How long does RDD remain in memory? - apache-spark

Question

Considering memory being limited, I had a feeling that spark automatically removes RDD from each node. I'd like to know is this time configurable? How does spark decide when to evict an RDD from memory
Note: I'm not talking about rdd.cache()

Answer 1

I'd like to know is this time configurable? How does spark decide when
to evict an RDD from memory
An RDD is an object just like any other. If you don't persist/cache it, it will act as any other object under a managed language would and be collected once there are no alive root objects pointing to it.
The "how" part, as #Jacek points out is the responsibility of an object called ContextCleaner. Mainly, if you want the details, this is what the cleaning method looks like:
private def keepCleaning(): Unit = Utils.tryOrStopSparkContext(sc) {
while (!stopped) {
try {
val reference = Option(referenceQueue.remove(ContextCleaner.REF_QUEUE_POLL_TIMEOUT))
.map(_.asInstanceOf[CleanupTaskWeakReference])
// Synchronize here to avoid being interrupted on stop()
synchronized {
reference.foreach { ref =>
logDebug("Got cleaning task " + ref.task)
referenceBuffer.remove(ref)
ref.task match {
case CleanRDD(rddId) =>
doCleanupRDD(rddId, blocking = blockOnCleanupTasks)
case CleanShuffle(shuffleId) =>
doCleanupShuffle(shuffleId, blocking = blockOnShuffleCleanupTasks)
case CleanBroadcast(broadcastId) =>
doCleanupBroadcast(broadcastId, blocking = blockOnCleanupTasks)
case CleanAccum(accId) =>
doCleanupAccum(accId, blocking = blockOnCleanupTasks)
case CleanCheckpoint(rddId) =>
doCleanCheckpoint(rddId)
}
}
}
} catch {
case ie: InterruptedException if stopped => // ignore
case e: Exception => logError("Error in cleaning thread", e)
}
}
}
If you want to learn more, I suggest browsing Sparks source or even better, reading #Jacek book called "Mastering Apache Spark" (This points to an explanation regarding ContextCleaner)

Answer 2

In general, that's how Yuval Itzchakov wrote "just like any other object", but...(there's always "but", isn't it?)
In Spark, it's not that obvious since we have shuffle blocks (among the other blocks managed by Spark). They are managed by BlockManagers running on executors. They somehow will have to be notified when an object on the driver gets evicted from memory, right?
That's where ContextCleaner comes to stage. It's Spark Application's Garbage Collector that is responsible for application-wide cleanup of shuffles, RDDs, broadcasts, accumulators and checkpointed RDDs that is aimed at reducing the memory requirements of long-running data-heavy Spark applications.
ContextCleaner runs on the driver. It is created and immediately started when SparkContext starts (and spark.cleaner.referenceTracking Spark property is enabled, which it is by default). It is stopped when SparkContext is stopped.
You can see it working by doing the dump of all the threads in a Spark application using jconsole or jstack. ContextCleaner uses a daemon Spark Context Cleaner thread that cleans RDD, shuffle, and broadcast states.
You can also see its work by enabling INFO or DEBUG logging levels for org.apache.spark.ContextCleaner logger. Just add the following line to conf/log4j.properties:
log4j.logger.org.apache.spark.ContextCleaner=DEBUG

Answer 3

Measuring the Impact of GC
The first step in GC tuning is to collect statistics on how frequently garbage collection occurs and the amount of time spent GC. This can be done by adding -verbose:gc -XX:+PrintGCDetails -XX:+PrintGCTimeStamps to the Java options. (See the configuration guide for info on passing Java options to Spark jobs.) Next time your Spark job is run, you will see messages printed in the worker’s logs each time a garbage collection occurs. Note these logs will be on your cluster’s worker nodes (in the stdout files in their work directories), not on your driver program.
Advanced GC Tuning
To further tune garbage collection, we first need to understand some basic information about memory management in the JVM:
Java Heap space is divided in to two regions Young and Old. The Young generation is meant to hold short-lived objects while the Old generation is intended for objects with longer lifetimes.
The Young generation is further divided into three regions [Eden, Survivor1, Survivor2].
A simplified description of the garbage collection procedure: When Eden is full, a minor GC is run on Eden and objects that are alive from Eden and Survivor1 are copied to Survivor2. The Survivor regions are swapped. If an object is old enough or Survivor2 is full, it is moved to Old. Finally when Old is close to full, a full GC is invoked.

Answer 4

According to the Resilient Distributed Data-set paper -
Our worker nodes cache RDD partitions in memory as
Java objects. We use an LRU replacement policy at the
level of RDDs (i.e., we do not evict partitions from an
RDD in order to load other partitions from the same
RDD) because most operations are scans. We found this
simple policy to work well in all our user applications so
far. Programmers that want more control can also set a
retention priority for each RDD as an argument to cache.