Polars Integration¶

This section covers Polars-specific cursors, result sets, and data converters.

Polars Cursors¶

class pyathena.polars.cursor.PolarsCursor(s3_staging_dir: str | None = None, schema_name: str | None = None, catalog_name: str | None = None, work_group: str | None = None, poll_interval: float = 1, encryption_option: str | None = None, kms_key: str | None = None, kill_on_interrupt: bool = True, unload: bool = False, result_reuse_enable: bool = False, result_reuse_minutes: int = 60, block_size: int | None = None, cache_type: str | None = None, max_workers: int = 20, chunksize: int | None = None, **kwargs)[source]¶

Cursor for handling Polars DataFrame results from Athena queries.

This cursor returns query results as Polars DataFrames using Polars’ native reading capabilities. It does not require PyArrow for basic functionality, but can optionally provide Arrow Table access when PyArrow is installed.

The cursor supports both regular CSV-based results and high-performance UNLOAD operations that return results in Parquet format for improved performance with large datasets.

description¶: Sequence of column descriptions for the last query.

rowcount¶: Number of rows affected by the last query (-1 for SELECT queries).

arraysize¶: Default number of rows to fetch with fetchmany().

Example

>>> from pyathena.polars.cursor import PolarsCursor
>>> cursor = connection.cursor(PolarsCursor)
>>> cursor.execute("SELECT * FROM large_table")
>>> df = cursor.as_polars()  # Returns polars.DataFrame

# Optional: Get Arrow Table (requires pyarrow) >>> table = cursor.as_arrow()

# High-performance UNLOAD for large datasets >>> cursor = connection.cursor(PolarsCursor, unload=True) >>> cursor.execute(“SELECT * FROM huge_table”) >>> df = cursor.as_polars() # Faster Parquet-based result

Note

Requires polars to be installed. PyArrow is optional and only needed for as_arrow() functionality.

__init__(s3_staging_dir: str | None = None, schema_name: str | None = None, catalog_name: str | None = None, work_group: str | None = None, poll_interval: float = 1, encryption_option: str | None = None, kms_key: str | None = None, kill_on_interrupt: bool = True, unload: bool = False, result_reuse_enable: bool = False, result_reuse_minutes: int = 60, block_size: int | None = None, cache_type: str | None = None, max_workers: int = 20, chunksize: int | None = None, **kwargs) → None[source]¶

Initialize a PolarsCursor.

Parameters:

s3_staging_dir – S3 location for query results.
schema_name – Default schema name.
catalog_name – Default catalog name.
work_group – Athena workgroup name.
poll_interval – Query status polling interval in seconds.
encryption_option – S3 encryption option (SSE_S3, SSE_KMS, CSE_KMS).
kms_key – KMS key ARN for encryption.
kill_on_interrupt – Cancel running query on keyboard interrupt.
unload – Enable UNLOAD for high-performance Parquet output.
result_reuse_enable – Enable Athena query result reuse.
result_reuse_minutes – Minutes to reuse cached results.
block_size – S3 read block size.
cache_type – S3 caching strategy.
max_workers – Maximum worker threads for parallel S3 operations.
chunksize – Number of rows per chunk for memory-efficient processing. If specified, data is loaded lazily in chunks for all data access methods including fetchone(), fetchmany(), and iter_chunks().
**kwargs – Additional connection parameters.

Example

>>> cursor = connection.cursor(PolarsCursor, unload=True)
>>> # With chunked processing
>>> cursor = connection.cursor(PolarsCursor, chunksize=50000)

static get_default_converter(unload: bool = False) → DefaultPolarsTypeConverter | DefaultPolarsUnloadTypeConverter | Any[source]¶

Get the default type converter for Polars results.

Parameters:: unload – If True, returns converter for UNLOAD (Parquet) results.
Returns:: Type converter appropriate for the result format.

Execute a SQL query and return results as Polars DataFrames.

Executes the SQL query on Amazon Athena and configures the result set for Polars DataFrame output using Polars’ native reading capabilities.

Parameters:

operation – SQL query string to execute.
parameters – Query parameters for parameterized queries.
work_group – Athena workgroup to use for this query.
s3_staging_dir – S3 location for query results.
cache_size – Number of queries to check for result caching.
cache_expiration_time – Cache expiration time in seconds.
result_reuse_enable – Enable Athena result reuse for this query.
result_reuse_minutes – Minutes to reuse cached results.
paramstyle – Parameter style (‘qmark’ or ‘pyformat’).
on_start_query_execution – Callback invoked with the query ID before execute() waits for the query: after the StartQueryExecution call, or after a reusable query ID is found through cache_size.
result_set_type_hints – Athena type signatures for complex-type columns, keyed by column name (case-insensitive) or zero-based column index.
options – Shared execution options as an ExecuteOptions instance. Individual keyword arguments take precedence over options fields.
**kwargs – Additional execution parameters passed to Polars read functions.

Returns:

Self reference for method chaining.

Example

>>> cursor.execute("SELECT * FROM sales WHERE year = 2023")
>>> df = cursor.as_polars()  # Returns Polars DataFrame

as_polars() → pl.DataFrame[source]¶

Return query results as a Polars DataFrame.

