[jira] [Updated] (SPARK-29055) Memory leak in Spark

["George Papa (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-29055?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

George Papa updated SPARK-29055:
--------------------------------
    Summary: Memory leak in Spark  (was: Memory leak in Spark Driver)

> Memory leak in Spark
> --------------------
>
>                 Key: SPARK-29055
>                 URL: https://issues.apache.org/jira/browse/SPARK-29055
>             Project: Spark
>          Issue Type: Bug
>          Components: Block Manager, Spark Core
>    Affects Versions: 2.3.3, 2.4.3, 2.4.4
>            Reporter: George Papa
>            Priority: Major
>         Attachments: test_csvs.zip
>
>
> I used Spark 2.1.1 and I upgraded into the latest version 2.4.4. I observed from Spark UI that the driver memory is{color:#ff0000} increasing continuously{color} and after of long running I had the following error : {color:#ff0000}java.lang.OutOfMemoryError: GC overhead limit exceeded{color}
> In Spark 2.1.1 the driver memory consumption (Storage Memory tab) was extremely low and after the run of ContextCleaner and BlockManager the memory was decreasing.
> Also, I tested the Spark versions 2.3.3, 2.4.3 and I had the same behavior.
>  
> *HOW TO REPRODUCE THIS BEHAVIOR:*
> Create a very simple application(streaming count_file.py) in order to reproduce this behavior. This application reads CSV files from a directory, count the rows and then remove the processed files.
> {code:java}
> import time
> import os
> from pyspark.sql import SparkSession
> from pyspark.sql import functions as F
> from pyspark.sql import types as T
> target_dir = "..."
> spark=SparkSession.builder.appName("DataframeCount").getOrCreate()
> while True:
>     for f in os.listdir(target_dir):
>         df = spark.read.load(target_dir + f, format="csv")
>         print("Number of records: {0}".format(df.count()))
>         time.sleep(15){code}
> Submit code:
> {code:java}
> spark-submit 
> --master spark://xxx.xxx.xx.xxx
> --deploy-mode client
> --executor-memory 4g
> --executor-cores 3
> streaming count_file.py
> {code}
>  
> *TESTED CASES WITH THE SAME BEHAVIOUR:*
>  * I tested with default settings (spark-defaults.conf)
>  * Add spark.cleaner.periodicGC.interval 1min (or less)
>  * {{Turn spark.cleaner.referenceTracking.blocking}}=false
>  * Run the application in cluster mode
>  * Increase/decrease the resources of the executors and driver
>  * I tested with extraJavaOptions in driver and executor -XX:+UseG1GC -XX:InitiatingHeapOccupancyPercent=35 -XX:ConcGCThreads=12
>   
> *DEPENDENCIES*
>  * Operation system: Ubuntu 16.04.3 LTS
>  * Java: jdk1.8.0_131 (tested also with jdk1.8.0_221)
>  * Python: Python 2.7.12



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