[jira] [Created] (SPARK-23246) (Py)Spark OOM because of metadata build-up that cannot be cleaned

[MBA Learns to Code (JIRA)]

MBA Learns to Code created SPARK-23246:
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             Summary: (Py)Spark OOM because of metadata build-up that cannot be 
cleaned
                 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


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), so 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 to 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 iteration
# 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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