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>
92 lines
2.9 KiB
Python
92 lines
2.9 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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import sys
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from pyspark import since, _NoValue
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class RuntimeConfig(object):
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"""User-facing configuration API, accessible through `SparkSession.conf`.
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Options set here are automatically propagated to the Hadoop configuration during I/O.
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"""
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def __init__(self, jconf):
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"""Create a new RuntimeConfig that wraps the underlying JVM object."""
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self._jconf = jconf
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@since(2.0)
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def set(self, key, value):
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"""Sets the given Spark runtime configuration property."""
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self._jconf.set(key, value)
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@since(2.0)
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def get(self, key, default=_NoValue):
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"""Returns the value of Spark runtime configuration property for the given key,
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assuming it is set.
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"""
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self._checkType(key, "key")
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if default is _NoValue:
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return self._jconf.get(key)
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else:
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if default is not None:
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self._checkType(default, "default")
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return self._jconf.get(key, default)
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@since(2.0)
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def unset(self, key):
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"""Resets the configuration property for the given key."""
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self._jconf.unset(key)
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def _checkType(self, obj, identifier):
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"""Assert that an object is of type str."""
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if not isinstance(obj, str):
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raise TypeError("expected %s '%s' to be a string (was '%s')" %
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(identifier, obj, type(obj).__name__))
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@since(2.4)
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def isModifiable(self, key):
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"""Indicates whether the configuration property with the given key
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is modifiable in the current session.
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"""
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return self._jconf.isModifiable(key)
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def _test():
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import os
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import doctest
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from pyspark.sql.session import SparkSession
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import pyspark.sql.conf
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os.chdir(os.environ["SPARK_HOME"])
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globs = pyspark.sql.conf.__dict__.copy()
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spark = SparkSession.builder\
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.master("local[4]")\
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.appName("sql.conf tests")\
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.getOrCreate()
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globs['sc'] = spark.sparkContext
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globs['spark'] = spark
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(failure_count, test_count) = doctest.testmod(pyspark.sql.conf, globs=globs)
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spark.stop()
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if failure_count:
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sys.exit(-1)
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if __name__ == "__main__":
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_test()
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