---
layout: global
title: Tuning Spark
---
Because of the in-memory nature of most Spark computations, Spark programs can be bottlenecked
by any resource in the cluster: CPU, network bandwidth, or memory.
Most often, if the data fits in memory, the bottleneck is network bandwidth, but sometimes, you
also need to do some tuning, such as
[storing RDDs in serialized form]({{HOME_PATH}}/scala-programming-guide.html#rdd-persistence), to
make the memory usage smaller.
This guide will cover two main topics: data serialization, which is crucial for good network
performance, and memory tuning. We also sketch several smaller topics.
This document assumes that you have familiarity with the Spark API and have already read the [Scala]({{HOME_PATH}}/scala-programming-guide.html) or [Java]({{HOME_PATH}}/java-programming-guide.html) programming guides. After reading this guide, do not hesitate to reach out to the [Spark mailing list](http://groups.google.com/group/spark-users) with performance related concerns.
# The Spark Storage Model
Spark's key abstraction is a distributed dataset, or RDD. RDD's consist of partitions. RDD partitions are stored either in memory or on disk, with replication or without replication, depending on the chosen [persistence options]({{HOME_PATH}}/scala-programming-guide.html#rdd-persistence). When RDD's are stored in memory, they can be stored as deserialized Java objects, or in a serialized form, again depending on the persistence option chosen. When RDD's are transferred over the network, or spilled to disk, they are always serialized. Spark can use different serializers, configurable with the `spark.serializer` option.
# Serialization Options
Serialization plays an important role in the performance of Spark applications, especially those which are network-bound. The format of data sent across
the network -- formats that are slow to serialize objects into, or consume a large number of
bytes, will greatly slow down the computation.
Often, this will be the first thing you should tune to optimize a Spark application.
Spark aims to strike a balance between convenience (allowing you to work with any Java type
in your operations) and performance. It provides two serialization libraries:
* [Java serialization](http://docs.oracle.com/javase/6/docs/api/java/io/Serializable.html):
By default, Spark serializes objects using Java's `ObjectOutputStream` framework, and can work
with any class you create that implements
[`java.io.Serializable`](http://docs.oracle.com/javase/6/docs/api/java/io/Serializable.html).
You can also control the performance of your serialization more closely by extending
[`java.io.Externalizable`](http://docs.oracle.com/javase/6/docs/api/java/io/Externalizable.html).
Java serialization is flexible but often quite slow, and leads to large
serialized formats for many classes.
* [Kryo serialization](http://code.google.com/p/kryo/wiki/V1Documentation): Spark can also use
the Kryo library (currently just version 1) to serialize objects more quickly. Kryo is significantly
faster and more compact than Java serialization (often as much as 10x), but does not support all
`Serializable` types and requires you to *register* the classes you'll use in the program in advance
for best performance.
You can switch to using Kryo by calling `System.setProperty("spark.serializer", "spark.KryoSerializer")`
*before* creating your SparkContext. The only reason it is not the default is because of the custom
registration requirement, but we recommend trying it in any network-intensive application.
Finally, to register your classes with Kryo, create a public class that extends
[`spark.KryoRegistrator`]({{HOME_PATH}}api/core/index.html#spark.KryoRegistrator) and set the
`spark.kryo.registrator` system property to point to it, as follows:
{% highlight scala %}
class MyRegistrator extends KryoRegistrator {
override def registerClasses(kryo: Kryo) {
kryo.register(classOf[MyClass1])
kryo.register(classOf[MyClass2])
}
}
// Make sure to set these properties *before* creating a SparkContext!
System.setProperty("spark.serializer", "spark.KryoSerializer")
System.setProperty("spark.kryo.registrator", "mypackage.MyRegistrator")
val sc = new SparkContext(...)
{% endhighlight %}
The [Kryo documentation](http://code.google.com/p/kryo/wiki/V1Documentation) describes more advanced
registration options, such as adding custom serialization code.
If your objects are large, you may also need to increase the `spark.kryoserializer.buffer.mb`
system property. The default is 32, but this value needs to be large enough to hold the *largest*
object you will serialize.
Finally, if you don't register your classes, Kryo will still work, but it will have to store the
full class name with each object, which is wasteful.