Returns the query results as a Polars DataFrame. This is the primary method for accessing results with PolarsCursor.

Returns:: Polars DataFrame containing all query results.
Raises:: ProgrammingError – If no query has been executed or no results are available.

Example

>>> cursor = connection.cursor(PolarsCursor)
>>> cursor.execute("SELECT * FROM my_table")
>>> df = cursor.as_polars()
>>> print(f"DataFrame has {df.height} rows and {df.width} columns")
>>> filtered = df.filter(pl.col("value") > 100)

as_arrow() → Table[source]¶

Return query results as an Apache Arrow Table.

Converts the Polars DataFrame to an Apache Arrow Table for interoperability with other Arrow-compatible tools and libraries.

Returns:

Apache Arrow Table containing all query results.

Raises:

ProgrammingError – If no query has been executed or no results are available.
ImportError – If pyarrow is not installed.

Example

>>> cursor = connection.cursor(PolarsCursor)
>>> cursor.execute("SELECT * FROM my_table")
>>> table = cursor.as_arrow()
>>> print(f"Table has {table.num_rows} rows and {table.num_columns} columns")

iter_chunks() → Iterator[pl.DataFrame][source]¶

Iterate over result chunks as Polars DataFrames.

This method provides an iterator interface for processing result sets. When chunksize is specified, it yields DataFrames in chunks using lazy evaluation for memory-efficient processing. When chunksize is not specified, it yields the entire result as a single DataFrame, providing a consistent interface regardless of chunking configuration.

Yields:: Polars DataFrame for each chunk of rows, or the entire DataFrame if chunksize was not specified.
Raises:: ProgrammingError – If no result set is available.

Example

>>> # With chunking for large datasets
>>> cursor = connection.cursor(PolarsCursor, chunksize=50000)
>>> cursor.execute("SELECT * FROM large_table")
>>> for chunk in cursor.iter_chunks():
...     process_chunk(chunk)  # Each chunk is a Polars DataFrame
>>>
>>> # Without chunking - yields entire result as single chunk
>>> cursor = connection.cursor(PolarsCursor)
>>> cursor.execute("SELECT * FROM small_table")
>>> for df in cursor.iter_chunks():
...     process(df)  # Single DataFrame with all data

DEFAULT_FETCH_SIZE: int = 1000¶

DEFAULT_RESULT_REUSE_MINUTES = 60¶

LIST_DATABASES_MAX_RESULTS = 50¶

LIST_QUERY_EXECUTIONS_MAX_RESULTS = 50¶

LIST_TABLE_METADATA_MAX_RESULTS = 50¶

property arraysize: int¶

The default number of rows per fetchmany() call.

execute() passes it to the new result set, so a change applies to the result sets of later executions. Setting it to zero or a negative value raises ProgrammingError.

Returns:: The default number of rows per fetchmany() call.

cancel() → None¶

Cancel the currently executing query.

Raises:: ProgrammingError – If no query is currently executing.

property catalog: str | None¶: The data catalog in the QueryExecutionContext of the query.

close() → None¶: Close the cursor and release associated resources.

property completion_date_time: datetime | None¶: The date and time when the query completed.

property connection: Connection[Any]¶: The connection that created this cursor.

property data_manifest_location: str | None¶: The S3 location of the data manifest that lists the files the query wrote.

property data_scanned_in_bytes: int | None¶: The number of bytes that the query scanned.

property database: str | None¶: The database in the QueryExecutionContext of the query.

property description: list[tuple[str, str, None, None, int, int, str]] | None¶: The DB API 2.0 column descriptions of the result set, or None without one.

property effective_engine_version: str | None¶: The Athena engine version that ran the query.

property encryption_option: str | None¶: The EncryptionOption of the query results, such as SSE_S3 or SSE_KMS.

property engine_execution_time_in_millis: int | None¶: The time in milliseconds that the query engine took to run the query.

property error_category: int | None¶

1 for system, 2 for user, 3 for other.

Type:: The ErrorCategory of the failure

property error_message: str | None¶: The ErrorMessage that describes the query failure.

property error_type: int | None¶: The ErrorType code of the query failure.

executemany(operation: str, seq_of_parameters: list[dict[str, Any] | list[str] | None], **kwargs) → None¶

Execute a SQL query multiple times with different parameters.

On success, rowcount is the sum of the affected row counts, or -1 if any execution has an unknown count. An empty parameter list sets it to 0. On failure, it is -1; earlier executions are not rolled back. Result sets are discarded.

On failure, query_id retains the current query ID when available. If parameter iteration fails, this can identify the last successful execution.

Parameters:

operation – SQL query string to execute.
seq_of_parameters – Sequence of parameter sets, one per execution.
**kwargs – Additional keyword arguments passed to each execute().

property execution_parameters: list[str]¶: The ExecutionParameters values of the query.

property expected_bucket_owner: str | None¶: The AWS account ID expected to own the S3 bucket of the query results.

fetchall() → list[tuple[Any | None, ...] | dict[Any, Any | None]]¶

Fetch all remaining rows from the result set.

