[
https://issues.apache.org/jira/browse/SPARK-15716?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15329990#comment-15329990
]
Yan Chen edited comment on SPARK-15716 at 6/14/16 5:45 PM:
-----------------------------------------------------------
!http://i.imgur.com/gcm4Y6p.png!
I have difficulties uploading big files from company. If you really want the
heap dump file, I can try to find a way to upload it.
This one is run on Spark 1.4.1 community version. Behavior is same as before:
memory usage keep going up.
Log shows:
{code}
Exception in thread "JobGenerator" java.lang.OutOfMemoryError: GC overhead
limit exceeded
{code}
after 30 minutes.
Parameters:
* Driver memory: 500M
* Executor memory: 500M
* # of executors: 2
* no file is put in the input dir
Code:
{code:java}
import org.apache.spark.SparkConf
import org.apache.spark.HashPartitioner
import org.apache.spark.streaming._
import org.apache.log4j.{Level, Logger}
object StatefulNetworkWordCount {
def main(args: Array[String]) {
val log4jInitialized = Logger.getRootLogger.getAllAppenders.hasMoreElements
if (!log4jInitialized) {
Logger.getRootLogger.setLevel(Level.WARN)
}
val updateFunc = (values: Seq[Int], state: Option[Int]) => {
val currentCount = values.sum
val previousCount = state.getOrElse(0)
Some(currentCount + previousCount)
}
val newUpdateFunc = (iterator: Iterator[(String, Seq[Int], Option[Int])])
=> {
iterator.flatMap(t => updateFunc(t._2, t._3).map(s => (t._1, s)))
}
val sparkConf = new SparkConf()
.setAppName("StatefulNetworkWordCount")
sparkConf.set("spark.streaming.minRememberDuration", "180s")
sparkConf.set("spark.streaming.receiver.writeAheadLog.enable", "true")
sparkConf.set("spark.streaming.unpersist", "true")
sparkConf.set("spark.streaming.ui.retainedBatches", "10")
sparkConf.set("spark.ui.retainedJobs", "10")
sparkConf.set("spark.ui.retainedStages", "10")
sparkConf.set("spark.worker.ui.retainedExecutors", "10")
sparkConf.set("spark.worker.ui.retainedDrivers", "10")
sparkConf.set("spark.sql.ui.retainedExecutions", "10")
sparkConf.set("spark.cleaner.ttl", "240s")
// Create the context with a 1 second batch size
val ssc = new StreamingContext(sparkConf, Milliseconds(args(2).toLong))
ssc.checkpoint(args(1))
// Initial RDD input to updateStateByKey
val initialRDD = ssc.sparkContext.parallelize(List(("hello", 1), ("world",
1)))
// Create a ReceiverInputDStream on target ip:port and count the
// words in input stream of \n delimited test (eg. generated by 'nc')
val lines = ssc.textFileStream(args(0))//ssc.socketTextStream(args(0),
args(1).toInt)
val words = lines.flatMap(_.split(" "))
val wordDstream = words.map(x => (x, 1))
// Update the cumulative count using updateStateByKey
// This will give a Dstream made of state (which is the cumulative count of
the words)
val stateDstream = wordDstream.updateStateByKey[Int](newUpdateFunc,
new HashPartitioner (ssc.sparkContext.defaultParallelism), true,
initialRDD)
stateDstream.print()
ssc.start()
ssc.awaitTermination()
}
}
{code}
was (Author: yani.chen):
!http://i.imgur.com/gcm4Y6p.png!
I have difficulties uploading files from company. If you really want the heap
dump file, I can try to find a way to upload it.
This one is run on Spark 1.4.1 community version. Behavior is same as before:
memory usage keep going up.
Log shows:
{code}
Exception in thread "JobGenerator" java.lang.OutOfMemoryError: GC overhead
limit exceeded
{code}
after 30 minutes.
Parameters:
* Driver memory: 500M
* Executor memory: 500M
* # of executors: 2
* no file is put in the input dir
Code:
{code:java}
import org.apache.spark.SparkConf
import org.apache.spark.HashPartitioner
import org.apache.spark.streaming._
import org.apache.log4j.{Level, Logger}
object StatefulNetworkWordCount {
def main(args: Array[String]) {
val log4jInitialized = Logger.getRootLogger.getAllAppenders.hasMoreElements
if (!log4jInitialized) {
Logger.getRootLogger.setLevel(Level.WARN)
}
val updateFunc = (values: Seq[Int], state: Option[Int]) => {
val currentCount = values.sum
val previousCount = state.getOrElse(0)
Some(currentCount + previousCount)
}
val newUpdateFunc = (iterator: Iterator[(String, Seq[Int], Option[Int])])
=> {
iterator.flatMap(t => updateFunc(t._2, t._3).map(s => (t._1, s)))
}
val sparkConf = new SparkConf()
.setAppName("StatefulNetworkWordCount")
sparkConf.set("spark.streaming.minRememberDuration", "180s")
sparkConf.set("spark.streaming.receiver.writeAheadLog.enable", "true")
sparkConf.set("spark.streaming.unpersist", "true")
sparkConf.set("spark.streaming.ui.retainedBatches", "10")
sparkConf.set("spark.ui.retainedJobs", "10")
sparkConf.set("spark.ui.retainedStages", "10")
sparkConf.set("spark.worker.ui.retainedExecutors", "10")
sparkConf.set("spark.worker.ui.retainedDrivers", "10")
sparkConf.set("spark.sql.ui.retainedExecutions", "10")
sparkConf.set("spark.cleaner.ttl", "240s")
// Create the context with a 1 second batch size
val ssc = new StreamingContext(sparkConf, Milliseconds(args(2).toLong))
ssc.checkpoint(args(1))
// Initial RDD input to updateStateByKey
val initialRDD = ssc.sparkContext.parallelize(List(("hello", 1), ("world",
1)))
// Create a ReceiverInputDStream on target ip:port and count the
// words in input stream of \n delimited test (eg. generated by 'nc')
val lines = ssc.textFileStream(args(0))//ssc.socketTextStream(args(0),
args(1).toInt)
val words = lines.flatMap(_.split(" "))
val wordDstream = words.map(x => (x, 1))
// Update the cumulative count using updateStateByKey
// This will give a Dstream made of state (which is the cumulative count of
the words)
val stateDstream = wordDstream.updateStateByKey[Int](newUpdateFunc,
new HashPartitioner (ssc.sparkContext.defaultParallelism), true,
initialRDD)
stateDstream.print()
ssc.start()
ssc.awaitTermination()
}
}
{code}
> 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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