[jira] [Commented] (SPARK-15796) Reduce spark.memory.fraction default to avoid overrunning old gen in JVM default config

["koert kuipers (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-15796?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15378776#comment-15378776 ] 

koert kuipers commented on SPARK-15796:
---------------------------------------

we ran into this as well with a long lived application that cached a large amount of rdds. we resorted to either spark.memory.useLegacyMode or spark.memory.fraction = 0.5

> Reduce spark.memory.fraction default to avoid overrunning old gen in JVM default config
> ---------------------------------------------------------------------------------------
>
>                 Key: SPARK-15796
>                 URL: https://issues.apache.org/jira/browse/SPARK-15796
>             Project: Spark
>          Issue Type: Improvement
>    Affects Versions: 1.6.0, 1.6.1
>            Reporter: Gabor Feher
>            Assignee: Sean Owen
>            Priority: Blocker
>             Fix For: 2.0.0
>
>         Attachments: baseline.txt, memfrac06.txt, memfrac063.txt, memfrac066.txt
>
>
> While debugging performance issues in a Spark program, I've found a simple way to slow down Spark 1.6 significantly by filling the RDD memory cache. This seems to be a regression, because setting "spark.memory.useLegacyMode=true" fixes the problem. Here is a repro that is just a simple program that fills the memory cache of Spark using a MEMORY_ONLY cached RDD (but of course this comes up in more complex situations, too):
> {code}
> import org.apache.spark.SparkContext
> import org.apache.spark.SparkConf
> import org.apache.spark.storage.StorageLevel
> object CacheDemoApp { 
>   def main(args: Array[String]) {
>     val conf = new SparkConf().setAppName("Cache Demo Application")                                       
>     val sc = new SparkContext(conf)
>     val startTime = System.currentTimeMillis()
>                                                                                                           
>     val cacheFiller = sc.parallelize(1 to 500000000, 1000)                                                
>       .mapPartitionsWithIndex {
>         case (ix, it) =>
>           println(s"CREATE DATA PARTITION ${ix}")                                                         
>           val r = new scala.util.Random(ix)
>           it.map(x => (r.nextLong, r.nextLong))
>       }
>     cacheFiller.persist(StorageLevel.MEMORY_ONLY)
>     cacheFiller.foreach(identity)
>     val finishTime = System.currentTimeMillis()
>     val elapsedTime = (finishTime - startTime) / 1000
>     println(s"TIME= $elapsedTime s")
>   }
> }
> {code}
> If I call it the following way, it completes in around 5 minutes on my Laptop, while often stopping for slow Full GC cycles. I can also see with jvisualvm (Visual GC plugin) that the old generation of JVM is 96.8% filled.
> {code}
> sbt package
> ~/spark-1.6.0/bin/spark-submit \
>   --class "CacheDemoApp" \
>   --master "local[2]" \
>   --driver-memory 3g \
>   --driver-java-options "-XX:+PrintGCDetails" \
>   target/scala-2.10/simple-project_2.10-1.0.jar
> {code}
> If I add any one of the below flags, then the run-time drops to around 40-50 seconds and the difference is coming from the drop in GC times:
>   --conf "spark.memory.fraction=0.6"
> OR
>   --conf "spark.memory.useLegacyMode=true"
> OR
>   --driver-java-options "-XX:NewRatio=3"
> All the other cache types except for DISK_ONLY produce similar symptoms. It looks like that the problem is that the amount of data Spark wants to store long-term ends up being larger than the old generation size in the JVM and this triggers Full GC repeatedly.
> I did some research:
> * In Spark 1.6, spark.memory.fraction is the upper limit on cache size. It defaults to 0.75.
> * In Spark 1.5, spark.storage.memoryFraction is the upper limit in cache size. It defaults to 0.6 and...
> * http://spark.apache.org/docs/1.5.2/configuration.html even says that it shouldn't be bigger than the size of the old generation.
> * On the other hand, OpenJDK's default NewRatio is 2, which means an old generation size of 66%. Hence the default value in Spark 1.6 contradicts this advice.
> http://spark.apache.org/docs/1.6.1/tuning.html recommends that if the old generation is running close to full, then setting spark.memory.storageFraction to a lower value should help. I have tried with spark.memory.storageFraction=0.1, but it still doesn't fix the issue. This is not a surprise: http://spark.apache.org/docs/1.6.1/configuration.html explains that storageFraction is not an upper-limit but a lower limit-like thing on the size of Spark's cache. The real upper limit is spark.memory.fraction.
> To sum up my questions/issues:
> * At least http://spark.apache.org/docs/1.6.1/tuning.html should be fixed. Maybe the old generation size should also be mentioned in configuration.html near spark.memory.fraction.
> * Is it a goal for Spark to support heavy caching with default parameters and without GC breakdown? If so, then better default values are needed.



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