4ad9bfd53b
### What changes were proposed in this pull request? This PR aims to drop Python 2.7, 3.4 and 3.5. Roughly speaking, it removes all the widely known Python 2 compatibility workarounds such as `sys.version` comparison, `__future__`. Also, it removes the Python 2 dedicated codes such as `ArrayConstructor` in Spark. ### Why are the changes needed? 1. Unsupport EOL Python versions 2. Reduce maintenance overhead and remove a bit of legacy codes and hacks for Python 2. 3. PyPy2 has a critical bug that causes a flaky test, SPARK-28358 given my testing and investigation. 4. Users can use Python type hints with Pandas UDFs without thinking about Python version 5. Users can leverage one latest cloudpickle, https://github.com/apache/spark/pull/28950. With Python 3.8+ it can also leverage C pickle. ### Does this PR introduce _any_ user-facing change? Yes, users cannot use Python 2.7, 3.4 and 3.5 in the upcoming Spark version. ### How was this patch tested? Manually tested and also tested in Jenkins. Closes #28957 from HyukjinKwon/SPARK-32138. Authored-by: HyukjinKwon <gurwls223@apache.org> Signed-off-by: HyukjinKwon <gurwls223@apache.org>
60 lines
2.5 KiB
Python
60 lines
2.5 KiB
Python
#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# 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, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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"""
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Important classes of Spark SQL and DataFrames:
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- :class:`pyspark.sql.SparkSession`
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Main entry point for :class:`DataFrame` and SQL functionality.
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- :class:`pyspark.sql.DataFrame`
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A distributed collection of data grouped into named columns.
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- :class:`pyspark.sql.Column`
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A column expression in a :class:`DataFrame`.
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- :class:`pyspark.sql.Row`
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A row of data in a :class:`DataFrame`.
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- :class:`pyspark.sql.GroupedData`
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Aggregation methods, returned by :func:`DataFrame.groupBy`.
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- :class:`pyspark.sql.DataFrameNaFunctions`
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Methods for handling missing data (null values).
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- :class:`pyspark.sql.DataFrameStatFunctions`
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Methods for statistics functionality.
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- :class:`pyspark.sql.functions`
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List of built-in functions available for :class:`DataFrame`.
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- :class:`pyspark.sql.types`
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List of data types available.
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- :class:`pyspark.sql.Window`
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For working with window functions.
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"""
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from pyspark.sql.types import Row
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from pyspark.sql.context import SQLContext, HiveContext, UDFRegistration
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from pyspark.sql.session import SparkSession
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from pyspark.sql.column import Column
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from pyspark.sql.catalog import Catalog
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from pyspark.sql.dataframe import DataFrame, DataFrameNaFunctions, DataFrameStatFunctions
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from pyspark.sql.group import GroupedData
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from pyspark.sql.readwriter import DataFrameReader, DataFrameWriter
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from pyspark.sql.window import Window, WindowSpec
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from pyspark.sql.pandas.group_ops import PandasCogroupedOps
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__all__ = [
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'SparkSession', 'SQLContext', 'HiveContext', 'UDFRegistration',
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'DataFrame', 'GroupedData', 'Column', 'Catalog', 'Row',
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'DataFrameNaFunctions', 'DataFrameStatFunctions', 'Window', 'WindowSpec',
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'DataFrameReader', 'DataFrameWriter', 'PandasCogroupedOps'
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]
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