Returns:: The remaining rows.
Raises:: ProgrammingError – If no result set is available.

fetchmany(size: int | None = None) → list[tuple[Any | None, ...] | dict[Any, Any | None]]¶

Fetch multiple rows from the result set.

Parameters:: size – Maximum number of rows to fetch. If None or not positive, arraysize is used.
Returns:: The fetched rows.
Raises:: ProgrammingError – If no result set is available.

fetchone() → tuple[Any | None, ...] | dict[Any, Any | None] | None¶

Fetch the next row of the result set.

Returns:: The next row (a tuple, or a dict for dict cursors), or None if no more rows.
Raises:: ProgrammingError – If no result set is available.

get_table_metadata(table_name: str, catalog_name: str | None = None, schema_name: str | None = None, logging_: bool = True) → AthenaTableMetadata¶

Get one table’s metadata.

In AwsDataCatalog and S3 Tables catalogs, a throttled request is answered from the AWS Glue Data Catalog; see glue_metadata_fallback.

Parameters:

table_name – The table name.
catalog_name – The catalog, or None for the cursor’s catalog.
schema_name – The database, or None for the cursor’s schema.
logging – Whether to log a failed request.

Returns:

The table’s metadata.

Raises:

OperationalError – If the request fails, including when the table does not exist.

property has_result_set: bool¶: Whether the cursor has a result set.

property kms_key: str | None¶: The KMS key used to encrypt the query results.

list_databases(catalog_name: str | None, max_results: int | None = None) → list[AthenaDatabase]¶

List the catalog’s databases.

In AwsDataCatalog and S3 Tables catalogs, a throttled request is answered from the AWS Glue Data Catalog; see glue_metadata_fallback.

Parameters:

catalog_name – The catalog, or None for the cursor’s catalog.
max_results – The page size of each request.

Returns:

The catalog’s databases.

Raises:

OperationalError – If the request fails.

list_table_metadata(catalog_name: str | None = None, schema_name: str | None = None, expression: str | None = None, max_results: int | None = None, logging_: bool = True) → list[AthenaTableMetadata]¶

List a database’s table metadata.

In AwsDataCatalog and S3 Tables catalogs, a throttled request is answered from the AWS Glue Data Catalog; see glue_metadata_fallback.

Parameters:

catalog_name – The catalog, or None for the cursor’s catalog.
schema_name – The database, or None for the cursor’s schema.
expression – A table name pattern.
max_results – The page size of each request.
logging – Whether to log a failed request.

Returns:

The metadata of the database’s tables.

Raises:

OperationalError – If the request fails.

property output_location: str | None¶: The S3 location of the query results.

property query: str | None¶: The SQL statement that the query execution ran.

property query_id: str | None¶

The query execution ID of the last execution.

With cache_size or cache_expiration_time, this can be the ID of a previous execution whose result is reused.

Returns:: The query execution ID, or None if there is none since the last reset.

property query_planning_time_in_millis: int | None¶: The time in milliseconds that Athena took to plan the query.

property query_queue_time_in_millis: int | None¶: The time in milliseconds that the query waited in the queue.

property result_reuse_enabled: bool | None¶: Whether reuse of previous query results by age is enabled for the query.

property result_reuse_minutes: int | None¶: The maximum age in minutes of a previous query result that Athena can reuse.

property result_set: AthenaResultSet | None¶

The result set of the last executed query.

Returns:: The result set, or None before a query succeeds or after a reset.

property retryable: bool | None¶: Whether Athena reports the query failure as retryable.

property reused_previous_result: bool | None¶: Whether Athena reused a previous query result instead of running the query.

property rowcount: int¶

Get the number of rows affected by the last operation.

For SELECT statements, this returns -1 as per DB API 2.0 specification. For DML operations (INSERT, UPDATE, DELETE) and CTAS, this returns the number of affected rows. After a successful executemany(), this is the sum across executions, or -1 if any count is unknown.

Returns:: The number of rows, or -1 if not applicable or unknown.

property rownumber: int | None¶

The zero-based index of the next row in the result set.

Returns:: The row index, or None if there is no result set or the index is unknown.

property s3_acl_option: str | None¶: The S3AclOption of the query results, such as BUCKET_OWNER_FULL_CONTROL.

property selected_engine_version: str | None¶: The Athena engine version selected to run the query.

property service_processing_time_in_millis: int | None¶: The time in milliseconds that Athena took to publish the query results.

setinputsizes(sizes)¶

Accept input sizes as DB API 2.0 requires, and ignore them.

Parameters:: sizes – Sequence of parameter types or sizes.

setoutputsize(size, column=None)¶

Accept a column buffer size as DB API 2.0 requires, and ignore it.