# Memory Tuning
There are three considerations in tuning memory usage: the *amount* of memory used by your objects
(you may want your entire dataset to fit in memory), the *cost* of accessing those objects, and the
overhead of *garbage collection* (if you have high turnover in terms of objects).
## Determining memory consumption
The best way to size the amount of memory consumption your dataset will require is to create an RDD, put it into cache, and look at the master logs. The logs will tell you how much memory each partition is consuming, which you can aggregate to get the total size of the RDD. Depending on the object complexity in your dataset, and whether you are storing serialized data, memory overhead can range from 1X (e.g. no overhead vs on-disk storage) to 5X. This guide covers ways to keep memory overhead low, in cases where memory is a contended resource.
## Efficient Data Structures
By default, Java objects are fast to access, but can easily consume a factor of 2-5x more space
than the "raw" data inside their fields. This is due to several reasons:
* Each distinct Java object has an "object header", which is about 16 bytes and contains information
such as a pointer to its class. For an object with very little data in it (say one `Int` field), this
can be bigger than the data.
* Java Strings have about 40 bytes of overhead over the raw string data (since they store it in an
array of `Char`s and keep extra data such as the length), and store each character
as *two* bytes due to Unicode. Thus a 10-character string can easily consume 60 bytes.
* Common collection classes, such as `HashMap` and `LinkedList`, use linked data structures, where
there is a "wrapper" object for each entry (e.g. `Map.Entry`). This object not only has a header,
but also pointers (typically 8 bytes each) to the next object in the list.
* Collections of primitive types often store them as "boxed" objects such as `java.lang.Integer`.
There are several ways to reduce this cost and still make Java objects efficient to work with:
1. Design your data structures to prefer arrays of objects, and primitive types, instead of the
standard Java or Scala collection classes (e.g. `HashMap`). The [fastutil](http://fastutil.di.unimi.it)
library provides convenient collection classes for primitive types that are compatible with the
Java standard library.
2. Avoid nested structures with a lot of small objects and pointers when possible.
3. If you have less than 32 GB of RAM, set the JVM flag `-XX:+UseCompressedOops` to make pointers be
four bytes instead of eight. Also, on Java 7 or later, try `-XX:+UseCompressedStrings` to store
ASCII strings as just 8 bits per character. You can add these options in
[`spark-env.sh`]({{HOME_PATH}}configuration.html#environment-variables-in-spark-envsh).
When your objects are still too large to efficiently store despite this tuning, a much simpler way
to reduce memory usage is to store them in *serialized* form, using the serialized StorageLevels in
the [RDD persistence API]({{HOME_PATH}}scala-programming-guide#rdd-persistence).
Spark will then store each RDD partition as one large byte array.
The only downside of storing data in serialized form is slower access times, due to having to
deserialize each object on the fly.
We highly recommend [using Kryo](#data-serialization) if you want to cache data in serialized form, as
it leads to much smaller sizes than Java serialization (and certainly than raw Java objects).
Finally, JVM garbage collection can be a problem when you have large "churn" in terms of the RDDs
stored by your program. (It is generally not a problem in programs that just read an RDD once
and then run many operations on it.) When Java needs to evict old objects to make room for new ones, it will
need to trace through all your Java objects and find the unused ones. The main point to remember here is
that *the cost of garbage collection is proportional to the number of Java objects*, so using data
structures with fewer objects (e.g. an array of `Int`s instead of a `LinkedList`) greatly reduces
this cost. An even better method is to persist objects in serialized form, as described above: now
there will be only *one* object (a byte array) per RDD partition. Before trying other advanced
techniques, the first thing to try if GC is a problem is to use serialized caching.
## Cache Size Tuning
One of the important configuration parameters passed to Spark is the amount of memory that should be used for
caching RDDs. By default, Spark uses 66% of the configured memory (`SPARK_MEM`) to cache RDDs. This means that
around 33% of memory is available for any objects created during task execution.
In case your tasks slow down and you find that your JVM is using almost all of its allocated memory, lowering
this value will help reducing the memory consumption. To change this to say 50%, you can call
`System.setProperty("spark.storage.memoryFraction", "0.5")`. Combined with the use of serialized caching,
using a smaller cache should be sufficient to mitigate most of the garbage collection problems.
