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From "Saisai Shao (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-5297) File Streams do not work with custom key/values
Date Mon, 19 Jan 2015 01:39:34 GMT

    [ https://issues.apache.org/jira/browse/SPARK-5297?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14282077#comment-14282077
] 

Saisai Shao commented on SPARK-5297:
------------------------------------

Hi [~sowen], I think this issue is the same as previously fixed SPARK-2103 issue, the problem
is that using 
{code}
implicit val cmk: ClassTag[K] = 
    implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[K]]
{code}
 to get the implicit class tag actually cannot the the class of right object for Java, I will
fix this.

> File Streams do not work with custom key/values
> -----------------------------------------------
>
>                 Key: SPARK-5297
>                 URL: https://issues.apache.org/jira/browse/SPARK-5297
>             Project: Spark
>          Issue Type: Bug
>          Components: Streaming
>    Affects Versions: 1.2.0
>            Reporter: Leonidas Fegaras
>            Priority: Minor
>             Fix For: 1.2.0
>
>
> The following code:
> {code}
> stream_context.<K,V,SequenceFileInputFormat<K,V>>fileStream(directory)
> .foreachRDD(new Function<JavaPairRDD<K,V>,Void>() {
>      public Void call ( JavaPairRDD<K,V> rdd ) throws Exception {
>          for ( Tuple2<K,V> x: rdd.collect() )
>              System.out.println("# "+x._1+" "+x._2);
>          return null;
>      }
>   });
> stream_context.start();
> stream_context.awaitTermination();
> {code}
> for custom (serializable) classes K and V compiles fine but gives an error
> when I drop a new hadoop sequence file in the directory:
> {quote}
> 15/01/17 09:13:59 ERROR scheduler.JobScheduler: Error generating jobs for time 1421507639000
ms
> java.lang.ClassCastException: java.lang.Object cannot be cast to org.apache.hadoop.mapreduce.InputFormat
> 	at org.apache.spark.rdd.NewHadoopRDD.getPartitions(NewHadoopRDD.scala:91)
> 	at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:205)
> 	at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:203)
> 	at scala.Option.getOrElse(Option.scala:120)
> 	at org.apache.spark.rdd.RDD.partitions(RDD.scala:203)
> 	at org.apache.spark.streaming.dstream.FileInputDStream$$anonfun$3.apply(FileInputDStream.scala:236)
> 	at org.apache.spark.streaming.dstream.FileInputDStream$$anonfun$3.apply(FileInputDStream.scala:234)
> 	at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
> 	at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
> 	at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
> 	at scala.collection.mutable.WrappedArray.foreach(WrappedArray.scala:34)
> 	at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
> 	at scala.collection.AbstractTraversable.map(Traversable.scala:105)
> 	at org.apache.spark.streaming.dstream.FileInputDStream.org$apache$spark$streaming$dstream$FileInputDStream$$filesToRDD(FileInputDStream.scala:234)
> 	at org.apache.spark.streaming.dstream.FileInputDStream.compute(FileInputDStream.scala:128)
> 	at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1.apply(DStream.scala:296)
> 	at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1.apply(DStream.scala:288)
> 	at scala.Option.orElse(Option.scala:257)
> {quote}
> The same classes K and V work fine for non-streaming Spark:
> {code}
> spark_context.newAPIHadoopFile(path,F.class,K.class,SequenceFileInputFormat.class,conf)
> {code}
> also streaming works fine for TextFileInputFormat.



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