Spark java.lang.OutOfMemoryError: Java heap space

Question

My cluster: 1 master, 11 slaves, each node has 6 GB memory.

My settings:

spark.executor.memory=4g, Dspark.akka.frameSize=512

Here is the problem:

First, I read some data (2.19 GB) from HDFS to RDD:

val imageBundleRDD = sc.newAPIHadoopFile(...)

Second, do something on this RDD:

val res = imageBundleRDD.map(data => {
                               val desPoints = threeDReconstruction(data._2, bg)
                                 (data._1, desPoints)
                             })

Last, output to HDFS:

res.saveAsNewAPIHadoopFile(...)

When I run my program it shows:

.....
14/01/15 21:42:27 INFO cluster.ClusterTaskSetManager: Starting task 1.0:24 as TID 33 on executor 9: Salve7.Hadoop (NODE_LOCAL)
14/01/15 21:42:27 INFO cluster.ClusterTaskSetManager: Serialized task 1.0:24 as 30618515 bytes in 210 ms
14/01/15 21:42:27 INFO cluster.ClusterTaskSetManager: Starting task 1.0:36 as TID 34 on executor 2: Salve11.Hadoop (NODE_LOCAL)
14/01/15 21:42:28 INFO cluster.ClusterTaskSetManager: Serialized task 1.0:36 as 30618515 bytes in 449 ms
14/01/15 21:42:28 INFO cluster.ClusterTaskSetManager: Starting task 1.0:32 as TID 35 on executor 7: Salve4.Hadoop (NODE_LOCAL)
Uncaught error from thread [spark-akka.actor.default-dispatcher-3] shutting down JVM since 'akka.jvm-exit-on-fatal-error' is enabled for ActorSystem[spark]
java.lang.OutOfMemoryError: Java heap space

There are too many tasks?

PS: Every thing is ok when the input data is about 225 MB.

How can I solve this problem?

Answer 1

The location to set the memory heap size (at least in spark-1.0.0) is in conf/spark-env. The relevant variables are SPARK_EXECUTOR_MEMORY & SPARK_DRIVER_MEMORY. More docs are in the deployment guide

Also, don't forget to copy the configuration file to all the slave nodes.

Answer 2

You should increase the driver memory. In your $SPARK_HOME/conf folder you should find the file spark-defaults.conf, edit and set the spark.driver.memory 4000m depending on the memory on your master, I think. This is what fixed the issue for me and everything runs smoothly

Answer 3

Have a look at the start up scripts a Java heap size is set there, it looks like you're not setting this before running Spark worker.

# Set SPARK_MEM if it isn't already set since we also use it for this process
SPARK_MEM=${SPARK_MEM:-512m}
export SPARK_MEM

# Set JAVA_OPTS to be able to load native libraries and to set heap size
JAVA_OPTS="$OUR_JAVA_OPTS"
JAVA_OPTS="$JAVA_OPTS -Djava.library.path=$SPARK_LIBRARY_PATH"
JAVA_OPTS="$JAVA_OPTS -Xms$SPARK_MEM -Xmx$SPARK_MEM"

You can find the documentation to deploy scripts here.

Answer 4

You should configure offHeap memory settings as shown below:

`val spark = SparkSession
     .builder()
     .master("local[*]")
     .config("spark.executor.memory", "70g")
     .config("spark.driver.memory", "50g")
     .config("spark.memory.offHeap.enabled",true)
     .config("spark.memory.offHeap.size","16g")   
     .appName("sampleCodeForReference")
     .getOrCreate()`

Give the driver memory and executor memory as per your machines RAM availability. You can increase the offHeap size if you are still facing the OutofMemory issue.

Answer 5

I suffered from this issue a lot, we use dynamic resource allocation and I thought it will utilize my cluster resources to best fit the application.

But the truth is, the dynamic resource allocation doesn't set the driver memory and it keeps it to its default value which is 1g.

I have resolved it by setting spark.driver.memory to a number that suits my driver's memory (for 32gb ram I set it to 18gb)

you can set it using spark submit command as follows:

spark-submit --conf spark.driver.memory=18gb ....cont

Very important note, this property will not be taken into consideration if you set it from code, according to spark documentation:

Spark properties mainly can be divided into two kinds: one is related to deploy, like “spark.driver.memory”, “spark.executor.instances”, this kind of properties may not be affected when setting programmatically through SparkConf in runtime, or the behavior is depending on which cluster manager and deploy mode you choose, so it would be suggested to set through configuration file or spark-submit command line options; another is mainly related to Spark runtime control, like “spark.task.maxFailures”, this kind of properties can be set in either way.

Answer 6

Broadly speaking, spark Executor JVM memory can be divided into two parts. Spark memory and User memory. This is controlled by property spark.memory.fraction - the value is between 0 and 1. When working with images or doing memory intensive processing in spark applications, consider decreasing the spark.memory.fraction. This will make more memory available to your application work. Spark can spill, so it will still work with less memory share.

The second part of the problem is division of work. If possible, partition your data into smaller chunks. Smaller data possibly needs less memory. But if that is not possible, you are sacrifice compute for memory. Typically a single executor will be running multiple cores. Total memory of executors must be enough to handle memory requirements of all concurrent tasks. If increasing executor memory is not a option, you can decrease the cores per executor so that each task gets more memory to work with. Test with 1 core executors which have largest possible memory you can give and then keep increasing cores until you find the best core count.