support,
        )

    def __repr__(self) -> str:
        not_null_pieces = [
            pc
            for pc in [
                f"dimension={self.dimension}" if self.dimension is not None else None,
                None if self.service is None else f"service={self.service}",
            ]
            if pc is not None
        ]
        inner_desc = ", ".join(not_null_pieces)

        return f"{self.__class__.__name__}({self.column_type.value}[{inner_desc}])"

    def as_dict(self) -> dict[str, Any]:
        """Recast this object into a dictionary."""

        return {
            k: v
            for k, v in {
                "type": self.column_type.value,
                "dimension": self.dimension,
                "service": None if self.service is None else self.service.as_dict(),
                "apiSupport": self.api_support.as_dict() if self.api_support else None,
            }.items()
            if v is not None
        }

    @classmethod
    def _from_dict(cls, raw_dict: dict[str, Any]) -> TableVectorColumnTypeDescriptor:
        """
        Create an instance of TableVectorColumnTypeDescriptor from a dictionary
        such as one from the Data API.
        """

        _warn_residual_keys(
            cls,
            raw_dict,
            {"type", "dimension", "service", "apiSupport"},
        )
        return TableVectorColumnTypeDescriptor(
            column_type=raw_dict["type"],
            dimension=raw_dict.get("dimension"),
            service=VectorServiceOptions.coerce(raw_dict.get("service")),
            api_support=TableAPISupportDescriptor._from_dict(raw_dict["apiSupport"])
            if raw_dict.get("apiSupport")
            else None,
        )

Ancestors

• TableColumnTypeDescriptor
• abc.ABC

Class variables

var column_type : TableVectorColumnType

var dimension : int | None

var service : VectorServiceOptions | None

Methods

def as_dict(self) ‑> dict[str, typing.Any]

Recast this object into a dictionary.

Expand source code

def as_dict(self) -> dict[str, Any]:
    """Recast this object into a dictionary."""

    return {
        k: v
        for k, v in {
            "type": self.column_type.value,
            "dimension": self.dimension,
            "service": None if self.service is None else self.service.as_dict(),
            "apiSupport": self.api_support.as_dict() if self.api_support else None,
        }.items()
        if v is not None
    }

Inherited members

• TableColumnTypeDescriptor:
  • as_spec
  • coerce