I have a spark job that (runs in spark 1.3.1) has to iterate over several keys (about 42) and process the job. Here is the structure of the program
- Get the key from a map
- Fetch data from hive (hadoop-yarn underneath) that is matching the key as a data frame
- Process data
- Write results to hive
When I run this for one key, everything works fine. When I run with 42 keys, I am getting an out of memory exception around 12th iteration. Is there a way I can clean the memory in between each iteration? Help appreciated.
Here is the high level code that I am working with.
public abstract class SparkRunnable {
public static SparkContext sc = null;
public static JavaSparkContext jsc = null;
public static HiveContext hiveContext = null;
public static SQLContext sqlContext = null;
protected SparkRunnableModel(String appName){
//get the system properties to setup the model
// Getting a java spark context object by using the constants
SparkConf conf = new SparkConf().setAppName(appName);
sc = new SparkContext(conf);
jsc = new JavaSparkContext(sc);
// Creating a hive context object connection by using java spark
hiveContext = new org.apache.spark.sql.hive.HiveContext(sc);
// sql context
sqlContext = new SQLContext(sc);
}
public abstract void processModel(Properties properties) throws Exception;
}
class ModelRunnerMain(model: String) extends SparkRunnableModel(model: String) with Serializable {
override def processModel(properties: Properties) = {
val dataLoader = DataLoader.getDataLoader(properties)
//loads keys data frame from a keys table in hive and converts that to a list
val keysList = dataLoader.loadSeriesData()
for (key <- keysList) {
runModelForKey(key, dataLoader)
}
}
def runModelForKey(key: String, dataLoader: DataLoader) = {
//loads data frame from a table(~50 col X 800 rows) using "select * from table where key='<key>'"
val keyDataFrame = dataLoader.loadKeyData()
// filter this data frame into two data frames
...
// join them to transpose
...
// convert the data frame into an RDD
...
// run map on the RDD to add bunch of new columns
...
}
}
My data frame size is under a Meg. But I create several data frames from this by selecting and joining etc. I assume all these get garbage collected once the iteration is done.
Here is configuration I am running with.
- spark.eventLog.enabled:true spark.broadcast.port:7086
- spark.driver.memory:12g spark.shuffle.spill:false
- spark.serializer:org.apache.spark.serializer.KryoSerializer
- spark.storage.memoryFraction:0.7 spark.executor.cores:8
- spark.io.compression.codec:lzf spark.shuffle.consolidateFiles:true
- spark.shuffle.service.enabled:true spark.master:yarn-client
- spark.executor.instances:8 spark.shuffle.service.port:7337
- spark.rdd.compress:true spark.executor.memory:48g
- spark.executor.id: spark.sql.shuffle.partitions:700
- spark.cores.max:56
Here is the exception I am getting.
Exception in thread "dag-scheduler-event-loop" java.lang.OutOfMemoryError: Java heap space
at org.apache.spark.util.io.ByteArrayChunkOutputStream.allocateNewChunkIfNeeded(ByteArrayChunkOutputStream.scala:66)
at org.apache.spark.util.io.ByteArrayChunkOutputStream.write(ByteArrayChunkOutputStream.scala:55)
at com.ning.compress.lzf.ChunkEncoder.encodeAndWriteChunk(ChunkEncoder.java:264)
at com.ning.compress.lzf.LZFOutputStream.writeCompressedBlock(LZFOutputStream.java:266)
at com.ning.compress.lzf.LZFOutputStream.write(LZFOutputStream.java:124)
at com.esotericsoftware.kryo.io.Output.flush(Output.java:155)
at com.esotericsoftware.kryo.io.Output.require(Output.java:135)
at com.esotericsoftware.kryo.io.Output.writeBytes(Output.java:220)
at com.esotericsoftware.kryo.io.Output.writeBytes(Output.java:206)
at com.esotericsoftware.kryo.serializers.DefaultArraySerializers$ByteArraySerializer.write(DefaultArraySerializers.java:29)
at com.esotericsoftware.kryo.serializers.DefaultArraySerializers$ByteArraySerializer.write(DefaultArraySerializers.java:18)
at com.esotericsoftware.kryo.Kryo.writeClassAndObject(Kryo.java:568)
at org.apache.spark.serializer.KryoSerializationStream.writeObject(KryoSerializer.scala:124)
at org.apache.spark.broadcast.TorrentBroadcast$.blockifyObject(TorrentBroadcast.scala:202)
at org.apache.spark.broadcast.TorrentBroadcast.writeBlocks(TorrentBroadcast.scala:101)
at org.apache.spark.broadcast.TorrentBroadcast.<init>(TorrentBroadcast.scala:84)
at org.apache.spark.broadcast.TorrentBroadcastFactory.newBroadcast(TorrentBroadcastFactory.scala:34)
at org.apache.spark.broadcast.TorrentBroadcastFactory.newBroadcast(TorrentBroadcastFactory.scala:29)
at org.apache.spark.broadcast.BroadcastManager.newBroadcast(BroadcastManager.scala:62)
at org.apache.spark.SparkContext.broadcast(SparkContext.scala:1051)
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$submitMissingTasks(DAGScheduler.scala:839)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskCompletion$15$$anonfun$apply$1.apply$mcVI$sp(DAGScheduler.scala:1042)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskCompletion$15$$anonfun$apply$1.apply(DAGScheduler.scala:1039)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskCompletion$15$$anonfun$apply$1.apply(DAGScheduler.scala:1039)
at scala.Option.foreach(Option.scala:236)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskCompletion$15.apply(DAGScheduler.scala:1039)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskCompletion$15.apply(DAGScheduler.scala:1038)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
at org.apache.spark.scheduler.DAGScheduler.handleTaskCompletion(DAGScheduler.scala:1038)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1390)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1354)
