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https://issues.apache.org/jira/browse/SPARK-29321?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Jungtaek Lim updated SPARK-29321:
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Fix Version/s: (was: 2.4.5)
(was: 3.0.0)
> CLONE - Possible memory leak in Spark
> -------------------------------------
>
> Key: SPARK-29321
> URL: https://issues.apache.org/jira/browse/SPARK-29321
> Project: Spark
> Issue Type: Bug
> Components: Block Manager, Spark Core
> Affects Versions: 2.3.3
> Reporter: George Papa
> Assignee: Jungtaek Lim
> Priority: Major
>
> I used Spark 2.1.1 and I upgraded into new versions. After Spark version
> 2.3.3, I observed from Spark UI that the driver memory is{color:#ff0000}
> increasing continuously.{color}
> In more detail, the driver memory and executors memory have the same used
> memory storage and after each iteration the storage memory is increasing. You
> can reproduce this behavior by running the following snippet code. The
> following example, is very simple, without any dataframe persistence, but the
> memory consumption is not stable as it was in former Spark versions
> (Specifically until Spark 2.3.2).
> Also, I tested with Spark streaming and structured streaming API and I had
> the same behavior. I tested with an existing application which reads from
> Kafka source and do some aggregations, persist dataframes and then unpersist
> them. The persist and unpersist it works correct, I see the dataframes in the
> storage tab in Spark UI and after the unpersist, all dataframe have removed.
> But, after the unpersist the executors memory is not zero, BUT has the same
> value with the driver memory. This behavior also affects the application
> performance because the memory of the executors is increasing as the driver
> increasing and after a while the persisted dataframes are not fit in the
> executors memory and I have spill to disk.
> Another error which I had after a long running, was
> {color:#ff0000}java.lang.OutOfMemoryError: GC overhead limit exceeded, but I
> don't know if its relevant with the above behavior or not.{color}
>
> *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
>
> *NOTE:* 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.
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