hadoop writables NotSerializableException with Apache Spark API

Question

Spark Java application throws NotSerializableException on hadoop writables.

public final class myAPP {
  public static void main(String[] args) throws Exception {    
    if (args.length < 1) {
      System.err.println("Usage: myAPP <file>");
      System.exit(1);
    }
    SparkConf sparkConf = new SparkConf().setAppName("myAPP").setMaster("local");
    JavaSparkContext ctx = new JavaSparkContext(sparkConf);
    Configuration conf = new Configuration();
    JavaPairRDD<LongWritable,Text> lines = ctx.newAPIHadoopFile(args[0], TextInputFormat.class, LongWritable.class, Text.class, conf);
    System.out.println(    lines.collect().toString());
    ctx.stop();
  }

.

java.io.NotSerializableException: org.apache.hadoop.io.LongWritable
Serialization stack:
    - object not serializable (class: org.apache.hadoop.io.LongWritable, value: 15227295)
    - field (class: scala.Tuple2, name: _1, type: class java.lang.Object)
    - object (class scala.Tuple2, (15227295,))
    - element of array (index: 0)
    - array (class [Lscala.Tuple2;, size 1153163)
    at org.apache.spark.serializer.SerializationDebugger$.improveException(SerializationDebugger.scala:38)
    at org.apache.spark.serializer.JavaSerializationStream.writeObject(JavaSerializer.scala:47)
    at org.apache.spark.serializer.JavaSerializerInstance.serialize(JavaSerializer.scala:80)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(Unknown Source)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(Unknown Source)
    at java.lang.Thread.run(Unknown Source)
15/04/26 16:05:05 ERROR TaskSetManager: Task 0.0 in stage 0.0 (TID 0) had a not serializable result: org.apache.hadoop.io.LongWritable
Serialization stack:
    - object not serializable (class: org.apache.hadoop.io.LongWritable, value: 15227295)
    - field (class: scala.Tuple2, name: _1, type: class java.lang.Object)
    - object (class scala.Tuple2, (15227295,))
    - element of array (index: 0)
    - array (class [Lscala.Tuple2;, size 1153163); not retrying
15/04/26 16:05:05 INFO TaskSchedulerImpl: Removed TaskSet 0.0, whose tasks have all completed, from pool 
15/04/26 16:05:05 INFO TaskSchedulerImpl: Cancelling stage 0
15/04/26 16:05:05 INFO DAGScheduler: Job 0 failed: collect at Parser2.java:60, took 0.460181 s
Exception in thread "main" org.apache.spark.SparkException: Job aborted due to stage failure: Task 0.0 in stage 0.0 (TID 0) had a not serializable result: org.apache.hadoop.io.LongWritable

In Spark Scala program I register hadoop writables as below and it works fine.

sparkConf.registerKryoClasses(Array(classOf[org.apache.hadoop.io.LongWritable], classOf[org.apache.hadoop.io.Text]))

Apparently this approach doesn't work with Apache Spark API

sparkConf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer");
sparkConf.set("spark.kryo.registrator", LongWritable.class.getName());

.

Exception in thread "main" org.apache.spark.SparkException: Failed to register classes with Kryo
    at org.apache.spark.serializer.KryoSerializer.newKryo(KryoSerializer.scala:101)
    at org.apache.spark.serializer.KryoSerializerInstance.<init>(KryoSerializer.scala:153)
    at org.apache.spark.serializer.KryoSerializer.newInstance(KryoSerializer.scala:115)
    at org.apache.spark.broadcast.TorrentBroadcast$.blockifyObject(TorrentBroadcast.scala:200)
    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.rdd.NewHadoopRDD.<init>(NewHadoopRDD.scala:77)
    at org.apache.spark.SparkContext.newAPIHadoopFile(SparkContext.scala:848)
    at org.apache.spark.api.java.JavaSparkContext.newAPIHadoopFile(JavaSparkContext.scala:488)
    at com.nsn.PMParser.Parser2.main(Parser2.java:56)
Caused by: java.lang.ClassCastException: org.apache.hadoop.io.LongWritable cannot be cast to org.apache.spark.serializer.KryoRegistrator
    at org.apache.spark.serializer.KryoSerializer$$anonfun$newKryo$3.apply(KryoSerializer.scala:97)
    at org.apache.spark.serializer.KryoSerializer$$anonfun$newKryo$3.apply(KryoSerializer.scala:97)
    at scala.Option.map(Option.scala:145)
    at org.apache.spark.serializer.KryoSerializer.newKryo(KryoSerializer.scala:97)
    ... 13 more

hadoop writables NotSerializableException with Apache Spark Java API?

Sudarshan Thitte · Accepted Answer · 2015-07-01 23:50:48Z

8

As of Spark v1.4.0, you can use this Java API to register classes to be serialized using Kryo: https://spark.apache.org/docs/latest/api/java/org/apache/spark/SparkConf.html#registerKryoClasses(java.lang.Class[]) , by passing in an array of Class objects, each of which can be obtained using http://docs.oracle.com/javase/7/docs/api/java/lang/Class.html#forName(java.lang.String)

such as:

new SparkConf().registerKryoClasses(new Class<?>[]{
    Class.forName("org.apache.hadoop.io.LongWritable"),
    Class.forName("org.apache.hadoop.io.Text")
});

Hope this helps.

answered Jul 1, 2015 at 23:50

Sudarshan Thitte

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1 Comment

Data Man Over a year ago

If you don't mind an extra import, you can replace Class.forName("org.apache.hadoop.io.LongWritable") with LongWritable.class.

banjara · Accepted Answer · 2015-04-26 15:45:08Z

1

use

sparkConf.set("spark.kryo.classesToRegister", "org.apache.hadoop.io.LongWritable,org.apache.hadoop.io.Text")

or you can simply use

ctx.textFile(args[0]);

to load RDD

edited Apr 26, 2015 at 15:45

answered Apr 26, 2015 at 15:32

banjara

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2 Comments

Vijay Innamuri Over a year ago

It looks above code is for Spark Scala API but the issue is with Spark Java API...

banjara Over a year ago

@VijayInnamuri yes in java use sparkConf.set("spark.kryo.classesToRegister", "org.apache.hadoop.io.LongWritable,org.apache.hadoop.io.Text"); spark.kryo.registrator is used to define kryo registrator class which extends org.apache.spark.serializer.KryoRegistrator and overrides registerClasses method. in registerClasses method you define classes to register like kryo.register(LongWritable.class);

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