Parameters:

size – Buffer size for large columns.
column – Index of the column the size applies to, or None for all large columns.

property state: str | None¶: The state of the query execution, such as RUNNING or SUCCEEDED.

property state_change_reason: str | None¶: The StateChangeReason that gives further detail about the state.

property statement_type: str | None¶: The StatementType of the query, such as DDL, DML, or UTILITY.

property submission_date_time: datetime | None¶: The date and time when the query was submitted.

property substatement_type: str | None¶: The SubstatementType of the query, such as INSERT or MERGE.

property total_execution_time_in_millis: int | None¶: The total time in milliseconds that Athena took to run the query.

property work_group: str | None¶: The work group in which the query ran.

class pyathena.polars.async_cursor.AsyncPolarsCursor(s3_staging_dir: str | None = None, schema_name: str | None = None, catalog_name: str | None = None, work_group: str | None = None, poll_interval: float = 1, encryption_option: str | None = None, kms_key: str | None = None, kill_on_interrupt: bool = True, max_workers: int = 20, arraysize: int = 1000, unload: bool = False, result_reuse_enable: bool = False, result_reuse_minutes: int = 60, block_size: int | None = None, cache_type: str | None = None, chunksize: int | None = None, **kwargs)[source]¶

Asynchronous cursor that returns results as Polars DataFrames.

This cursor extends AsyncCursor to provide asynchronous query execution with results returned as Polars DataFrames using Polars’ native reading capabilities. It does not require PyArrow for basic functionality, but can optionally provide Arrow Table access when PyArrow is installed.

Features:

Asynchronous query execution with concurrent futures
Native Polars CSV and Parquet reading (no PyArrow required)
Memory-efficient columnar data processing
Support for UNLOAD operations with Parquet output
Optional Arrow interoperability when PyArrow is installed

arraysize¶: Number of rows to fetch per batch (configurable).

Example

>>> from pyathena.polars.async_cursor import AsyncPolarsCursor
>>>
>>> cursor = connection.cursor(AsyncPolarsCursor, unload=True)
>>> query_id, future = cursor.execute("SELECT * FROM large_table")
>>>
>>> # Get result when ready
>>> result_set = future.result()
>>> df = result_set.as_polars()
>>>
>>> # Optional: Convert to Arrow Table if pyarrow is installed
>>> table = result_set.as_arrow()

Note

Requires polars to be installed. PyArrow is optional and only needed for as_arrow() functionality. UNLOAD operations generate Parquet files in S3 for optimal performance.

__init__(s3_staging_dir: str | None = None, schema_name: str | None = None, catalog_name: str | None = None, work_group: str | None = None, poll_interval: float = 1, encryption_option: str | None = None, kms_key: str | None = None, kill_on_interrupt: bool = True, max_workers: int = 20, arraysize: int = 1000, unload: bool = False, result_reuse_enable: bool = False, result_reuse_minutes: int = 60, block_size: int | None = None, cache_type: str | None = None, chunksize: int | None = None, **kwargs) → None[source]¶

Initialize an AsyncPolarsCursor.

Parameters:

s3_staging_dir – S3 location for query results.
schema_name – Default schema name.
catalog_name – Default catalog name.
work_group – Athena workgroup name.
poll_interval – Query status polling interval in seconds.
encryption_option – S3 encryption option (SSE_S3, SSE_KMS, CSE_KMS).
kms_key – KMS key ARN for encryption.
kill_on_interrupt – Cancel running query on keyboard interrupt.
max_workers – Maximum number of workers for concurrent execution.
arraysize – Number of rows to fetch per batch.
unload – Enable UNLOAD for high-performance Parquet output.
result_reuse_enable – Enable Athena query result reuse.
result_reuse_minutes – Minutes to reuse cached results.
block_size – S3 read block size.
cache_type – S3 caching strategy.
chunksize – Number of rows per chunk for memory-efficient processing. If specified, data is loaded lazily in chunks for all data access methods including fetchone(), fetchmany(), and iter_chunks().
**kwargs – Additional connection parameters.

Example

>>> cursor = connection.cursor(AsyncPolarsCursor, unload=True)
>>> # With chunked processing
>>> cursor = connection.cursor(AsyncPolarsCursor, chunksize=50000)

static get_default_converter(unload: bool = False) → DefaultPolarsTypeConverter | DefaultPolarsUnloadTypeConverter | Any[source]¶

Get the default type converter for Polars results.

Parameters:: unload – If True, returns converter for UNLOAD (Parquet) results.
Returns:: Type converter appropriate for the result format.

property arraysize: int¶: Get the number of rows to fetch per batch.

LIST_DATABASES_MAX_RESULTS = 50¶

LIST_QUERY_EXECUTIONS_MAX_RESULTS = 50¶

LIST_TABLE_METADATA_MAX_RESULTS = 50¶

cancel(query_id: str) → Future[None]¶

Cancel a running query asynchronously.

Submits a cancellation request for the specified query. The cancellation itself runs asynchronously in the background.

Parameters:: query_id – The Athena query execution ID to cancel.
Returns:: Future object that completes when the cancellation request finishes.

Example

>>> query_id, future = cursor.execute("SELECT * FROM huge_table")
>>> # Later, cancel the query
>>> cancel_future = cursor.cancel(query_id)
>>> cancel_future.result()  # Wait for cancellation to complete

close(wait: bool = False) → None¶: Close the cursor.

property connection: Connection[Any]¶: The connection that created this cursor.

description(query_id: str) → Future[list[tuple[str, str, None, None, int, int, str]] | None]¶

Get the column descriptions of a query’s result set asynchronously.

The future waits for the query to finish before it reads the result set.

