Gabor Feher created SPARK-15796:
-----------------------------------
Summary: Spark 1.6 default memory settings can cause heavy GC when
caching
Key: SPARK-15796
URL: https://issues.apache.org/jira/browse/SPARK-15796
Project: Spark
Issue Type: Bug
Affects Versions: 1.6.1, 1.6.0
Reporter: Gabor Feher
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 the 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 shou;d 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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