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From "George Papa (Jira)" <j...@apache.org>
Subject [jira] [Created] (SPARK-29321) CLONE - Possible memory leak in Spark
Date Tue, 01 Oct 2019 20:01:00 GMT
George Papa created SPARK-29321:
-----------------------------------

             Summary: 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
             Fix For: 2.4.5, 3.0.0


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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