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https://issues.apache.org/jira/browse/SPARK-15716?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Sean Owen updated SPARK-15716:
------------------------------
    Comment: was deleted

(was: [~yani.chen] the problem is you've described several different problems, 
and it's not clear what the problem is or whether it is due to Spark. You've 
described an increasing heap size (but no out of memory error), a stuck 
process, a crashed process, and some checkpoint problem. JIRAs aren't for 
open-ended discussion, but as much as possible about a specific isolated 
problem and its proposed resolution.)

> 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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