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https://issues.apache.org/jira/browse/SPARK-5297?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14281899#comment-14281899
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Sean Owen commented on SPARK-5297:
----------------------------------
I don't see that this has anything to do with custom key / values?
What it suggests is that type information got lost along the way from Java to
Scala. This fails: {{val inputFormat = inputFormatClass.newInstance}}.
I won't claim to understand this enough to be sure, but I don't see how the
type info gets into the underlying call to {{StreamingContext.fileStream}}.
{{JavaStreamingContext}} says:
{code}
def fileStream[K, V, F <: NewInputFormat[K, V]](
directory: String): JavaPairInputDStream[K, V] = {
implicit val cmk: ClassTag[K] =
implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[K]]
implicit val cmv: ClassTag[V] =
implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[V]]
implicit val cmf: ClassTag[F] =
implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[F]]
ssc.fileStream[K, V, F](directory)
}
{code}
Obviously in {{newAPIHadoopFile}} the type information is carried explicitly
there.
Is this something you can weigh in on [~tdas]?
> 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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