Find a row (Python)

Finds a single row in a table using filter and sort clauses, including vector search.

For general information about working with tables and rows, see About tables with the Data API (Python).

Ready to write code? See the examples for this method to get started. If you are new to the Data API, check out the quickstart.

Result

Returns a dictionary representation of a row that matches the specified filter and sort clauses, or returns None if no row was found.

The columns included in the returned row depend on the subset of columns that were requested in the projection. If requested and applicable, the row will also include a $similarity key with a numeric similarity score that represents the closeness of the sort vector and the row’s vector.

Parameters

Use the find_one method, which belongs to the astrapy.Table class.

Method signature 
find_one(
  filter: Dict[str, Any],
  *,
  projection: Iterable[str] | Dict[str, bool],
  include_similarity: bool,
  sort: Dict[str, Any],
  general_method_timeout_ms: int,
  request_timeout_ms: int,
  timeout_ms: int,
) -> Dict[str, Any] | None

For best performance, filter and sort on indexed columns, partition keys, and clustering keys.

Filtering on non-indexed columns is inefficient and resource-intensive, especially for large datasets. With the Data API clients, such operations can hit the client timeout limit before the underlying HTTP operation is complete. If you filter on non-indexed columns, the Data API will give a warning.

An empty filter or omitted filter may also result in an inefficient and long-running operation.

Additionally, the Data API can perform in-memory sorting, depending on the columns you sort on, the table’s partitioning structure, and whether the sorted columns are indexed. In-memory sorts can have performance implications.
Name Type Summary

filter

Dict[str, Any]

Optional. An object that defines filter criteria using the Data API filter syntax. The method only finds rows that match the filter criteria. Filters can improve performance by reducing the number of rows that the Data API processes.

For a list of available filter operators and more examples, see Filter operators for tables (Python).

To perform a vector search, use sort instead of filter.

To avoid fetching unnecessary rows, which can contain tombstones, DataStax recommends that you use a filter that limits the number of rows scanned. For example, filter on partition key columns or indexed columns.

Default: No filter

For an example, see Use filters to find a row.

sort

Dict[str, Any]

Optional. Sorts rows by one or more columns, or performs a vector search.

For more information, see Sort clauses for tables (Python).

For examples, see Use sorting to find a row and Use vector search with a search vector to find a row.

projection

Dict[str, bool]

Optional. Controls which columns are included or excluded in the returned rows.

For more information, see Projections for tables (Python).

DataStax recommends a projection to avoid unnecessarily returning large columns, such as vector columns with highly dimensional embeddings.

Default: All columns

For examples, see Include only specific columns in the response and Exclude specific columns from the response.

include_similarity

bool

Optional. Whether to include a $similarity property in the response. The $similarity value represents the closeness of the sort vector and the row’s vector.

Default: false

For an example, see Include the similarity score with the result.

general_method_timeout_ms

int

Optional. The maximum time, in milliseconds, that the client should wait for the underlying HTTP request.

This parameter is aliased as request_timeout_ms and timeout_ms for convenience.

Default: The default value for the table. This default is 30 seconds unless you specified a different default when you initialized the Table or DataAPIClient object. For more information, see Timeout options.

Examples

The following examples demonstrate how to find a row in a table.

Use filters to find a row

You can use a filter to find a row that matches specific criteria. For example, you can find a row with an is_checked_out value of false and a number_of_pages value less than 300.

For optimal performance, only filter on indexed columns. The Data API returns a warning if you filter on a non-indexed column.

For a list of available filter operators, see Filter operators for tables (Python).