Parameters:: query_id – The Athena query execution ID.
Returns:: Future object containing the DB API 2.0 column descriptions, or None.

Execute a SQL query asynchronously and return results as Polars DataFrames.

Executes the SQL query on Amazon Athena asynchronously and returns the query ID with a future that resolves to a result set for Polars DataFrame output.

Parameters:

operation – SQL query string to execute.
parameters – Query parameters for parameterized queries.
work_group – Athena workgroup to use for this query.
s3_staging_dir – S3 location for query results.
cache_size – Number of queries to check for result caching.
cache_expiration_time – Cache expiration time in seconds.
result_reuse_enable – Enable Athena result reuse for this query.
result_reuse_minutes – Minutes to reuse cached results.
paramstyle – Parameter style (‘qmark’ or ‘pyformat’).
result_set_type_hints – Athena type signatures for complex-type columns, keyed by column name (case-insensitive) or zero-based column index.
options – Shared execution options as an ExecuteOptions instance. Individual keyword arguments take precedence over options fields.
**kwargs – Additional execution parameters passed to Polars read functions.

Returns:

Tuple of (query_id, future) where future resolves to AthenaPolarsResultSet.

Example

>>> query_id, future = cursor.execute("SELECT * FROM sales")
>>> result_set = future.result()
>>> df = result_set.as_polars()  # Returns Polars DataFrame

executemany(operation: str, seq_of_parameters: list[dict[str, Any] | list[str] | None], **kwargs) → None¶

Execute multiple queries asynchronously (not supported).

This method is not supported for asynchronous cursors because managing multiple concurrent queries would be complex and resource-intensive.

Parameters:

operation – SQL query string.
seq_of_parameters – Sequence of parameter sets.
**kwargs – Additional arguments.

Raises:

NotSupportedError – Always raised as this operation is not supported.

Note

For bulk operations, consider using execute() with parameterized queries or batch processing patterns instead.

get_table_metadata(table_name: str, catalog_name: str | None = None, schema_name: str | None = None, logging_: bool = True) → AthenaTableMetadata¶

Get one table’s metadata.

In AwsDataCatalog and S3 Tables catalogs, a throttled request is answered from the AWS Glue Data Catalog; see glue_metadata_fallback.

Parameters:

table_name – The table name.
catalog_name – The catalog, or None for the cursor’s catalog.
schema_name – The database, or None for the cursor’s schema.
logging – Whether to log a failed request.

Returns:

The table’s metadata.

Raises:

OperationalError – If the request fails, including when the table does not exist.

list_databases(catalog_name: str | None, max_results: int | None = None) → list[AthenaDatabase]¶

List the catalog’s databases.

In AwsDataCatalog and S3 Tables catalogs, a throttled request is answered from the AWS Glue Data Catalog; see glue_metadata_fallback.

Parameters:

catalog_name – The catalog, or None for the cursor’s catalog.
max_results – The page size of each request.

Returns:

The catalog’s databases.

Raises:

OperationalError – If the request fails.

List a database’s table metadata.

In AwsDataCatalog and S3 Tables catalogs, a throttled request is answered from the AWS Glue Data Catalog; see glue_metadata_fallback.

Parameters:

catalog_name – The catalog, or None for the cursor’s catalog.
schema_name – The database, or None for the cursor’s schema.
expression – A table name pattern.
max_results – The page size of each request.
logging – Whether to log a failed request.

Returns:

The metadata of the database’s tables.

Raises:

OperationalError – If the request fails.

poll(query_id: str) → Future[AthenaQueryExecution]¶

Poll for query completion asynchronously.

Waits for the query to complete (succeed, fail, or be cancelled) and returns the final execution status. This method blocks until completion but runs the polling in a background thread.

Parameters:: query_id – The Athena query execution ID to poll.
Returns:: Future object containing the final AthenaQueryExecution status.

Note

This method performs polling internally, so it will take time proportional to your query execution duration.

query_execution(query_id: str) → Future[AthenaQueryExecution]¶

Get query execution details asynchronously.

Retrieves the current execution status and metadata for a query. This is useful for monitoring query progress without blocking.

Parameters:: query_id – The Athena query execution ID.
Returns:: Future object containing AthenaQueryExecution with query details.

setinputsizes(sizes)¶

Accept input sizes as DB API 2.0 requires, and ignore them.

Parameters:: sizes – Sequence of parameter types or sizes.

setoutputsize(size, column=None)¶

Accept a column buffer size as DB API 2.0 requires, and ignore it.

Parameters:

size – Buffer size for large columns.
column – Index of the column the size applies to, or None for all large columns.

Polars Result Set¶

class pyathena.polars.result_set.AthenaPolarsResultSet(connection: Connection[Any], converter: Converter, query_execution: AthenaQueryExecution, arraysize: int, retry_config: RetryConfig, unload: bool = False, unload_location: str | None = None, block_size: int | None = None, cache_type: str | None = None, max_workers: int = 20, chunksize: int | None = None, result_set_type_hints: dict[str | int, str] | None = None, **kwargs)[source]¶

Result set that provides Polars DataFrame results with optional Arrow interoperability.

