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https://issues.apache.org/jira/browse/SPARK-23246?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16342166#comment-16342166
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Sean Owen commented on SPARK-23246:
-----------------------------------
What's the memory leak -- in a heap dump, what objects are you saying are
retained indefinitely?
You may be legitimately using that memory, like in data about completed stages
for the UI. You may just need more driver memory or to turn down the number of
remembered jobs, etc. That is I'm not sure this established any memory leak by
itself.
> (Py)Spark OOM because of iteratively accumulated metadata that cannot be
> cleared
> --------------------------------------------------------------------------------
>
> Key: SPARK-23246
> URL: https://issues.apache.org/jira/browse/SPARK-23246
> Project: Spark
> Issue Type: Bug
> Components: PySpark, Spark Core, SQL
> Affects Versions: 2.2.1
> Reporter: MBA Learns to Code
> Priority: Critical
>
> I am having consistent OOM crashes when trying to use PySpark for iterative
> algorithms in which I create new DataFrames per iteration (e.g. by sampling
> from a "mother" DataFrame), do something with such DataFrames, and never need
> such DataFrames ever in future iterations.
> The below script simulates such OOM failures. Even when one tries explicitly
> .unpersist() the temporary DataFrames (by using the --unpersist flag below)
> and/or deleting and garbage-collecting the Python objects (by using the
> --py-gc flag below), the Java objects seem to stay on and accumulate until
> they exceed the JVM/driver memory.
> Please suggest how I may overcome this so that we can have long-running
> iterative programs using Spark that uses resources only up to a bounded,
> controllable limit.
>
> {code:java}
> from __future__ import print_function
> import argparse
> import gc
> import pandas
> import pyspark
> arg_parser = argparse.ArgumentParser()
> arg_parser.add_argument('--unpersist', action='store_true')
> arg_parser.add_argument('--py-gc', action='store_true')
> arg_parser.add_argument('--n-partitions', type=int, default=1000)
> args = arg_parser.parse_args()
> # create SparkSession (*** set spark.driver.memory to 512m in
> spark-defaults.conf ***)
> spark = pyspark.sql.SparkSession.builder \
> .config('spark.executor.instances', '2') \
> .config('spark.executor.cores', '2') \
> .config('spark.executor.memory', '512m') \
> .enableHiveSupport() \
> .getOrCreate()
> # create Parquet file for subsequent repeated loading
> df = spark.createDataFrame(
> pandas.DataFrame(
> dict(
> row=range(args.n_partitions),
> x=args.n_partitions * [0]
> )
> )
> )
> parquet_path = '/tmp/TestOOM-{}Partitions.parquet'.format(args.n_partitions)
> df.write.parquet(
> path=parquet_path,
> partitionBy='row',
> mode='overwrite'
> )
> i = 0
> # the below loop simulates an iterative algorithm that creates new DataFrames
> in each iteration (e.g. sampling from a "mother" DataFrame), do something,
> and never need those DataFrames again in future iterations
> # we are having a problem cleaning up the built-up metadata
> # hence the program will crash after while because of OOM
> while True:
> _df = spark.read.parquet(parquet_path)
> if args.unpersist:
> _df.unpersist()
> if args.py_gc:
> del _df
> gc.collect()
> i += 1; print('COMPLETED READ ITERATION #{}\n'.format(i))
> {code}
>
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