Is that its not supported with Avro. Unlikely.
On Fri, Jun 26, 2015 at 8:01 AM, ÐΞ€ρ@Ҝ (๏̯͡๏) <deepuj...@gmail.com> wrote:
> My imports:
>
> import org.apache.avro.generic.GenericData
>
> import org.apache.avro.generic.GenericRecord
>
> import org.apache.avro.mapred.AvroKey
>
> import org.apache.avro.Schema
>
> import org.apache.hadoop.io.NullWritable
>
> import org.apache.avro.mapreduce.AvroKeyInputFormat
>
> import org.apache.hadoop.conf.Configuration
>
> import org.apache.hadoop.fs.FileSystem
>
> import org.apache.hadoop.fs.Path
>
> import org.apache.hadoop.io.Text
>
>
> def readGenericRecords(sc: SparkContext, inputDir: String, startDate:
> Date, endDate: Date) = {
>
> val path = getInputPaths(inputDir, startDate, endDate)
>
> val hadoopConfiguration = new Configuration(sc.hadoopConfiguration)
>
> hadoopConfiguration.set(
> "mapreduce.input.fileinputformat.split.maxsize", "67108864")
>
> sc.newAPIHadoopFile[AvroKey[GenericRecord], NullWritable,
> AvroKeyInputFormat[GenericRecord]](path + "/*.avro")
>
> }
>
> I need to read Avro datasets and am using strings instead of constant from
> InputFormat class.
>
>
> When i click on any hadoop dependency from eclipse, i see they point to
> hadoop 2.2.x jars.
>
>
>
> On Fri, Jun 26, 2015 at 7:44 AM, Silvio Fiorito <
> silvio.fior...@granturing.com> wrote:
>
>> Make sure you’re importing the right namespace for Hadoop v2.0. This
>> is what I tried:
>>
>> import org.apache.hadoop.io.{LongWritable, Text}
>> import org.apache.hadoop.mapreduce.lib.input.{FileInputFormat,
>> TextInputFormat}
>>
>> val hadoopConf = new org.apache.hadoop.conf.Configuration()
>> hadoopConf.setLong(FileInputFormat.SPLIT_MAXSIZE, 2048)
>>
>> val input = sc.newAPIHadoopFile(
>> "README.md",
>> classOf[TextInputFormat],
>> classOf[LongWritable],
>> classOf[Text],
>> hadoopConf).map(_._2.toString())
>>
>> println(input.partitions.size)
>>
>> input.
>> flatMap(_.split(" ")).
>> filter(_.length > 0).
>> map((_, 1)).
>> reduceByKey(_ + _).
>> coalesce(1).
>> sortBy(_._2, false).
>> take(10).
>> foreach(println)
>>
>>
>> From: "ÐΞ€ρ@Ҝ (๏̯͡๏)"
>> Date: Friday, June 26, 2015 at 10:18 AM
>> To: Silvio Fiorito
>> Cc: user
>> Subject: Re:
>>
>> All these throw compilation error at newAPIHadoopFile
>>
>> 1)
>>
>> val hadoopConfiguration = new Configuration()
>>
>> hadoopConfiguration.set(
>> "mapreduce.input.fileinputformat.split.maxsize", "67108864")
>>
>> sc.newAPIHadoopFile[AvroKey, NullWritable, AvroKeyInputFormat](path
>> + "/*.avro", classOf[AvroKey], classOf[NullWritable],
>> classOf[AvroKeyInputFormat], hadoopConfiguration)
>>
>> 2)
>>
>> val hadoopConfiguration = new Configuration()
>>
>> hadoopConfiguration.set(
>> "mapreduce.input.fileinputformat.split.maxsize", "67108864")
>>
>> sc.newAPIHadoopFile[AvroKey, NullWritable, AvroKeyInputFormat](path
>> + "/*.avro", classOf[AvroKey[GenericRecord]], classOf[NullWritable],
>> classOf[AvroKeyInputFormat[GenericRecord]],hadoopConfiguration)
>>
>> 3)
>>
>> val hadoopConfiguration = new Configuration()
>>
>> hadoopConfiguration.set(
>> "mapreduce.input.fileinputformat.split.maxsize", "67108864")
>>
