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From Akhil Das <ak...@sigmoidanalytics.com>
Subject Re: Reading Binary files in Spark program
Date Tue, 19 May 2015 12:56:36 GMT
Try something like:

JavaPairRDD<IntWritable, Text> output = sc.newAPIHadoopFile(inputDir,
      org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat.class,
IntWritable.class,
      Text.class, new Job().getConfiguration());

With the type of input format that you require.

Thanks
Best Regards

On Tue, May 19, 2015 at 3:57 PM, Tapan Sharma <tapan.sharma@gmail.com>
wrote:

> Hi Team,
>
> I am new to Spark and learning.
> I am trying to read image files into spark job. This is how I am doing:
> Step 1. Created sequence files with FileName as Key and Binary image as
> value. i.e.  Text and BytesWritable.
> I am able to read these sequence files into Map Reduce programs.
>
> Step 2.
> I understand that Text and BytesWritable are Non Serializable therefore, I
> read the sequence file in Spark as following:
>
>     SparkConf sparkConf = new SparkConf().setAppName("JavaSequenceFile");
>     JavaSparkContext ctx = new JavaSparkContext(sparkConf);
>     JavaPairRDD<String, Byte> seqFiles = ctx.sequenceFile(args[0],
> String.class, Byte.class) ;
>     final List<Tuple2&lt;String, Byte>> tuple2s = seqFiles.collect();
>
>
>
> The moment I try to call collect() method to get the keys of sequence file,
> following exception has been thrown
>
> Can any one help me understanding why collect() method is failing? If I use
> toArray() on seqFiles object then also I am getting same call stack.
>
> Regards
> Tapan
>
>
>
> java.io.NotSerializableException: org.apache.hadoop.io.Text
>         at
> java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1183)
>         at
> java.io.ObjectOutputStream.defaultWriteFields(ObjectOutputStream.java:1547)
>         at
> java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1508)
>         at
>
> java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1431)
>         at
> java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1177)
>         at
> java.io.ObjectOutputStream.writeArray(ObjectOutputStream.java:1377)
>         at
> java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1173)
>         at
> java.io.ObjectOutputStream.writeObject(ObjectOutputStream.java:347)
>         at
>
> org.apache.spark.serializer.JavaSerializationStream.writeObject(JavaSerializer.scala:42)
>         at
>
> org.apache.spark.serializer.JavaSerializerInstance.serialize(JavaSerializer.scala:73)
>         at
> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:206)
>         at
>
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
>         at
>
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
>         at java.lang.Thread.run(Thread.java:745)
> 2015-05-19 15:15:03,705 ERROR [task-result-getter-0]
> scheduler.TaskSetManager (Logging.scala:logError(75)) - Task 0.0 in stage
> 0.0 (TID 0) had a not serializable result: org.apache.hadoop.io.Text; not
> retrying
> 2015-05-19 15:15:03,731 INFO  [task-result-getter-0]
> scheduler.TaskSchedulerImpl (Logging.scala:logInfo(59)) - Removed TaskSet
> 0.0, whose tasks have all completed, from pool
> 2015-05-19 15:15:03,739 INFO  [sparkDriver-akka.actor.default-dispatcher-2]
> scheduler.TaskSchedulerImpl (Logging.scala:logInfo(59)) - Cancelling stage
> 0
> 2015-05-19 15:15:03,747 INFO  [main] scheduler.DAGScheduler
> (Logging.scala:logInfo(59)) - Job 0 failed: collect at
> JavaSequenceFile.java:44, took 4.421397 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.Text
>         at
> org.apache.spark.scheduler.DAGScheduler.org
> $apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1214)
>         at
>
> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1203)
>         at
>
> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1202)
>         at
>
> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
>         at
> scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
>         at
> org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1202)
>         at
>
> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:696)
>         at
>
> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:696)
>         at scala.Option.foreach(Option.scala:236)
>         at
>
> org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:696)
>         at
>
> org.apache.spark.scheduler.DAGSchedulerEventProcessActor$$anonfun$receive$2.applyOrElse(DAGScheduler.scala:1420)
>         at akka.actor.ActorCell.receiveMessage(ActorCell.scala:498)
>         at akka.actor.ActorCell.invoke(ActorCell.scala:456)
>         at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:237)
>         at akka.dispatch.Mailbox.run(Mailbox.scala:219)
>         at
>
> akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(AbstractDispatcher.scala:386)
>         at
> scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260)
>         at
>
> scala.concurrent.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339)
>         at
> scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979)
>         at
>
> scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107)
>
>
>
>
>
>
> --
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