In case you are interested in further tuning the Java GC, continue reading below.
## GC Tuning
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
`SPARK_JAVA_OPTS` environment variable. Next time your Spark job is run, you will see messages printed on the
console whenever a JVM garbage collection takes place. Note that garabage collections that occur at the executor can be
found in the executor logs and not on the `spark-shell`.
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.
The goal of GC-tuning in Spark is to ensure that only long-lived RDDs are stored in the Old generation and that
the Young generation is sufficiently sized to store short-lived objects. This will help avoid full GCs to collect
temporary objects created during task execution. Some steps which may be useful are:
* Check if there are too many garbage collections by collecting GC stats. If a full GC is invoked multiple times for
before a task completes, it means that there isn't enough memory available for executing tasks.
* In the GC stats that are printed, if the OldGen is close to being full, reduce the amount of memory used for caching.
This can be done using the `spark.storage.memoryFraction` property. It is better to cache fewer objects than to slow
down task execution !
* If there are too many minor collections but not too many major GCs, allocating more memory for Eden would help. You
can approximate the size of the Eden to be an over-estimate of how much memory each task will need. If the size of Eden
is determined to be `E`, then you can set the size of the Young generation using the option `-Xmn=4/3*E`. (The scaling
up by 4/3 is to account for space used by survivor regions as well)
* As an example, if your task is reading data from HDFS, the amount of memory used by the task can be estimated using
the size of the data block read from HDFS. Note that the size of a decompressed block is often 2 or 3 times the
size of the block. So if we wish to have 3 or 4 tasks worth of working space, and the HDFS block size is 64 MB,
we can estimate size of Eden to be `4*3*64MB`.
* Monitor how the frequency and time taken by garbage collection changes with the new settings.
Our experience suggests that the effect of GC tuning depends on your application and the amount of memory available.
There are [many more tuning options](http://www.oracle.com/technetwork/java/javase/gc-tuning-6-140523.html) described online,
but at a high level, managing how frequently full GC takes place can help in reducing the overhead.
# Other Considerations
## Level of Parallelism
Clusters will not be fully utilized unless you set the level of parallelism for each operation high
enough. Spark automatically sets the number of "map" tasks to run on each file according to its size
(though you can control it through optional parameters to `SparkContext.textFile`, etc), but for
distributed "reduce" operations, such as `groupByKey` and `reduceByKey`, it uses a default value of 8.
You can pass the level of parallelism as a second argument (see the
[`spark.PairRDDFunctions`]({{HOME_PATH}}api/core/index.html#spark.PairRDDFunctions) documentation),
or set the system property `spark.default.parallelism` to change the default.
In general, we recommend 2-3 tasks per CPU core in your cluster.
## Memory Usage of Reduce Tasks
Sometimes, you will get an OutOfMemoryError not because your RDDs don't fit in memory, but because the
working set of one of your tasks, such as one of the reduce tasks in `groupByKey`, was too large.
Spark's shuffle operations (`sortByKey`, `groupByKey`, `reduceByKey`, `join`, etc) build a hash table
within each task to perform the grouping, which can often be large. The simplest fix here is to
*increase the level of parallelism*, so that each task's input set is smaller. Spark can efficiently
support tasks as short as 200 ms, because it reuses one worker JVMs across all tasks and it has
a low task launching cost, so you can safely increase the level of parallelism to more than the
number of cores in your clusters.
## Broadcasting Large Variables
Using the [broadcast functionality]({{HOME_PATH}}scala-programming-guide#broadcast-variables)
available in `SparkContext` can greatly reduce the size of each serialized task, and the cost
of launching a job over a cluster. If your tasks use any large object from the driver program
inside of them (e.g. a static lookup table), consider turning it into a broadcast variable.
Spark prints the serialized size of each task on the master, so you can look at that to
decide whether your tasks are too large; in general tasks larger than about 20 KB are probably
worth optimizing.
# Summary
This has been a quick guide to point out the main concerns you should know about when tuning a
Spark application -- most importantly, data serialization and memory tuning. For most programs,
switching to Kryo serialization and persisting data in serialized form will solve most common
performance issues. Feel free to ask on the
[Spark mailing list](http://groups.google.com/group/spark-users) about other tuning best practices.
