[jira] [Commented] (SPARK-10529) When creating multiple HiveContext objects in one jvm, jdbc connections to metastore cann't be released and it may cause PermGen OutOfMemoryError.

["ZhengYaofeng (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-10529?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14738541#comment-14738541 ] 

ZhengYaofeng commented on SPARK-10529:
--------------------------------------

I find that IsolatedClientLoader class contains a attribute called 'classLoader', created as a URLClassLoader object. So, if you create a new IsolatedClientLoader object, you will get a new URLClassLoader object. As you know, different URLClassLoader objects will not find each other's loaded classes, so they will load new and the same amount of classes, including ClientWrapper, ReflectionMagic and so on. Maybe you've got something. Yeah, in my test program, I create multiple HiveContext objects, it means multiple IsolatedClientLoader objects and multiple URLClassLoader objects are created. Maybe IsolatedClientLoader objects and URLClassLoader objects can be released, however classes loaded by different URLClassLoader objects can't be released. 

I made a patch to resolve this problem. I just put a ThreadLocal variable to store the URLClassLoader object. So, every time you create a HiveContext object, it will use the same classLoader and it can find classes already loaded by itself. You won't worry about the jdbc connections being released or loading classes repeatedly.

> When creating multiple HiveContext objects in one jvm, jdbc connections to metastore cann't be released and it may cause PermGen OutOfMemoryError.
> --------------------------------------------------------------------------------------------------------------------------------------------------
>
>                 Key: SPARK-10529
>                 URL: https://issues.apache.org/jira/browse/SPARK-10529
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 1.4.1
>            Reporter: ZhengYaofeng
>         Attachments: IsolatedClientLoader.scala
>
>
> Test code as follows:
> object SqlTest {
>   def main(args: Array[String]) {
>     def createSc = {
>       val sparkConf = new SparkConf().setAppName(s"SqlTest")
>         .setMaster("spark://zdh221:7077")
>         .set("spark.executor.memory", "4g")
>         .set("spark.executor.cores", "2")
>         .set("spark.cores.max", "6")
>       new SparkContext(sparkConf)
>     }
>     for (index <- 1 to 200) {
>       println(s"============Current Index:${index}=============")
>       val hc = new HiveContext(createSc)
>       hc.sql("show databases").collect().foreach(println)
>       hc.sparkContext.stop()
>       Thread.sleep(3000)
>     }
>     Thread.sleep(1000000)
>   }
> }	
> Testing on spark 1.4.1 with run cmd bellow.
> 	export CLASSPATH="$CLASSPATH:/home/hadoop/spark/conf:/home/hadoop/spark/lib/*:/home/hadoop/zyf/lib/*"
> 	java -Xmx8096m -Xms1024m -XX:MaxPermSize=1024m -cp $CLASSPATH SqlTest
> Files list:
> 	/home/hadoop/spark/conf:core-site.xml;hdfs-site.xml;hive-site.xml;slaves;spark-defaults.conf;spark-env.sh
> 	/home/hadoop/zyf/lib:hadoop-lzo-0.4.20.jar;mysql-connector-java-5.1.28-bin.jar;sqltest-1.0-SNAPSHOT.jar
> 	
> MySQL is used as the metastore. You can obviously see that jdbc connections to MySQL grow constantly through command 'show status like 'Threads_connected';' when my test app is running. Even if you invoke 'Hive.closeCurrent()', it cann't release current jdbc connections. Besides I can not find another possible way. If you take spark 1.3.1 to test, jdbc connections won't grow.
> Meanwhile, it ends with 'java.lang.OutOfMemoryError: PermGen space' when cycling 45 times, which means 45 HiveContext objects are created. It's interesting that if you set MaxPermSize to '2048m', it can cycle 93 times, if you set MaxPermSize to '3072m', it can cycle 141 times. So,it indicates that each time creating one HiveContext object, it loads the same amount of new classes and they won't be released.



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