# -*- coding: utf-8 -*-
from __future__ import annotations
import logging
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union, cast
from pyathena.arrow.converter import (
DefaultArrowTypeConverter,
DefaultArrowUnloadTypeConverter,
)
from pyathena.arrow.result_set import AthenaArrowResultSet
from pyathena.common import BaseCursor, CursorIterator
from pyathena.error import OperationalError, ProgrammingError
from pyathena.model import AthenaCompression, AthenaFileFormat, AthenaQueryExecution
from pyathena.result_set import WithResultSet
if TYPE_CHECKING:
from pyarrow import Table
_logger = logging.getLogger(__name__) # type: ignore
[docs]
class ArrowCursor(BaseCursor, CursorIterator, WithResultSet):
"""Cursor for handling Apache Arrow Table results from Athena queries.
This cursor returns query results as Apache Arrow Tables, which provide
efficient columnar data processing and memory usage. Arrow Tables are
especially useful for analytical workloads and data science applications.
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.
Attributes:
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.arrow.cursor import ArrowCursor
>>> cursor = connection.cursor(ArrowCursor)
>>> cursor.execute("SELECT * FROM large_table")
>>> table = cursor.fetchall() # Returns pyarrow.Table
>>> df = table.to_pandas() # Convert to pandas if needed
# High-performance UNLOAD for large datasets
>>> cursor = connection.cursor(ArrowCursor, unload=True)
>>> cursor.execute("SELECT * FROM huge_table")
>>> table = cursor.fetchall() # Faster Parquet-based result
"""
[docs]
def __init__(
self,
s3_staging_dir: Optional[str] = None,
schema_name: Optional[str] = None,
catalog_name: Optional[str] = None,
work_group: Optional[str] = None,
poll_interval: float = 1,
encryption_option: Optional[str] = None,
kms_key: Optional[str] = None,
kill_on_interrupt: bool = True,
unload: bool = False,
result_reuse_enable: bool = False,
result_reuse_minutes: int = CursorIterator.DEFAULT_RESULT_REUSE_MINUTES,
on_start_query_execution: Optional[Callable[[str], None]] = None,
**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._on_start_query_execution = on_start_query_execution
self._query_id: Optional[str] = None
self._result_set: Optional[AthenaArrowResultSet] = None
[docs]
@staticmethod
def get_default_converter(
unload: bool = False,
) -> Union[DefaultArrowTypeConverter, DefaultArrowUnloadTypeConverter, Any]:
if unload:
return DefaultArrowUnloadTypeConverter()
return DefaultArrowTypeConverter()
@property
def arraysize(self) -> int:
return self._arraysize
@arraysize.setter
def arraysize(self, value: int) -> None:
if value <= 0:
raise ProgrammingError("arraysize must be a positive integer value.")
self._arraysize = value
@property # type: ignore
def result_set(self) -> Optional[AthenaArrowResultSet]:
return self._result_set
@result_set.setter
def result_set(self, val) -> None:
self._result_set = val
@property
def query_id(self) -> Optional[str]:
return self._query_id
@query_id.setter
def query_id(self, val) -> None:
self._query_id = val
@property
def rownumber(self) -> Optional[int]:
return self.result_set.rownumber if self.result_set else None
[docs]
def close(self) -> None:
if self.result_set and not self.result_set.is_closed:
self.result_set.close()
[docs]
def execute(
self,
operation: str,
parameters: Optional[Union[Dict[str, Any], List[str]]] = None,
work_group: Optional[str] = None,
s3_staging_dir: Optional[str] = None,
cache_size: Optional[int] = 0,
cache_expiration_time: Optional[int] = 0,
result_reuse_enable: Optional[bool] = None,
result_reuse_minutes: Optional[int] = None,
paramstyle: Optional[str] = None,
on_start_query_execution: Optional[Callable[[str], None]] = None,
**kwargs,
) -> ArrowCursor:
"""Execute a SQL query and return results as Apache Arrow Tables.
