databricks-sql-python/src/databricks/sql/result_set.py at main · databricks/databricks-sql-python · GitHub

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from __future__ import annotations

from abc import ABC, abstractmethod

from typing import List, Optional, TYPE_CHECKING, Tuple

import logging

import pandas

try:

import pyarrow

except ImportError:

pyarrow = None

if TYPE_CHECKING:

from databricks.sql.backend.thrift_backend import ThriftDatabricksClient

from databricks.sql.client import Connection

from databricks.sql.backend.databricks_client import DatabricksClient

from databricks.sql.types import Row

from databricks.sql.exc import RequestError, CursorAlreadyClosedError

from databricks.sql.utils import (

ColumnTable,

ColumnQueue,

concat_table_chunks,

)

from databricks.sql.backend.types import CommandId, CommandState, ExecuteResponse

from databricks.sql.telemetry.models.event import StatementType

logger = logging.getLogger(__name__)

class ResultSet(ABC):

"""

Abstract base class for result sets returned by different backend implementations.

This class defines the interface that all concrete result set implementations must follow.

"""

def __init__(

self,

connection: Connection,

backend: DatabricksClient,

arraysize: int,

buffer_size_bytes: int,

command_id: CommandId,

status: CommandState,

has_been_closed_server_side: bool = False,

has_more_rows: bool = False,

results_queue=None,

description: List[Tuple] = [],

is_staging_operation: bool = False,

lz4_compressed: bool = False,

arrow_schema_bytes: Optional[bytes] = None,

num_modified_rows: Optional[int] = None,

"""

A ResultSet manages the results of a single command.

Parameters:

:param connection: The parent connection that was used to execute this command

:param backend: The specialised backend client to be invoked in the fetch phase

:param arraysize: The max number of rows to fetch at a time (PEP-249)

:param buffer_size_bytes: The size (in bytes) of the internal buffer + max fetch

:param command_id: The command ID

:param status: The command status

:param has_been_closed_server_side: Whether the command has been closed on the server

:param has_more_rows: Whether the command has more rows

:param results_queue: The results queue

:param description: column description of the results

:param is_staging_operation: Whether the command is a staging operation

"""

self.connection = connection

self.backend = backend

self.arraysize = arraysize

self.buffer_size_bytes = buffer_size_bytes

self._next_row_index = 0

self.description = description

self.command_id = command_id

self.status = status

self.has_been_closed_server_side = has_been_closed_server_side

self.has_more_rows = has_more_rows

self.results = results_queue

self._is_staging_operation = is_staging_operation

self.lz4_compressed = lz4_compressed

self._arrow_schema_bytes = arrow_schema_bytes

# Affected-row count for DML; None for SELECT / unreported.

self.num_modified_rows = num_modified_rows

def __iter__(self):

while True:

row = self.fetchone()

if row:

yield row

else:

break

def _convert_arrow_table(self, table):

column_names = [c[0] for c in self.description]

ResultRow = Row(*column_names)

if self.connection.disable_pandas is True:

return [

ResultRow(*[v.as_py() for v in r]) for r in zip(*table.itercolumns())

]

# Need to use nullable types, as otherwise type can change when there are missing values.

# See https://arrow.apache.org/docs/python/pandas.html#nullable-types

# NOTE: This api is epxerimental https://pandas.pydata.org/pandas-docs/stable/user_guide/integer_na.html

dtype_mapping = {

pyarrow.int8(): pandas.Int8Dtype(),

pyarrow.int16(): pandas.Int16Dtype(),

pyarrow.int32(): pandas.Int32Dtype(),

pyarrow.int64(): pandas.Int64Dtype(),

pyarrow.uint8(): pandas.UInt8Dtype(),

pyarrow.uint16(): pandas.UInt16Dtype(),

pyarrow.uint32(): pandas.UInt32Dtype(),

pyarrow.uint64(): pandas.UInt64Dtype(),

pyarrow.bool_(): pandas.BooleanDtype(),

pyarrow.float32(): pandas.Float32Dtype(),

pyarrow.float64(): pandas.Float64Dtype(),

pyarrow.string(): pandas.StringDtype(),

}

# Need to rename columns, as the to_pandas function cannot handle duplicate column names

table_renamed = table.rename_columns([str(c) for c in range(table.num_columns)])

df = table_renamed.to_pandas(

types_mapper=dtype_mapping.get,

date_as_object=True,

timestamp_as_object=True,

)

res = df.to_numpy(na_value=None, dtype="object")

return [ResultRow(*v) for v in res]

@property

def rownumber(self):

return self._next_row_index

@property

def is_staging_operation(self) -> bool:

"""Whether this result set represents a staging operation."""

return self._is_staging_operation

@abstractmethod

def fetchone(self) -> Optional[Row]:

"""Fetch the next row of a query result set."""

pass

@abstractmethod

def fetchmany(self, size: int) -> List[Row]:

"""Fetch the next set of rows of a query result."""

pass

@abstractmethod

def fetchall(self) -> List[Row]:

"""Fetch all remaining rows of a query result."""

pass

@abstractmethod

def fetchmany_arrow(self, size: int) -> "pyarrow.Table":

"""Fetch the next set of rows as an Arrow table."""

pass

@abstractmethod

def fetchall_arrow(self) -> "pyarrow.Table":

"""Fetch all remaining rows as an Arrow table."""

pass

def close(self) -> None:

"""

Close the result set.

