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https://issues.apache.org/jira/browse/SPARK-27648?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16839097#comment-16839097
]
tommy duan commented on SPARK-27648:
------------------------------------
Hi [~kabhwan]
I'm so sorry.
The above is the main code. The broadcast updates mentioned above are all for
business.
{code:java}
public class Main{
public static void main(String[] args){
SparkSession sparkSession =..;
SparkConf conf = _SparkHelper.getSparkConf();
String resHDFSPath = SparkConfig.getInstance().getResPath(conf);
HashMap<Long, Map<String, String>> resouseData =new
HashMap<>(SparkConfig.getInstance().getRes(conf, sparkSession,resHDFSPath));
JavaSparkContext jsc =
JavaSparkContext.fromSparkContext(sparkSession.sparkContext());
Broadcast<HashMap<Long, Map<String, String>>> resBroadcast =
jsc.broadcast(resouseData);
sparkSession.streams().addListener(new
ResourceLoadListener(sparkSession,resBroadcast,resHDFSPath));
// The business code
start(sparkSession);
sparkSession.streams().awaitAnyTermination();
}
}
public class ResourceLoadListener extends StreamingQueryListener {
private SparkSession sparkSession = null;
private Broadcast<HashMap<Long, Map<String, String>>> resBroadcast = null;
private String resHDFSPath = "";
public ResourceLoadListener(SparkSession sparkSession, Broadcast<HashMap<Long,
Map<String, String>>> resBroadcast,String resHDFSPath){
this.sparkSession = sparkSession;
this.resBroadcast = resBroadcast;
this.resHDFSPath = resHDFSPath;
}
@Override
public void onQueryStarted(QueryStartedEvent event) {
}
@Override
public void onQueryProgress(QueryProgressEvent queryProgress){
try{
SparkConf conf = SparkHelper.getInstance().getSparkConf();
HashMap<Long, Map<String, String>> resouseData =new
HashMap<>(SparkConfig.getInstance().getRes(conf, sparkSession,resHDFSPath);
JavaSparkContext jsc =
JavaSparkContext.fromSparkContext(sparkSession.sparkContext());
resBroadcast.unpersist(true);
resBroadcast = jsc.broadcast(resouseData);
System.out.println("reload resource size:"+resBroadcast.getValue().size());
}catch (Exception e){
System.out.println("reload resource error:"+this.resHDFSPath+e);
}
}
@Override
public void onQueryTerminated(QueryTerminatedEvent event) {
}
}
{code}
> In Spark2.4 Structured Streaming：The executor storage memory increasing over
> time
> ---------------------------------------------------------------------------------
>
> Key: SPARK-27648
> URL: https://issues.apache.org/jira/browse/SPARK-27648
> Project: Spark
> Issue Type: Bug
> Components: Structured Streaming
> Affects Versions: 2.4.0
> Reporter: tommy duan
> Priority: Major
> Attachments: houragg(1).out, houragg_filter.csv,
> image-2019-05-09-17-51-14-036.png, image-2019-05-10-17-49-42-034.png
>
>
> *Spark Program Code Business:*
> Read the topic on kafka, aggregate the stream data sources, and then output
> it to another topic line of kafka.
> *Problem Description:*
> *1) Using spark structured streaming in CDH environment (spark 2.2)*, memory
> overflow problems often occur (because of too many versions of state stored
> in memory, this bug has been modified in spark 2.4).
> {code:java}
> /spark-submit \
> --conf “spark.yarn.executor.memoryOverhead=4096M”
> --num-executors 15 \
> --executor-memory 3G \
> --executor-cores 2 \
> --driver-memory 6G \{code}
> {code}
> Executor memory exceptions occur when running with this submit resource under
> SPARK 2.2 and the normal running time does not exceed one day.
> The solution is to set the executor memory larger than before
> {code:java}
> My spark-submit script is as follows:
> /spark-submit\
> conf "spark. yarn. executor. memoryOverhead = 4096M"
> num-executors 15\
> executor-memory 46G\
> executor-cores 3\
> driver-memory 6G\
> ...{code}
> In this case, the spark program can be guaranteed to run stably for a long
> time, and the executor storage memory is less than 10M (it has been running
> stably for more than 20 days).
> *2) From the upgrade information of Spark 2.4, we can see that the problem of
> large memory consumption of state storage has been solved in Spark 2.4.*
> So we upgraded spark to SPARK 2.4 under CDH, tried to run the spark program,
> and found that the use of memory was reduced.
> But a problem arises, as the running time increases, the storage memory of
> executor is growing (see Executors - > Storage Memory from the Spark on Yarn
> Resource Manager UI).
> This program has been running for 14 days (under SPARK 2.2, running with
> this submit resource, the normal running time is not more than one day,
> Executor memory abnormalities will occur).
> The script submitted by the program under spark2.4 is as follows:
> {code:java}
> /spark-submit \
> --conf “spark.yarn.executor.memoryOverhead=4096M”
> --num-executors 15 \
> --executor-memory 3G \
> --executor-cores 2 \
> --driver-memory 6G
> {code}
> Under Spark 2.4, I counted the size of executor memory as time went by during
> the running of the spark program:
> |Run-time(hour)|Storage Memory size（MB）|Memory growth rate(MB/hour)|
> |23.5H|41.6MB/1.5GB|1.770212766|
> |108.4H|460.2MB/1.5GB|4.245387454|
> |131.7H|559.1MB/1.5GB|4.245254366|
> |135.4H|575MB/1.5GB|4.246676514|
> |153.6H|641.2MB/1.5GB|4.174479167|
> |219H|888.1MB/1.5GB|4.055251142|
> |263H|1126.4MB/1.5GB|4.282889734|
> |309H|1228.8MB/1.5GB|3.976699029|
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