The following example uses untyped documents or rows, but you can define a client-side type for your collection to help statically catch errors. For examples, see Typing support.

from astrapy import DataAPIClient
from astrapy.authentication import UsernamePasswordTokenProvider
from astrapy.constants import Environment

# Get an existing table
client = DataAPIClient(environment=Environment.HCD)
database = client.get_database(
    "API_ENDPOINT",
    token=UsernamePasswordTokenProvider("USERNAME", "PASSWORD"),
    keyspace="KEYSPACE_NAME",
)
table = database.get_table("TABLE_NAME")

# Find a row
result = table.find_one(
    {
        "$and": [
            {"is_checked_out": False},
            {"number_of_pages": {"$lt": 300}},
        ]
    }
)

print(result)

Use vector search with a search vector to find a row

Perform a vector search by providing a search vector in the sort clause. This returns the row whose vector column value is most similar to the provided search vector.

The vector column must be indexed.

If your table has multiple vector columns, you can only sort on one vector column at a time.

You can use the astrapy.data_types.DataAPIVector class to binary-encode your search vector. DataStax recommends that you always use a DataAPIVector object instead of a list of floats to improve performance.

When you read the value of a vector column, the client always returns a DataAPIVector object, unless you change the default serialization/deserialization behavior.

from astrapy import DataAPIClient
from astrapy.authentication import UsernamePasswordTokenProvider
from astrapy.constants import Environment
from astrapy.data_types import DataAPIVector

# Get an existing table
client = DataAPIClient(environment=Environment.HCD)
database = client.get_database(
    "API_ENDPOINT",
    token=UsernamePasswordTokenProvider("USERNAME", "PASSWORD"),
    keyspace="KEYSPACE_NAME",
)
table = database.get_table("TABLE_NAME")

# Find a row
result = table.find_one(
    {}, sort={"summary_genres_vector": DataAPIVector([0.08, -0.62, 0.39])}
)

print(result)

Use lexicographical matching to find a row

Lexicographical matching is currently in public preview. Development is ongoing, and the features and functionality are subject to change. Hyper-Converged Database (HCD), and the use of such, is subject to the DataStax Preview Terms.

There are two ways to use lexicographical matching to find rows with the Data API:

  • Sort to find rows with a text or ascii column value that is most relevant to a given string of space-separated keywords or terms.

  • Filter with the $match operator to find rows with a text or ascii column value that is a lexicographical match to the specified string of space-separated keywords or terms

You can use these strategies together or separately.

Lexicographical matching is only available for text or ascii columns that have a text index, not a regular index. For more information, see Create a text index (Python) and Indexes in tables (Python).

The following example uses untyped documents or rows, but you can define a client-side type for your collection to help statically catch errors. For examples, see Typing support.

from astrapy import DataAPIClient
from astrapy.authentication import UsernamePasswordTokenProvider
from astrapy.constants import Environment

# Get an existing table
client = DataAPIClient(environment=Environment.HCD)
database = client.get_database(
    "API_ENDPOINT",
    token=UsernamePasswordTokenProvider("USERNAME", "PASSWORD"),
    keyspace="KEYSPACE_NAME",
)
table = database.get_table("TABLE_NAME")

# Find a row
result = table.find_one(
    {"summary": {"$match": "futuristic laboratory discovery"}},
    sort={"summary": "futuristic laboratory"},
)

print(result)

Use sorting to find a row

You can use a sort clause to sort rows by one or more columns.

For best performance, only sort on columns that are indexed or that are part of the primary key.

For more information, see Sort clauses for tables (Python).

The following example uses untyped documents or rows, but you can define a client-side type for your collection to help statically catch errors. For examples, see Typing support.

from astrapy import DataAPIClient
from astrapy.authentication import UsernamePasswordTokenProvider
from astrapy.constants import Environment, SortMode

# Get an existing table
client = DataAPIClient(environment=Environment.HCD)
database = client.get_database(
    "API_ENDPOINT",
    token=UsernamePasswordTokenProvider("USERNAME", "PASSWORD"),
    keyspace="KEYSPACE_NAME",
)
table = database.get_table("TABLE_NAME")

# Find a row
result = table.find_one(
    {"is_checked_out": False},
    sort={
        "rating": SortMode.ASCENDING,
        "title": SortMode.DESCENDING,
    },
)

print(result)

Include the similarity score with the result

If you use a vector search to find a row, you can also include a $similarity property in the result. The $similarity value represents the closeness of the sort vector and the value of the row’s vector column.

