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. > > --------------------------------------------------------------------- > To unsubscribe, e-mail: user-unsubscr...@spark.apache.org > For additional commands, e-mail: user-h...@spark.apache.org > >