31a16fbb40
### What changes were proposed in this pull request? This PR proposes migration of [`pyspark-stubs`](https://github.com/zero323/pyspark-stubs) into Spark codebase. ### Why are the changes needed? ### Does this PR introduce _any_ user-facing change? Yes. This PR adds type annotations directly to Spark source. This can impact interaction with development tools for users, which haven't used `pyspark-stubs`. ### How was this patch tested? - [x] MyPy tests of the PySpark source ``` mypy --no-incremental --config python/mypy.ini python/pyspark ``` - [x] MyPy tests of Spark examples ``` MYPYPATH=python/ mypy --no-incremental --config python/mypy.ini examples/src/main/python/ml examples/src/main/python/sql examples/src/main/python/sql/streaming ``` - [x] Existing Flake8 linter - [x] Existing unit tests Tested against: - `mypy==0.790+dev.e959952d9001e9713d329a2f9b196705b028f894` - `mypy==0.782` Closes #29591 from zero323/SPARK-32681. Authored-by: zero323 <mszymkiewicz@gmail.com> Signed-off-by: HyukjinKwon <gurwls223@apache.org>
126 lines
4.4 KiB
Python
126 lines
4.4 KiB
Python
#
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# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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from typing import overload
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from typing import Any, Iterable, List, Optional, Tuple, TypeVar, Union
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from py4j.java_gateway import JavaObject # type: ignore[import]
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from pyspark.sql._typing import DateTimeLiteral, LiteralType, DecimalLiteral, RowLike
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from pyspark.sql.pandas._typing import DataFrameLike
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from pyspark.conf import SparkConf
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from pyspark.context import SparkContext
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from pyspark.rdd import RDD
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from pyspark.sql.catalog import Catalog
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from pyspark.sql.conf import RuntimeConfig
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from pyspark.sql.dataframe import DataFrame
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from pyspark.sql.pandas.conversion import SparkConversionMixin
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from pyspark.sql.types import AtomicType, StructType
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from pyspark.sql.readwriter import DataFrameReader
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from pyspark.sql.streaming import DataStreamReader, StreamingQueryManager
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from pyspark.sql.udf import UDFRegistration
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T = TypeVar("T")
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class SparkSession(SparkConversionMixin):
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class Builder:
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@overload
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def config(self, *, conf: SparkConf) -> SparkSession.Builder: ...
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@overload
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def config(self, key: str, value: Any) -> SparkSession.Builder: ...
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def master(self, master: str) -> SparkSession.Builder: ...
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def appName(self, name: str) -> SparkSession.Builder: ...
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def enableHiveSupport(self) -> SparkSession.Builder: ...
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def getOrCreate(self) -> SparkSession: ...
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builder: SparkSession.Builder
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def __init__(
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self, sparkContext: SparkContext, jsparkSession: Optional[JavaObject] = ...
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) -> None: ...
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def newSession(self) -> SparkSession: ...
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@classmethod
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def getActiveSession(cls) -> SparkSession: ...
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@property
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def sparkContext(self) -> SparkContext: ...
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@property
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def version(self) -> str: ...
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@property
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def conf(self) -> RuntimeConfig: ...
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@property
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def catalog(self) -> Catalog: ...
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@property
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def udf(self) -> UDFRegistration: ...
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def range(
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self,
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start: int,
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end: Optional[int] = ...,
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step: int = ...,
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numPartitions: Optional[int] = ...,
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) -> DataFrame: ...
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@overload
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def createDataFrame(
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self,
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data: Union[RDD[RowLike], Iterable[RowLike]],
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samplingRatio: Optional[float] = ...,
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) -> DataFrame: ...
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@overload
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def createDataFrame(
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self,
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data: Union[RDD[RowLike], Iterable[RowLike]],
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schema: Union[List[str], Tuple[str, ...]] = ...,
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verifySchema: bool = ...,
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) -> DataFrame: ...
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@overload
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def createDataFrame(
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self,
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data: Union[
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RDD[Union[DateTimeLiteral, LiteralType, DecimalLiteral]],
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Iterable[Union[DateTimeLiteral, LiteralType, DecimalLiteral]],
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],
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schema: Union[AtomicType, str],
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verifySchema: bool = ...,
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) -> DataFrame: ...
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@overload
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def createDataFrame(
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self,
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data: Union[RDD[RowLike], Iterable[RowLike]],
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schema: Union[StructType, str],
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verifySchema: bool = ...,
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) -> DataFrame: ...
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@overload
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def createDataFrame(
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self, data: DataFrameLike, samplingRatio: Optional[float] = ...
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) -> DataFrame: ...
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@overload
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def createDataFrame(
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self,
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data: DataFrameLike,
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schema: Union[StructType, str],
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verifySchema: bool = ...,
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) -> DataFrame: ...
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def sql(self, sqlQuery: str) -> DataFrame: ...
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def table(self, tableName: str) -> DataFrame: ...
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@property
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def read(self) -> DataFrameReader: ...
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@property
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def readStream(self) -> DataStreamReader: ...
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@property
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def streams(self) -> StreamingQueryManager: ...
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def stop(self) -> None: ...
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def __enter__(self) -> SparkSession: ...
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def __exit__(self, exc_type, exc_val, exc_tb) -> None: ...
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