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https://issues.apache.org/jira/browse/SPARK-29055?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16930467#comment-16930467
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Maxim Gekk commented on SPARK-29055:
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[~Geopap] Can you provide the csv files?
> Memory leak in Spark Driver
> ---------------------------
>
> 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
>
> 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 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(f, format="csv")
> print("Number of records: {0}".format(df.count()))
>
> os.remove(f)
> print("File {0} removed successfully!".format(f)){code}
> Submit code:
> {code:java}
> spark-submit
> --master spark://xxx.xxx.xx.xxx
> --deploy-mode client
> --executor-memory 4g
> --executor-cores 3
> --queue 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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