spark-instrumented-optimizer/python/pyspark/sql/session.pyi
zero323 31a16fbb40 [SPARK-32714][PYTHON] Initial pyspark-stubs port
### 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>
2020-09-24 14:15:36 +09:00

126 lines
4.4 KiB
Python

#
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# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
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# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
from typing import overload
from typing import Any, Iterable, List, Optional, Tuple, TypeVar, Union
from py4j.java_gateway import JavaObject # type: ignore[import]
from pyspark.sql._typing import DateTimeLiteral, LiteralType, DecimalLiteral, RowLike
from pyspark.sql.pandas._typing import DataFrameLike
from pyspark.conf import SparkConf
from pyspark.context import SparkContext
from pyspark.rdd import RDD
from pyspark.sql.catalog import Catalog
from pyspark.sql.conf import RuntimeConfig
from pyspark.sql.dataframe import DataFrame
from pyspark.sql.pandas.conversion import SparkConversionMixin
from pyspark.sql.types import AtomicType, StructType
from pyspark.sql.readwriter import DataFrameReader
from pyspark.sql.streaming import DataStreamReader, StreamingQueryManager
from pyspark.sql.udf import UDFRegistration
T = TypeVar("T")
class SparkSession(SparkConversionMixin):
class Builder:
@overload
def config(self, *, conf: SparkConf) -> SparkSession.Builder: ...
@overload
def config(self, key: str, value: Any) -> SparkSession.Builder: ...
def master(self, master: str) -> SparkSession.Builder: ...
def appName(self, name: str) -> SparkSession.Builder: ...
def enableHiveSupport(self) -> SparkSession.Builder: ...
def getOrCreate(self) -> SparkSession: ...
builder: SparkSession.Builder
def __init__(
self, sparkContext: SparkContext, jsparkSession: Optional[JavaObject] = ...
) -> None: ...
def newSession(self) -> SparkSession: ...
@classmethod
def getActiveSession(cls) -> SparkSession: ...
@property
def sparkContext(self) -> SparkContext: ...
@property
def version(self) -> str: ...
@property
def conf(self) -> RuntimeConfig: ...
@property
def catalog(self) -> Catalog: ...
@property
def udf(self) -> UDFRegistration: ...
def range(
self,
start: int,
end: Optional[int] = ...,
step: int = ...,
numPartitions: Optional[int] = ...,
) -> DataFrame: ...
@overload
def createDataFrame(
self,
data: Union[RDD[RowLike], Iterable[RowLike]],
samplingRatio: Optional[float] = ...,
) -> DataFrame: ...
@overload
def createDataFrame(
self,
data: Union[RDD[RowLike], Iterable[RowLike]],
schema: Union[List[str], Tuple[str, ...]] = ...,
verifySchema: bool = ...,
) -> DataFrame: ...
@overload
def createDataFrame(
self,
data: Union[
RDD[Union[DateTimeLiteral, LiteralType, DecimalLiteral]],
Iterable[Union[DateTimeLiteral, LiteralType, DecimalLiteral]],
],
schema: Union[AtomicType, str],
verifySchema: bool = ...,
) -> DataFrame: ...
@overload
def createDataFrame(
self,
data: Union[RDD[RowLike], Iterable[RowLike]],
schema: Union[StructType, str],
verifySchema: bool = ...,
) -> DataFrame: ...
@overload
def createDataFrame(
self, data: DataFrameLike, samplingRatio: Optional[float] = ...
) -> DataFrame: ...
@overload
def createDataFrame(
self,
data: DataFrameLike,
schema: Union[StructType, str],
verifySchema: bool = ...,
) -> DataFrame: ...
def sql(self, sqlQuery: str) -> DataFrame: ...
def table(self, tableName: str) -> DataFrame: ...
@property
def read(self) -> DataFrameReader: ...
@property
def readStream(self) -> DataStreamReader: ...
@property
def streams(self) -> StreamingQueryManager: ...
def stop(self) -> None: ...
def __enter__(self) -> SparkSession: ...
def __exit__(self, exc_type, exc_val, exc_tb) -> None: ...