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>
46 lines
1.5 KiB
Python
46 lines
1.5 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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import resource
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import sys
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from pyspark.sql import SparkSession
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if __name__ == "__main__":
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"""
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Usage: worker_memory_check [Memory_in_Mi]
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"""
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spark = SparkSession \
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.builder \
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.appName("PyMemoryTest") \
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.getOrCreate()
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sc = spark.sparkContext
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if len(sys.argv) < 2:
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print("Usage: worker_memory_check [Memory_in_Mi]", file=sys.stderr)
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sys.exit(-1)
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def f(x):
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rLimit = resource.getrlimit(resource.RLIMIT_AS)
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print("RLimit is " + str(rLimit))
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return rLimit
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resourceValue = sc.parallelize([1]).map(f).collect()[0][0]
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print("Resource Value is " + str(resourceValue))
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truthCheck = (resourceValue == int(sys.argv[1]))
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print("PySpark Worker Memory Check is: " + str(truthCheck))
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spark.stop()
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