from __future__ import annotations
import asyncio
import logging
from collections.abc import Iterable
from multiprocessing import cpu_count
from typing import (
TYPE_CHECKING,
Any,
cast,
)
from pyathena.aio.common import WithAsyncFetch
from pyathena.common import CursorIterator
from pyathena.error import OperationalError, ProgrammingError
from pyathena.model import AthenaQueryExecution
from pyathena.pandas.converter import (
DefaultPandasTypeConverter,
DefaultPandasUnloadTypeConverter,
)
from pyathena.pandas.result_set import AthenaPandasResultSet, PandasDataFrameIterator
if TYPE_CHECKING:
from pandas import DataFrame
_logger = logging.getLogger(__name__)
[docs]
class AioPandasCursor(WithAsyncFetch):
"""Native asyncio cursor that returns results as pandas DataFrames.
Uses ``asyncio.to_thread()`` for both result set creation and fetch
operations, keeping the event loop free. This is especially important
when ``chunksize`` is set, as fetch calls trigger lazy S3 reads.
Example:
>>> async with await pyathena.aio_connect(...) as conn:
... cursor = conn.cursor(AioPandasCursor)
... await cursor.execute("SELECT * FROM my_table")
... df = cursor.as_pandas()
"""
[docs]
def __init__(
self,
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,
engine: str = "auto",
chunksize: int | None = None,
block_size: int | None = None,
cache_type: str | None = None,
max_workers: int = (cpu_count() or 1) * 5,
result_reuse_enable: bool = False,
result_reuse_minutes: int = CursorIterator.DEFAULT_RESULT_REUSE_MINUTES,
auto_optimize_chunksize: bool = False,
**kwargs,
) -> None:
super().__init__(
s3_staging_dir=s3_staging_dir,
schema_name=schema_name,
catalog_name=catalog_name,
work_group=work_group,
poll_interval=poll_interval,
encryption_option=encryption_option,
kms_key=kms_key,
kill_on_interrupt=kill_on_interrupt,
result_reuse_enable=result_reuse_enable,
result_reuse_minutes=result_reuse_minutes,
**kwargs,
)
self._unload = unload
self._engine = engine
self._chunksize = chunksize
self._block_size = block_size
self._cache_type = cache_type
self._max_workers = max_workers
self._auto_optimize_chunksize = auto_optimize_chunksize
self._result_set: AthenaPandasResultSet | None = None
[docs]
@staticmethod
def get_default_converter(
unload: bool = False,
) -> DefaultPandasTypeConverter | Any:
if unload:
return DefaultPandasUnloadTypeConverter()
return DefaultPandasTypeConverter()
[docs]
async def execute( # type: ignore[override]
self,
operation: str,
parameters: dict[str, Any] | list[str] | None = None,
work_group: str | None = None,
s3_staging_dir: str | None = None,
cache_size: int | None = 0,
cache_expiration_time: int | None = 0,
result_reuse_enable: bool | None = None,
result_reuse_minutes: int | None = None,
paramstyle: str | None = None,
keep_default_na: bool = False,
na_values: Iterable[str] | None = ("",),
quoting: int = 1,
**kwargs,
) -> AioPandasCursor:
"""Execute a SQL query asynchronously and return results as pandas DataFrames.
Args:
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').
keep_default_na: Whether to keep default pandas NA values.
na_values: Additional values to treat as NA.
quoting: CSV quoting behavior (pandas csv.QUOTE_* constants).
**kwargs: Additional pandas read_csv/read_parquet parameters.
Returns:
Self reference for method chaining.
"""
self._reset_state()
operation, unload_location = self._prepare_unload(operation, s3_staging_dir)
self.query_id = await self._execute(
operation,
parameters=parameters,
work_group=work_group,
s3_staging_dir=s3_staging_dir,
cache_size=cache_size,
cache_expiration_time=cache_expiration_time,
result_reuse_enable=result_reuse_enable,
result_reuse_minutes=result_reuse_minutes,
paramstyle=paramstyle,
)
query_execution = await self._poll(self.query_id)
if query_execution.state == AthenaQueryExecution.STATE_SUCCEEDED:
self.result_set = await asyncio.to_thread(
AthenaPandasResultSet,
connection=self._connection,
converter=self._converter,
query_execution=query_execution,
arraysize=self.arraysize,
retry_config=self._retry_config,
keep_default_na=keep_default_na,
na_values=na_values,
quoting=quoting,
unload=self._unload,
unload_location=unload_location,
engine=kwargs.pop("engine", self._engine),
chunksize=kwargs.pop("chunksize", self._chunksize),
block_size=kwargs.pop("block_size", self._block_size),
cache_type=kwargs.pop("cache_type", self._cache_type),
max_workers=kwargs.pop("max_workers", self._max_workers),
auto_optimize_chunksize=self._auto_optimize_chunksize,
**kwargs,
)
else:
raise OperationalError(query_execution.state_change_reason)
return self
[docs]
async def fetchone( # type: ignore[override]
self,
) -> tuple[Any | None, ...] | dict[Any, Any | None] | None:
"""Fetch the next row of the result set.
Wraps the synchronous fetch in ``asyncio.to_thread`` to avoid
blocking the event loop when ``chunksize`` triggers lazy S3 reads.
Returns:
A tuple representing the next row, or None if no more rows.
Raises:
ProgrammingError: If no result set is available.
"""
if not self.has_result_set:
raise ProgrammingError("No result set.")
result_set = cast(AthenaPandasResultSet, self.result_set)
return await asyncio.to_thread(result_set.fetchone)
[docs]
async def fetchmany( # type: ignore[override]
self, size: int | None = None
) -> list[tuple[Any | None, ...] | dict[Any, Any | None]]:
"""Fetch multiple rows from the result set.
Wraps the synchronous fetch in ``asyncio.to_thread`` to avoid
blocking the event loop when ``chunksize`` triggers lazy S3 reads.
Args:
size: Maximum number of rows to fetch. Defaults to arraysize.
Returns:
List of tuples representing the fetched rows.
Raises:
ProgrammingError: If no result set is available.
"""
if not self.has_result_set:
raise ProgrammingError("No result set.")
result_set = cast(AthenaPandasResultSet, self.result_set)
return await asyncio.to_thread(result_set.fetchmany, size)
[docs]
async def fetchall( # type: ignore[override]
self,
) -> list[tuple[Any | None, ...] | dict[Any, Any | None]]:
"""Fetch all remaining rows from the result set.
Wraps the synchronous fetch in ``asyncio.to_thread`` to avoid
blocking the event loop when ``chunksize`` triggers lazy S3 reads.
Returns:
List of tuples representing all remaining rows.
Raises:
ProgrammingError: If no result set is available.
"""
if not self.has_result_set:
raise ProgrammingError("No result set.")
result_set = cast(AthenaPandasResultSet, self.result_set)
return await asyncio.to_thread(result_set.fetchall)
async def __anext__(self):
row = await self.fetchone()
if row is None:
raise StopAsyncIteration
return row
[docs]
def as_pandas(self) -> DataFrame | PandasDataFrameIterator:
"""Return DataFrame or PandasDataFrameIterator based on chunksize setting.
Returns:
DataFrame when chunksize is None, PandasDataFrameIterator when chunksize is set.
"""
if not self.has_result_set:
raise ProgrammingError("No result set.")
result_set = cast(AthenaPandasResultSet, self.result_set)
return result_set.as_pandas()