[ https://issues.apache.org/jira/browse/SPARK-27648?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
tommy duan updated SPARK-27648: ------------------------------- Description: *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 and tried to run the spark program. And we found that the use of spark program was reduced, but a problem appeared. With the increase of running time, the storage memory of executor was increasing (see "Executors - > Storage Memory" from Spark UI). The program has been running for 14 days (running under SPARK 2.2 with this submission resource). Executor memory problems occur when the normal P-line time does not exceed 1 thrifty L. The script submitted by the program under spark2.4 is as follows: {code:java} {code} /spark-submit \ --conf “spark.yarn.executor.memoryOverhead=4096M” --num-executors 15 \ --executor-memory 3G \ --executor-cores 2 \ --driver-memory 6G \{code} On 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| was: *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 My spark-submit script is as follows: 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 and tried to run the spark program. And we found that the use of spark program was reduced, but a problem appeared. With the increase of running time, the storage memory of executor was increasing (see "Executors - > Storage Memory" from Spark UI). The program has been running for 14 days (running under SPARK 2.2 with this submission resource). Executor memory problems occur when the normal P-line time does not exceed 1 thrifty L. The script submitted by the program under spark2.4 is as follows: {code:java} {code} /spark-submit \ --conf “spark.yarn.executor.memoryOverhead=4096M” --num-executors 15 \ --executor-memory 3G \ --executor-cores 2 \ --driver-memory 6G \{code} On 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| > 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 > > *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 and tried to run the spark > program. > And we found that the use of spark program was reduced, but a problem > appeared. > With the increase of running time, the storage memory of executor was > increasing (see "Executors - > Storage Memory" from Spark UI). > The program has been running for 14 days (running under SPARK 2.2 with this > submission resource). Executor memory problems occur when the normal P-line > time does not exceed 1 thrifty L. > The script submitted by the program under spark2.4 is as follows: > {code:java} > {code} > /spark-submit \ > --conf “spark.yarn.executor.memoryOverhead=4096M” > --num-executors 15 \ > --executor-memory 3G \ > --executor-cores 2 \ > --driver-memory 6G \{code} > On 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| -- This message was sent by Atlassian JIRA (v7.6.3#76005) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org