I am not doing anything special.
*Here is the code :*
SparkConf sparkConf = new SparkConf().setAppName("JavaSequenceFile");
JavaSparkContext ctx = new JavaSparkContext(sparkConf);
JavaPairRDD<String, Byte> seqFiles = ctx.sequenceFile(args[0],
String.class, Byte.class) ;
// Following statements is giving exception
final List<Tuple2<String, Byte>> tuple2s = seqFiles.toArray();
// Or
final List<Tuple2<String, Byte>> tuple2s = seqFiles.collect();
*And this is how I have created a sequence file:*
http://stuartsierra.com/2008/04/24/a-million-little-files
Regards
Tapan
On Wed, May 20, 2015 at 12:42 PM, Akhil Das <ak...@sigmoidanalytics.com>
wrote:
> If you can share the complete code and a sample file, may be i can try to
> reproduce it on my end.
>
> Thanks
> Best Regards
>
> On Wed, May 20, 2015 at 7:00 AM, Tapan Sharma <tapan.sha...@gmail.com>
> wrote:
>
>> Problem is still there.
>> Exception is not coming at the time of reading.
>> Also the count of JavaPairRDD is as expected. It is when we are calling
>> collect() or toArray() methods, the exception is coming.
>> Something to do with Text class even though I haven't used it in the
>> program.
>>
>> Regards
>> Tapan
>>
>> On Tue, May 19, 2015 at 6:26 PM, Akhil Das <ak...@sigmoidanalytics.com>
>> wrote:
>>
>>> 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.sha...@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<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)
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>> --
>>>> View this message in context:
>>>> http://apache-spark-user-list.1001560.n3.nabble.com/Reading-Binary-files-in-Spark-program-tp22942.html
>>>> Sent from the Apache Spark User List mailing list archive at Nabble.com.
>>>>
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>>>>
>>>>
>>>
>>
>
