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https://issues.apache.org/jira/browse/SPARK-15716?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15322912#comment-15322912
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Yan Chen commented on SPARK-15716:
----------------------------------

Original problem comes from Hortonworks. We also tried to use community 
version. Behavior is the same. But we have already reached out to Hortonworks 
for them to investigate in this issue. Besides, we already found out that using 
version 1.6 from community with yarn 2.7.1 from Hortonworks does not have 
memory issue. So we are currently using this combination in our production. But 
version 1.4.1 still have the issue. btw, I don't know why this issue is closed. 

> Memory usage of driver keeps 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, 1.5.0, 1.6.0, 1.6.1, 2.0.0
>         Environment: Oracle Java 1.8.0_51, 1.8.0_85, 1.8.0_91 and 1.8.0_92
> SUSE Linux, CentOS 6 and CentOS 7
>            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.
> I also did more experiments in the second and third comment I posted.



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