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https://issues.apache.org/jira/browse/SPARK-15796?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Sean Owen updated SPARK-15796:
------------------------------
Priority: Blocker (was: Minor)
Pardon marking this "Blocker", but I think this needs some attention before
2.0, if in fact the default memory settings for the new memory manager and JVM
ergonomics don't play well together. It's an easy resolution one way or the
other -- mostly a question of defaults and docs.
> 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
> Priority: Blocker
> 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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