Getting out of memory error while reading parquet file in spark submit job - scala

Question

[Stage 0:> (0 + 0) / 8]SLF4J: Failed to load class "org.slf4j.impl.StaticLoggerBinder".
SLF4J: Defaulting to no-operation (NOP) logger implementation
SLF4J: See http://www.slf4j.org/codes.html#StaticLoggerBinder for further details.
[Stage 1:=====================================================> (43 + 3) / 46]17/11/16 13:11:18 ERROR Executor: Exception in task 0.0 in stage 2.0 (TID 54)
java.lang.OutOfMemoryError: Java heap space
at java.util.Arrays.copyOf(Arrays.java:3236)
at java.io.ByteArrayOutputStream.grow(ByteArrayOutputStream.java:118)
at java.io.ByteArrayOutputStream.ensureCapacity(ByteArrayOutputStream.java:93)
at java.io.ByteArrayOutputStream.write(ByteArrayOutputStream.java:153)
at java.io.ObjectOutputStream$BlockDataOutputStream.drain(ObjectOutputStream.java:1877)
at java.io.ObjectOutputStream$BlockDataOutputStream.setBlockDataMode(ObjectOutputStream.java:1786)
at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1189)
at java.io.ObjectOutputStream.writeObject(ObjectOutputStream.java:348)
at org.apache.spark.serializer.JavaSerializationStream.writeObject(JavaSerializer.scala:44)
at org.apache.spark.serializer.JavaSerializerInstance.serialize(JavaSerializer.scala:84)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:240)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
17/11/16 13:11:18 ERROR SparkUncaughtExceptionHandler: Uncaught exception in thread Thread[Executor task launch worker-4,5,main]
java.lang.OutOfMemoryError: Java heap space
at java.util.Arrays.copyOf(Arrays.java:3236)
at java.io.ByteArrayOutputStream.grow(ByteArrayOutputStream.java:118)
at java.io.ByteArrayOutputStream.ensureCapacity(ByteArrayOutputStream.java:93)
at java.io.ByteArrayOutputStream.write(ByteArrayOutputStream.java:153)
at java.io.ObjectOutputStream$BlockDataOutputStream.drain(ObjectOutputStream.java:1877)
at java.io.ObjectOutputStream$BlockDataOutputStream.setBlockDataMode(ObjectOutputStream.java:1786)
at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1189)
at java.io.ObjectOutputStream.writeObject(ObjectOutputStream.java:348)
at org.apache.spark.serializer.JavaSerializationStream.writeObject(JavaSerializer.scala:44)
at org.apache.spark.serializer.JavaSerializerInstance.serialize(JavaSerializer.scala:84)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:240)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
17/11/16 13:11:18 WARN TaskSetManager: Lost task 0.0 in stage 2.0 (TID 54, localhost): java.lang.OutOfMemoryError: Java heap space
at java.util.Arrays.copyOf(Arrays.java:3236)
at java.io.ByteArrayOutputStream.grow(ByteArrayOutputStream.java:118)
at java.io.ByteArrayOutputStream.ensureCapacity(ByteArrayOutputStream.java:93)
at java.io.ByteArrayOutputStream.write(ByteArrayOutputStream.java:153)
at java.io.ObjectOutputStream$BlockDataOutputStream.drain(ObjectOutputStream.java:1877)
at java.io.ObjectOutputStream$BlockDataOutputStream.setBlockDataMode(ObjectOutputStream.java:1786)
at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1189)
at java.io.ObjectOutputStream.writeObject(ObjectOutputStream.java:348)
at org.apache.spark.serializer.JavaSerializationStream.writeObject(JavaSerializer.scala:44)
at org.apache.spark.serializer.JavaSerializerInstance.serialize(JavaSerializer.scala:84)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:240)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
This is my code-
val sqlContext = new SQLContext(sc)
//sqlContext.setConf("spark.sql.inMemoryColumnarStorage.compressed", "true")
log.setLevel(Level.INFO)
val config = HBaseConfiguration.create()
val newDataDF = sqlContext.read.parquet(file)
newDataDF.registerTempTable("newDataDF")
//sqlContext.cacheTable("newDataDF")
val result = sqlContext.sql("SELECT rec FROM newDataDF")
val rows = result.map(t => t(0)).collect()
//val rows = result.map(t => t.getAs[String]("rec"))
It throws out of memory at below line
//val rows = result.map(t => t(0)).collect()
Have tried all options of memory tuning and increasing executor/driver memory increase, but nothing seems to work.
Any advise would be greatly appreciated.

Answer 1

Well, by calling collect on your DataFrame, you tell Spark to gather ALL data onto the driver. For larger datasets this will indeed drown the driver and cause OOMs.
Spark is a framework for distributed computing intended to be used on large dataset that will not fit on a single machine. Only in very few cases do you ever want to call collect on a DataFrame and that is when you do debugging (on small datasets) or you know that you dataset has been reduced vastly in size due to some filtering or aggregation transformations.

Answer 2

you have to increase spark.driver.memory which default value is 1gb. you can check the driver and executor memory using --verbose command. For more information check this link and set the memory as per your requirement. https://spark.apache.org/docs/latest/configuration.html