This result set handles CSV and Parquet result files from S3, converting them to Polars DataFrames using Polars’ native reading capabilities. It does not require PyArrow for basic functionality, but can optionally provide Arrow Table access when PyArrow is installed.

Features:

Native Polars CSV and Parquet reading (no PyArrow required)
Efficient columnar data processing with Polars
Optional Arrow interoperability when PyArrow is available
Support for both CSV and Parquet result formats
Chunked iteration for memory-efficient processing of large datasets
Optimized memory usage through columnar format

Example

>>> # Used automatically by PolarsCursor
>>> cursor = connection.cursor(PolarsCursor)
>>> cursor.execute("SELECT * FROM large_table")
>>>
>>> # Get Polars DataFrame
>>> df = cursor.as_polars()
>>>
>>> # Work with Polars
>>> print(f"DataFrame has {df.height} rows and {df.width} columns")
>>> filtered = df.filter(pl.col("value") > 100)
>>>
>>> # Optional: Get Arrow Table (requires pyarrow)
>>> table = cursor.as_arrow()
>>>
>>> # Memory-efficient chunked iteration
>>> cursor = connection.cursor(PolarsCursor, chunksize=50000)
>>> cursor.execute("SELECT * FROM huge_table")
>>> for chunk in cursor.iter_chunks():
...     process_chunk(chunk)

Note

This class is used internally by PolarsCursor and typically not instantiated directly by users. Requires polars to be installed. PyArrow is optional and only needed for as_arrow() functionality.

__init__(connection: Connection[Any], converter: Converter, query_execution: AthenaQueryExecution, arraysize: int, retry_config: RetryConfig, unload: bool = False, unload_location: str | None = None, block_size: int | None = None, cache_type: str | None = None, max_workers: int = 20, chunksize: int | None = None, result_set_type_hints: dict[str | int, str] | None = None, **kwargs) → None[source]¶

Initialize the Polars result set.

Parameters:

connection – The Athena connection object.
converter – Type converter for Athena data types.
query_execution – Query execution metadata.
arraysize – Number of rows to fetch per batch.
retry_config – Configuration for retry behavior.
unload – Whether this is an UNLOAD query result.
unload_location – S3 location for UNLOAD results.
block_size – Block size for S3 file reading.
cache_type – Cache type for S3 file system.
max_workers – Maximum number of worker threads.
chunksize – Number of rows per chunk for memory-efficient processing. If specified, data is loaded lazily in chunks for all data access methods including fetchone(), fetchmany(), and iter_chunks().
result_set_type_hints – Athena type signatures for complex-type columns, keyed by column name (case-insensitive) or zero-based column index.
**kwargs – Additional arguments passed to Polars read functions.

property dtypes: dict[str, Any]¶: Get Polars-compatible data types for result columns.

property converters: dict[str, Callable[[str | None], Any | None]]¶

Get converter functions for each column.

Returns:: Dictionary mapping column names to their converter functions.

fetchone() → tuple[Any | None, ...] | dict[Any, Any | None] | None[source]¶

Fetch the next row of the query result.

Returns:: A single row as a tuple, or None if no more rows are available.

as_polars() → pl.DataFrame[source]¶

Return query results as a Polars DataFrame.

Returns the query results as a Polars DataFrame. This is the primary method for accessing results with PolarsCursor.

Note

When chunksize is set, calling this method will collect all chunks into a single DataFrame, loading all data into memory. Use iter_chunks() for memory-efficient processing of large datasets.

Returns:: Polars DataFrame containing all query results.

Example

>>> cursor = connection.cursor(PolarsCursor)
>>> cursor.execute("SELECT * FROM my_table")
>>> df = cursor.as_polars()
>>> print(f"DataFrame has {df.height} rows")
>>> filtered = df.filter(pl.col("value") > 100)

as_arrow() → Table[source]¶

Return query results as an Apache Arrow Table.

Converts the Polars DataFrame to an Apache Arrow Table for interoperability with other Arrow-compatible tools and libraries.

Returns:: Apache Arrow Table containing all query results.
Raises:: ImportError – If pyarrow is not installed.

Example

>>> cursor = connection.cursor(PolarsCursor)
>>> cursor.execute("SELECT * FROM my_table")
>>> table = cursor.as_arrow()
>>> # Use with other Arrow-compatible libraries

DEFAULT_FETCH_SIZE: int = 1000¶

DEFAULT_RESULT_REUSE_MINUTES = 60¶

property arraysize: int¶: The default number of rows per fetchmany() call.

property catalog: str | None¶: The data catalog in the QueryExecutionContext of the query.

property completion_date_time: datetime | None¶: The date and time when the query completed.

property connection: Connection[Any]¶: The connection of the result set; raises ProgrammingError if closed.

property data_manifest_location: str | None¶: The S3 location of the data manifest that lists the files the query wrote.

property data_scanned_in_bytes: int | None¶: The number of bytes that the query scanned.

property database: str | None¶: The database in the QueryExecutionContext of the query.

property description: list[tuple[str, str, None, None, int, int, str]] | None¶

The DB API 2.0 column descriptions.

