[jira] [Comment Edited] (SPARK-15716) Memory usage keep growing up in Spark Streaming

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

    [ https://issues.apache.org/jira/browse/SPARK-15716?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15313116#comment-15313116 ] 

Yan Chen edited comment on SPARK-15716 at 6/2/16 9:43 PM:
----------------------------------------------------------

I tried to run it again, with only 500M of memory on both driver and executors. The same behavior happened. 

Batches are not run any more. They are queued up because of the GC's triggered by allocation failure. 

!http://i.imgur.com/JLQYsuA.png!

!http://i.imgur.com/xfwIuxc.png!

!http://i.imgur.com/au95zSA.png!

I also took a heap dump on the process.

!http://i.imgur.com/LkqHCEq.png!


was (Author: yani.chen):
I tried to run it again, with only 500M of memory on both driver and executors. The same behavior happened. 

Batches are not run any more. They are queued up because of the GC's triggered by allocation failure. 

!http://i.imgur.com/JLQYsuA.png!

!http://i.imgur.com/xfwIuxc.png!

!http://i.imgur.com/au95zSA.png!



> Memory usage keep growing up in Spark Streaming
> -----------------------------------------------
>
>                 Key: SPARK-15716
>                 URL: https://issues.apache.org/jira/browse/SPARK-15716
>             Project: Spark
>          Issue Type: Bug
>          Components: Streaming
>    Affects Versions: 1.4.1
>         Environment: Oracle Java 1.8.0_51, SUSE Linux
>            Reporter: Yan Chen
>   Original Estimate: 48h
>  Remaining Estimate: 48h
>
> Code:
> {code:java}
> import org.apache.hadoop.io.LongWritable;
> import org.apache.hadoop.io.Text;
> import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
> import org.apache.spark.SparkConf;
> import org.apache.spark.SparkContext;
> import org.apache.spark.streaming.Durations;
> import org.apache.spark.streaming.StreamingContext;
> import org.apache.spark.streaming.api.java.JavaPairDStream;
> import org.apache.spark.streaming.api.java.JavaStreamingContext;
> import org.apache.spark.streaming.api.java.JavaStreamingContextFactory;
> public class App {
>   public static void main(String[] args) {
>     final String input = args[0];
>     final String check = args[1];
>     final long interval = Long.parseLong(args[2]);
>     final SparkConf conf = new SparkConf();
>     conf.set("spark.streaming.minRememberDuration", "180s");
>     conf.set("spark.streaming.receiver.writeAheadLog.enable", "true");
>     conf.set("spark.streaming.unpersist", "true");
>     conf.set("spark.streaming.ui.retainedBatches", "10");
>     conf.set("spark.ui.retainedJobs", "10");
>     conf.set("spark.ui.retainedStages", "10");
>     conf.set("spark.worker.ui.retainedExecutors", "10");
>     conf.set("spark.worker.ui.retainedDrivers", "10");
>     conf.set("spark.sql.ui.retainedExecutions", "10");
>     JavaStreamingContextFactory jscf = () -> {
>       SparkContext sc = new SparkContext(conf);
>       sc.setCheckpointDir(check);
>       StreamingContext ssc = new StreamingContext(sc, Durations.milliseconds(interval));
>       JavaStreamingContext jssc = new JavaStreamingContext(ssc);
>       jssc.checkpoint(check);
>       // setup pipeline here
>       JavaPairDStream<LongWritable, Text> inputStream =
>           jssc.fileStream(
>               input,
>               LongWritable.class,
>               Text.class,
>               TextInputFormat.class,
>               (filepath) -> Boolean.TRUE,
>               false
>           );
>       JavaPairDStream<LongWritable, Text> usbk = inputStream
>           .updateStateByKey((current, state) -> state);
>       usbk.checkpoint(Durations.seconds(10));
>       usbk.foreachRDD(rdd -> {
>         rdd.count();
>         System.out.println("usbk: " + rdd.toDebugString().split("\n").length);
>         return null;
>       });
>       return jssc;
>     };
>     JavaStreamingContext jssc = JavaStreamingContext.getOrCreate(check, jscf);
>     jssc.start();
>     jssc.awaitTermination();
>   }
> }
> {code}
> Command used to run the code
> {code:none}
> spark-submit --keytab [keytab] --principal [principal] --class [package].App --master yarn --driver-memory 1g --executor-memory 1G --conf "spark.driver.maxResultSize=0" --conf "spark.logConf=true" --conf "spark.executor.instances=2" --conf "spark.executor.extraJavaOptions=-XX:+PrintFlagsFinal -XX:+PrintReferenceGC -verbose:gc -XX:+PrintGCDetails -XX:+PrintGCTimeStamps -XX:+PrintAdaptiveSizePolicy -XX:+UnlockDiagnosticVMOptions" --conf "spark.driver.extraJavaOptions=-Xloggc:/[dir]/memory-gc.log -XX:+PrintFlagsFinal -XX:+PrintReferenceGC -verbose:gc -XX:+PrintGCDetails -XX:+PrintGCTimeStamps -XX:+PrintAdaptiveSizePolicy -XX:+UnlockDiagnosticVMOptions" [jar-file-path] file:///[dir-on-nas-drive] [dir-on-hdfs] 200
> {code}
> It's a very simple piece of code, when I ran it, the memory usage of driver keeps going up. There is no file input in our runs. Batch interval is set to 200 milliseconds; processing time for each batch is below 150 milliseconds, while most of which are below 70 milliseconds.
> !http://i.imgur.com/uSzUui6.png!
> The right most four red triangles are full GC's which are triggered manually by using "jcmd pid GC.run" command.



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