If the connection has not been closed, and the result set has not already

been closed on the server for some other reason, issue a request to the server to close it.

"""

try:

if self.results is not None:

self.results.close()

else:

logger.warning("result set close: queue not initialized")

if (

self.status != CommandState.CLOSED

and not self.has_been_closed_server_side

and self.connection.open

self.backend.close_command(self.command_id)

except RequestError as e:

if isinstance(e.args[1], CursorAlreadyClosedError):

logger.info("Operation was canceled by a prior request")

finally:

self.has_been_closed_server_side = True

self.status = CommandState.CLOSED

class ThriftResultSet(ResultSet):

"""ResultSet implementation for the Thrift backend."""

def __init__(

self,

connection: Connection,

execute_response: ExecuteResponse,

thrift_client: ThriftDatabricksClient,

buffer_size_bytes: int = 104857600,

arraysize: int = 10000,

use_cloud_fetch: bool = True,

t_row_set=None,

max_download_threads: int = 10,

ssl_options=None,

has_more_rows: bool = True,

"""

Initialize a ThriftResultSet with direct access to the ThriftDatabricksClient.

Parameters:

:param connection: The parent connection

:param execute_response: Response from the execute command

:param thrift_client: The ThriftDatabricksClient instance for direct access

:param buffer_size_bytes: Buffer size for fetching results

:param arraysize: Default number of rows to fetch

:param use_cloud_fetch: Whether to use cloud fetch for retrieving results

:param t_row_set: The TRowSet containing result data (if available)

:param max_download_threads: Maximum number of download threads for cloud fetch

:param ssl_options: SSL options for cloud fetch

:param has_more_rows: Whether there are more rows to fetch

"""

self.num_chunks = 0

# Initialize ThriftResultSet-specific attributes

self._use_cloud_fetch = use_cloud_fetch

self.has_more_rows = has_more_rows

# Build the results queue if t_row_set is provided

results_queue = None

if t_row_set and execute_response.result_format is not None:

from databricks.sql.utils import ThriftResultSetQueueFactory

# Create the results queue using the provided format

results_queue = ThriftResultSetQueueFactory.build_queue(

row_set_type=execute_response.result_format,

t_row_set=t_row_set,

arrow_schema_bytes=execute_response.arrow_schema_bytes or b"",

max_download_threads=max_download_threads,

lz4_compressed=execute_response.lz4_compressed,

description=execute_response.description,

ssl_options=ssl_options,

session_id_hex=connection.get_session_id_hex(),

statement_id=execute_response.command_id.to_hex_guid(),

chunk_id=self.num_chunks,

http_client=connection.http_client,

)

if t_row_set.resultLinks:

self.num_chunks += len(t_row_set.resultLinks)

# Call parent constructor with common attributes

super().__init__(

connection=connection,

backend=thrift_client,

arraysize=arraysize,

buffer_size_bytes=buffer_size_bytes,

command_id=execute_response.command_id,

status=execute_response.status,

has_been_closed_server_side=execute_response.has_been_closed_server_side,

has_more_rows=has_more_rows,

results_queue=results_queue,

description=execute_response.description,

is_staging_operation=execute_response.is_staging_operation,

lz4_compressed=execute_response.lz4_compressed,

arrow_schema_bytes=execute_response.arrow_schema_bytes,

num_modified_rows=execute_response.num_modified_rows,

)

# Initialize results queue if not provided

if not self.results:

self._fill_results_buffer()

def _fill_results_buffer(self):

results, has_more_rows, result_links_count = self.backend.fetch_results(

command_id=self.command_id,

max_rows=self.arraysize,

max_bytes=self.buffer_size_bytes,

expected_row_start_offset=self._next_row_index,

lz4_compressed=self.lz4_compressed,

arrow_schema_bytes=self._arrow_schema_bytes,

description=self.description,

use_cloud_fetch=self._use_cloud_fetch,

chunk_id=self.num_chunks,

)

self.results = results

self.has_more_rows = has_more_rows

self.num_chunks += result_links_count

def _convert_columnar_table(self, table):

column_names = [c[0] for c in self.description]

ResultRow = Row(*column_names)

result = []

for row_index in range(table.num_rows):

curr_row = []

for col_index in range(table.num_columns):

curr_row.append(table.get_item(col_index, row_index))

result.append(ResultRow(*curr_row))

return result

def fetchmany_arrow(self, size: int) -> "pyarrow.Table":

"""

Fetch the next set of rows of a query result, returning a PyArrow table.

An empty sequence is returned when no more rows are available.