None without result metadata, or for INSERT, UPDATE, DELETE, and MERGE.

property effective_engine_version: str | None¶: The Athena engine version that ran the query.

property encryption_option: str | None¶: The EncryptionOption of the query results, such as SSE_S3 or SSE_KMS.

property engine_execution_time_in_millis: int | None¶: The time in milliseconds that the query engine took to run the query.

property error_category: int | None¶

1 for system, 2 for user, 3 for other.

Type:: The ErrorCategory of the failure

property error_message: str | None¶: The ErrorMessage that describes the query failure.

property error_type: int | None¶: The ErrorType code of the query failure.

property execution_parameters: list[str]¶: The ExecutionParameters values of the query.

property expected_bucket_owner: str | None¶: The AWS account ID expected to own the S3 bucket of the query results.

fetchall() → list[tuple[Any | None, ...] | dict[Any, Any | None]]¶

Fetch all remaining rows of the query result.

Returns:: The remaining rows.

fetchmany(size: int | None = None) → list[tuple[Any | None, ...] | dict[Any, Any | None]]¶

Fetch the next set of rows of the query result.

Parameters:: size – Maximum number of rows to fetch. If None or not positive, arraysize is used.
Returns:: The rows, fewer than size when the result is exhausted.

property is_closed: bool¶: Whether the result set is closed.

property is_unload: bool¶

Check if the query is an UNLOAD statement.

Returns:: True if the query is an UNLOAD statement, False otherwise.

property kms_key: str | None¶: The KMS key used to encrypt the query results.

property output_location: str | None¶: The S3 location of the query results.

property query: str | None¶: The SQL statement that the query execution ran.

property query_id: str | None¶: The ID of the query execution.

property query_planning_time_in_millis: int | None¶: The time in milliseconds that Athena took to plan the query.

property query_queue_time_in_millis: int | None¶: The time in milliseconds that the query waited in the queue.

property result_reuse_enabled: bool | None¶: Whether reuse of previous query results by age is enabled for the query.

property result_reuse_minutes: int | None¶: The maximum age in minutes of a previous query result that Athena can reuse.

property retryable: bool | None¶: Whether Athena reports the query failure as retryable.

property reused_previous_result: bool | None¶: Whether Athena reused a previous query result instead of running the query.

property rowcount: int¶: The number of rows affected by the last operation, or -1 if it is unknown.

property rownumber: int | None¶: The zero-based index of the next row, or None if it is unknown.

property s3_acl_option: str | None¶: The S3AclOption of the query results, such as BUCKET_OWNER_FULL_CONTROL.

property selected_engine_version: str | None¶: The Athena engine version selected to run the query.

property service_processing_time_in_millis: int | None¶: The time in milliseconds that Athena took to publish the query results.

property state: str | None¶: The state of the query execution, such as RUNNING or SUCCEEDED.

property state_change_reason: str | None¶: The StateChangeReason that gives further detail about the state.

property statement_type: str | None¶: The StatementType of the query, such as DDL, DML, or UTILITY.

property submission_date_time: datetime | None¶: The date and time when the query was submitted.

property substatement_type: str | None¶: The SubstatementType of the query, such as INSERT or MERGE.

property total_execution_time_in_millis: int | None¶: The total time in milliseconds that Athena took to run the query.

property work_group: str | None¶: The work group in which the query ran.

iter_chunks() → PolarsDataFrameIterator[source]¶

Iterate over result chunks as Polars DataFrames.

This method provides an iterator interface for processing large result sets. When chunksize is specified, it yields DataFrames in chunks using lazy evaluation for memory-efficient processing. When chunksize is not specified, it yields the entire result as a single DataFrame.

Returns:: PolarsDataFrameIterator that yields Polars DataFrames for each chunk of rows, or the entire DataFrame if chunksize was not specified.

Example

>>> # With chunking for large datasets
>>> cursor = connection.cursor(PolarsCursor, chunksize=50000)
>>> cursor.execute("SELECT * FROM large_table")
>>> for chunk in cursor.iter_chunks():
...     process_chunk(chunk)  # Each chunk is a Polars DataFrame
>>>
>>> # Without chunking - yields entire result as single chunk
>>> cursor = connection.cursor(PolarsCursor)
>>> cursor.execute("SELECT * FROM small_table")
>>> for df in cursor.iter_chunks():
...     process(df)  # Single DataFrame with all data

close() → None[source]¶: Close the result set and release resources.

class pyathena.polars.result_set.PolarsDataFrameIterator(reader: Iterator[pl.DataFrame] | pl.DataFrame, converters: dict[str, Callable[[str | None], Any | None]], column_names: list[str])[source]¶

Iterator for chunked DataFrame results from Athena queries.

This class wraps either a Polars DataFrame iterator (for chunked reading) or a single DataFrame, providing a unified iterator interface. It applies optional type conversion to each DataFrame chunk as it’s yielded.

The iterator is used by AthenaPolarsResultSet to provide chunked access to large query results, enabling memory-efficient processing of datasets that would be too large to load entirely into memory.