This parameter doesn’t work with vectorize; it only works if you provide the search vector for vector search directly.

The client always returns the similarity score as a DataAPIVector object, unless you change the default serialization/deserialization behavior.

from astrapy import DataAPIClient
from astrapy.authentication import UsernamePasswordTokenProvider
from astrapy.constants import Environment
from astrapy.data_types import DataAPIVector

# Get an existing table
client = DataAPIClient(environment=Environment.HCD)
database = client.get_database(
    "API_ENDPOINT",
    token=UsernamePasswordTokenProvider("USERNAME", "PASSWORD"),
    keyspace="KEYSPACE_NAME",
)
table = database.get_table("TABLE_NAME")

# Find a row
result = table.find_one(
    {},
    sort={"summary_genres_vector": DataAPIVector([0.08, -0.62, 0.39])},
    include_similarity=True,
)

if result:
    print(result["$similarity"])

Include only specific columns in the response

To specify which columns to include or exclude in the returned row, use a projection.

The following example demonstrates an inclusive projection.

The following example uses untyped documents or rows, but you can define a client-side type for your collection to help statically catch errors. For examples, see Typing support.

from astrapy import DataAPIClient
from astrapy.authentication import UsernamePasswordTokenProvider
from astrapy.constants import Environment

# Get an existing table
client = DataAPIClient(environment=Environment.HCD)
database = client.get_database(
    "API_ENDPOINT",
    token=UsernamePasswordTokenProvider("USERNAME", "PASSWORD"),
    keyspace="KEYSPACE_NAME",
)
table = database.get_table("TABLE_NAME")

# Find a row
result = table.find_one(
    {"number_of_pages": {"$lt": 300}},
    projection={"is_checked_out": True, "title": True},
)

print(result)

Exclude specific columns from the response

To specify which columns to include or exclude in the returned row, use a projection.

The following example demonstrates an exclusive projection.

The following example uses untyped documents or rows, but you can define a client-side type for your collection to help statically catch errors. For examples, see Typing support.

from astrapy import DataAPIClient
from astrapy.authentication import UsernamePasswordTokenProvider
from astrapy.constants import Environment

# Get an existing table
client = DataAPIClient(environment=Environment.HCD)
database = client.get_database(
    "API_ENDPOINT",
    token=UsernamePasswordTokenProvider("USERNAME", "PASSWORD"),
    keyspace="KEYSPACE_NAME",
)
table = database.get_table("TABLE_NAME")

# Find a row
result = table.find_one(
    {"number_of_pages": {"$lt": 300}},
    projection={"is_checked_out": False, "title": False},
)

print(result)

Use filter, sort, and projection together

The following example uses untyped documents or rows, but you can define a client-side type for your collection to help statically catch errors. For examples, see Typing support.

from astrapy import DataAPIClient
from astrapy.authentication import UsernamePasswordTokenProvider
from astrapy.constants import Environment, SortMode

# Get an existing table
client = DataAPIClient(environment=Environment.HCD)
database = client.get_database(
    "API_ENDPOINT",
    token=UsernamePasswordTokenProvider("USERNAME", "PASSWORD"),
    keyspace="KEYSPACE_NAME",
)
table = database.get_table("TABLE_NAME")

# Find a row
result = table.find_one(
    {
        "$and": [
            {"is_checked_out": False},
            {"number_of_pages": {"$lt": 300}},
        ]
    },
    sort={
        "rating": SortMode.ASCENDING,
        "title": SortMode.DESCENDING,
    },
    projection={"is_checked_out": True, "title": True},
)

print(result)

Client reference

For more information, see the client reference.

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