Executes the SQL query on Amazon Athena and configures the result set
for Apache Arrow Table output. Arrow format provides high-performance
columnar data processing with efficient memory usage.
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').
on_start_query_execution: Callback called when query starts.
**kwargs: Additional execution parameters.
Returns:
Self reference for method chaining.
Example:
>>> cursor.execute("SELECT * FROM sales WHERE year = 2023")
>>> table = cursor.as_arrow() # Returns Apache Arrow Table
"""
self._reset_state()
if self._unload:
s3_staging_dir = s3_staging_dir if s3_staging_dir else self._s3_staging_dir
assert s3_staging_dir, "If the unload option is used, s3_staging_dir is required."
operation, unload_location = self._formatter.wrap_unload(
operation,
s3_staging_dir=s3_staging_dir,
format_=AthenaFileFormat.FILE_FORMAT_PARQUET,
compression=AthenaCompression.COMPRESSION_SNAPPY,
)
else:
unload_location = None
self.query_id = 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,
)
# Call user callbacks immediately after start_query_execution
# Both connection-level and execute-level callbacks are invoked if set
if self._on_start_query_execution:
self._on_start_query_execution(self.query_id)
if on_start_query_execution:
on_start_query_execution(self.query_id)
query_execution = cast(AthenaQueryExecution, self._poll(self.query_id))
if query_execution.state == AthenaQueryExecution.STATE_SUCCEEDED:
self.result_set = AthenaArrowResultSet(
connection=self._connection,
converter=self._converter,
query_execution=query_execution,
arraysize=self.arraysize,
retry_config=self._retry_config,
unload=self._unload,
unload_location=unload_location,
**kwargs,
)
else:
raise OperationalError(query_execution.state_change_reason)
return self
[docs]
def executemany(
self,
operation: str,
seq_of_parameters: List[Optional[Union[Dict[str, Any], List[str]]]],
**kwargs,
) -> None:
for parameters in seq_of_parameters:
self.execute(operation, parameters, **kwargs)
# Operations that have result sets are not allowed with executemany.
self._reset_state()
[docs]
def cancel(self) -> None:
if not self.query_id:
raise ProgrammingError("QueryExecutionId is none or empty.")
self._cancel(self.query_id)
[docs]
def fetchone(
self,
) -> Optional[Union[Tuple[Optional[Any], ...], Dict[Any, Optional[Any]]]]:
if not self.has_result_set:
raise ProgrammingError("No result set.")
result_set = cast(AthenaArrowResultSet, self.result_set)
return result_set.fetchone()
[docs]
def fetchmany(
self, size: Optional[int] = None
) -> List[Union[Tuple[Optional[Any], ...], Dict[Any, Optional[Any]]]]:
if not self.has_result_set:
raise ProgrammingError("No result set.")
result_set = cast(AthenaArrowResultSet, self.result_set)
return result_set.fetchmany(size)
[docs]
def fetchall(
self,
) -> List[Union[Tuple[Optional[Any], ...], Dict[Any, Optional[Any]]]]:
if not self.has_result_set:
raise ProgrammingError("No result set.")
result_set = cast(AthenaArrowResultSet, self.result_set)
return result_set.fetchall()
[docs]
def as_arrow(self) -> "Table":
"""Return query results as an Apache Arrow Table.
Converts the entire result set into an Apache Arrow Table for efficient
columnar data processing. Arrow Tables provide excellent performance for
analytical workloads and interoperability with other data processing frameworks.
Returns:
Apache Arrow Table containing all query results.
Raises:
ProgrammingError: If no query has been executed or no results are available.
Example:
>>> cursor = connection.cursor(ArrowCursor)
>>> cursor.execute("SELECT * FROM my_table")
>>> table = cursor.as_arrow()
>>> print(f"Table has {table.num_rows} rows and {table.num_columns} columns")
"""
if not self.has_result_set:
raise ProgrammingError("No result set.")
result_set = cast(AthenaArrowResultSet, self.result_set)
return result_set.as_arrow()