Example

>>> # Iterate over DataFrame chunks
>>> for df_chunk in iterator:
...     process(df_chunk)
>>>
>>> # Iterate over individual rows
>>> for idx, row in iterator.iterrows():
...     print(row)

Note

This class is primarily for internal use by AthenaPolarsResultSet. Most users should access results through PolarsCursor methods.

__init__(reader: Iterator[pl.DataFrame] | pl.DataFrame, converters: dict[str, Callable[[str | None], Any | None]], column_names: list[str]) → None[source]¶

Initialize the iterator.

Parameters:

reader – Either a DataFrame iterator (for chunked) or a single DataFrame.
converters – Dictionary mapping column names to converter functions.
column_names – List of column names in order.

__next__() → pl.DataFrame[source]¶

Get the next DataFrame chunk.

Returns:: The next Polars DataFrame chunk.
Raises:: StopIteration – When no more chunks are available.

__iter__() → PolarsDataFrameIterator[source]¶: Return self as iterator.

__enter__() → PolarsDataFrameIterator[source]¶: Context manager entry.

__exit__(exc_type, exc_value, traceback) → None[source]¶: Context manager exit.

close() → None[source]¶: Close the iterator and release resources.

iterrows() → Iterator[tuple[int, dict[str, Any]]][source]¶

Iterate over rows as (index, row_dict) tuples.

Yields:: Tuple of (row_index, row_dict) for each row across all chunks.

as_polars() → pl.DataFrame[source]¶

Collect all chunks into a single DataFrame.

Returns:: Single Polars DataFrame containing all data.

Polars Data Converters¶

class pyathena.polars.converter.DefaultPolarsTypeConverter[source]¶

Optimized type converter for Polars DataFrame results.

This converter is specifically designed for the PolarsCursor and provides optimized type conversion for Polars DataFrames.

The converter focuses on:

Mapping Athena types to Polars dtypes when reading CSV results, including decimal as pl.Decimal(precision, scale)
Converting date, time, and varbinary values, and parsing JSON values, in rows returned by fetchone(), fetchmany(), and fetchall()
Keeping array, map, and row values as strings

Example

>>> from pyathena.polars.converter import DefaultPolarsTypeConverter
>>> converter = DefaultPolarsTypeConverter()
>>>
>>> # Used automatically by PolarsCursor
>>> cursor = connection.cursor(PolarsCursor)
>>> # converter is applied automatically to results

Note

This converter is used by default in PolarsCursor. Most users don’t need to instantiate it directly.

__init__() → None[source]¶: Initialize the converter with the default Polars conversion functions and dtypes.

get_dtype(type_: str, precision: int = 0, scale: int = 0) → Any[source]¶

Get the Polars data type for a given Athena type.

Parameters:

type – The Athena data type name.
precision – The precision for decimal types.
scale – The scale for decimal types.

Returns:

The Polars data type.

convert(type_: str, value: str | None, type_hint: str | None = None) → Any | None[source]¶

Convert a value returned by Athena to a Python object.

Parameters:

type – The Athena data type name.
value – The string value to convert, or None.
type_hint – Optional Athena DDL type signature of the value.

Returns:

The converted value.

class pyathena.polars.converter.DefaultPolarsUnloadTypeConverter[source]¶

Type converter for Polars UNLOAD operations.

This converter is designed for use with UNLOAD queries that write results directly to Parquet files in S3. Since UNLOAD operations bypass the normal conversion process and write data in native Parquet format, this converter has minimal functionality.

Note

Used automatically when PolarsCursor is configured with unload=True. UNLOAD results are read directly as Polars DataFrames from Parquet files.

__init__() → None[source]¶: Initialize the converter with no type mappings.

convert(type_: str, value: str | None, type_hint: str | None = None) → Any | None[source]¶

Convert a value returned by Athena to a Python object.

Parameters:

type – The Athena data type name.
value – The string value to convert, or None.
type_hint – Optional Athena DDL type signature of the value.

Returns:

The converted value.

Polars Utilities¶

pyathena.polars.util.to_column_info(schema: pl.Schema) → tuple[dict[str, Any], ...][source]¶

Convert a Polars schema to Athena column information.

Iterates through all fields in the schema and converts each field’s type information to an Athena-compatible column metadata dictionary.

Parameters:

schema – A Polars Schema object containing field definitions.

Returns:

Name: The column name
Type: The Athena SQL type name
Precision: Numeric precision (0 for non-numeric types)
Scale: Numeric scale (0 for non-numeric types)
Nullable: Always “NULLABLE” for Polars types

Return type:

A tuple of dictionaries, each containing column metadata with keys

pyathena.polars.util.get_athena_type(dtype: Any) → tuple[str, int, int][source]¶

Map a Polars data type to an Athena SQL type.

Converts Polars type identifiers to corresponding Athena SQL type names with appropriate precision and scale values. Handles all common Polars types including numeric, string, binary, temporal, and complex types.

Parameters:

dtype – A Polars DataType object to convert.

Returns:

type_name: The Athena SQL type (e.g., “varchar”, “bigint”, “timestamp”)
precision: The numeric precision or max length
scale: The numeric scale (decimal places)

Return type:

A tuple of (type_name, precision, scale) where

Note

Unknown types default to “string” with maximum varchar length. Decimal types preserve their original precision and scale.