"""

if size < 0:

raise ValueError("size argument for fetchmany is %s but must be >= 0", size)

# Hold 0-row chunks aside instead of appending them to ``partial_result_chunks``.

# CloudFetchQueue may return a placeholder empty table whose schema does not

# match the real downloaded chunks; concatenating it would corrupt the result.

partial_result_chunks: List["pyarrow.Table"] = []

zero_row_table: Optional["pyarrow.Table"] = None

n_remaining_rows = size

results = self.results.next_n_rows(size)

if results.num_rows == 0:

zero_row_table = results

else:

partial_result_chunks.append(results)

n_remaining_rows -= results.num_rows

self._next_row_index += results.num_rows

while (

n_remaining_rows > 0

and not self.has_been_closed_server_side

and self.has_more_rows

self._fill_results_buffer()

partial_results = self.results.next_n_rows(n_remaining_rows)

if partial_results.num_rows == 0:

continue

partial_result_chunks.append(partial_results)

n_remaining_rows -= partial_results.num_rows

self._next_row_index += partial_results.num_rows

if not partial_result_chunks:

partial_result_chunks.append(zero_row_table)

return concat_table_chunks(partial_result_chunks)

def fetchmany_columnar(self, size: int):

"""

Fetch the next set of rows of a query result, returning a Columnar Table.

An empty sequence is returned when no more rows are available.

"""

if size < 0:

raise ValueError("size argument for fetchmany is %s but must be >= 0", size)

results = self.results.next_n_rows(size)

n_remaining_rows = size - results.num_rows

self._next_row_index += results.num_rows

partial_result_chunks = [results]

while (

n_remaining_rows > 0

and not self.has_been_closed_server_side

and self.has_more_rows

self._fill_results_buffer()

partial_results = self.results.next_n_rows(n_remaining_rows)

partial_result_chunks.append(partial_results)

n_remaining_rows -= partial_results.num_rows

self._next_row_index += partial_results.num_rows

return concat_table_chunks(partial_result_chunks)

def fetchall_arrow(self) -> "pyarrow.Table":

"""Fetch all (remaining) rows of a query result, returning them as a PyArrow table."""

# Hold 0-row chunks aside instead of appending them to ``partial_result_chunks``.

# CloudFetchQueue may return a placeholder empty table whose schema does not

# match the real downloaded chunks; concatenating it would corrupt the result.

partial_result_chunks: List = []

zero_row_table: Optional["pyarrow.Table"] = None

results = self.results.remaining_rows()

if results.num_rows == 0:

zero_row_table = results

else:

partial_result_chunks.append(results)

self._next_row_index += results.num_rows

while not self.has_been_closed_server_side and self.has_more_rows:

self._fill_results_buffer()

partial_results = self.results.remaining_rows()

if partial_results.num_rows == 0:

continue

partial_result_chunks.append(partial_results)

self._next_row_index += partial_results.num_rows

if not partial_result_chunks:

partial_result_chunks.append(zero_row_table)

result_table = concat_table_chunks(partial_result_chunks)

# If PyArrow is installed and we have a ColumnTable result, convert it to PyArrow Table

# Valid only for metadata commands result set

if isinstance(result_table, ColumnTable) and pyarrow:

data = {

name: col

for name, col in zip(

result_table.column_names, result_table.column_table

)

}

return pyarrow.Table.from_pydict(data)

return result_table

def fetchall_columnar(self):

"""Fetch all (remaining) rows of a query result, returning them as a Columnar table."""

results = self.results.remaining_rows()

self._next_row_index += results.num_rows

partial_result_chunks = [results]

while not self.has_been_closed_server_side and self.has_more_rows:

self._fill_results_buffer()

partial_results = self.results.remaining_rows()

partial_result_chunks.append(partial_results)

self._next_row_index += partial_results.num_rows

return concat_table_chunks(partial_result_chunks)

def fetchone(self) -> Optional[Row]:

"""

Fetch the next row of a query result set, returning a single sequence,

or None when no more data is available.

"""

if isinstance(self.results, ColumnQueue):

res = self._convert_columnar_table(self.fetchmany_columnar(1))

else:

res = self._convert_arrow_table(self.fetchmany_arrow(1))

if len(res) > 0:

return res[0]

else:

return None

def fetchall(self) -> List[Row]:

"""

Fetch all (remaining) rows of a query result, returning them as a list of rows.

"""

if isinstance(self.results, ColumnQueue):

return self._convert_columnar_table(self.fetchall_columnar())

else:

return self._convert_arrow_table(self.fetchall_arrow())

def fetchmany(self, size: int) -> List[Row]:

"""

Fetch the next set of rows of a query result, returning a list of rows.

An empty sequence is returned when no more rows are available.

"""

if isinstance(self.results, ColumnQueue):

return self._convert_columnar_table(self.fetchmany_columnar(size))

else:

return self._convert_arrow_table(self.fetchmany_arrow(size))

@staticmethod

def _get_schema_description(table_schema_message):

"""

Takes a TableSchema message and returns a description 7-tuple as specified by PEP-249

"""

def map_col_type(type_):

if type_.startswith("decimal"):

return "decimal"

else:

return type_

return [

(column.name, map_col_type(column.datatype), None, None, None, None, None)

for column in table_schema_message.columns

]

databricks-sql-python/src/databricks/sql/result_set.py at main · databricks/databricks-sql-python · GitHub

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