>> sc.newAPIHadoopFile[AvroKey[GenericRecord], NullWritable,
>> AvroKeyInputFormat[GenericRecord]](path + "/*.avro",
>> classOf[AvroKey[GenericRecord]], classOf[NullWritable],
>> classOf[AvroKeyInputFormat[GenericRecord]], hadoopConfiguration)
>>
>> Error:
>>
>> [ERROR]
>> /Users/dvasthimal/ebay/projects/ep/ep-spark/src/main/scala/com/ebay/ep/poc/spark/reporting/process/util/DataUtil.scala:37:
>> error: overloaded method value newAPIHadoopFile with alternatives:
>>
>> [INFO] (path: String,fClass:
>> Class[org.apache.avro.mapreduce.AvroKeyInputFormat[org.apache.avro.generic.GenericRecord]],kClass:
>> Class[org.apache.avro.mapred.AvroKey[org.apache.avro.generic.GenericRecord]],vClass:
>> Class[org.apache.hadoop.io.NullWritable],conf:
>> org.apache.hadoop.conf.Configuration)org.apache.spark.rdd.RDD[(org.apache.avro.mapred.AvroKey[org.apache.avro.generic.GenericRecord],
>> org.apache.hadoop.io.NullWritable)] <and>
>>
>> [INFO] (path: String)(implicit km:
>> scala.reflect.ClassTag[org.apache.avro.mapred.AvroKey[org.apache.avro.generic.GenericRecord]],
>> implicit vm: scala.reflect.ClassTag[org.apache.hadoop.io.NullWritable],
>> implicit fm:
>> scala.reflect.ClassTag[org.apache.avro.mapreduce.AvroKeyInputFormat[org.apache.avro.generic.GenericRecord]])org.apache.spark.rdd.RDD[(org.apache.avro.mapred.AvroKey[org.apache.avro.generic.GenericRecord],
>> org.apache.hadoop.io.NullWritable)]
>>
>> [INFO] cannot be applied to (String,
>> Class[org.apache.avro.mapred.AvroKey[org.apache.avro.generic.GenericRecord]],
>> Class[org.apache.hadoop.io.NullWritable],
>> Class[org.apache.avro.mapreduce.AvroKeyInputFormat[org.apache.avro.generic.GenericRecord]],
>> org.apache.hadoop.conf.Configuration)
>>
>> [INFO] sc.newAPIHadoopFile[AvroKey[GenericRecord], NullWritable,
>> AvroKeyInputFormat[GenericRecord]](path + "/*.avro",
>> classOf[AvroKey[GenericRecord]], classOf[NullWritable],
>> classOf[AvroKeyInputFormat[GenericRecord]], hadoopConfiguration)
>>
>>
>>
>> On Thu, Jun 25, 2015 at 4:14 PM, Silvio Fiorito <
>> silvio.fior...@granturing.com> wrote:
>>
>>> Ok, in that case I think you can set the max split size in the Hadoop
>>> config object, using the FileInputFormat.SPLIT_MAXSIZE config parameter.
>>>
>>> Again, I haven’t done this myself, but looking through the Spark
>>> codebase here:
>>> https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/SparkContext.scala#L1053
>>>
>>> And the HDFS FileInputFormat implementation, that seems like a good
>>> option to try.
>>>
>>> You should be able to call conf.setLong(FileInputFormat.SPLIT_MAXSIZE,
>>> max).
>>>
>>> I hope that helps!
>>>
>>> From: "ÐΞ€ρ@Ҝ (๏̯͡๏)"
>>> Date: Thursday, June 25, 2015 at 5:49 PM
>>> To: Silvio Fiorito
>>> Cc: user
>>> Subject: Re:
>>>
>>> I use
>>>
>>> sc.newAPIHadoopFile[AvroKey[GenericRecord], NullWritable,
>>> AvroKeyInputFormat[GenericRecord]](path + "/*.avro")
>>>
>>>
>>>
>>> https://spark.apache.org/docs/1.3.1/api/java/org/apache/spark/SparkContext.html#newAPIHadoopFile(java.lang.String,
>>> java.lang.Class, java.lang.Class, java.lang.Class,
>>> org.apache.hadoop.conf.Configuration)
>>>
>>> Does not seem to have that partition option.
>>>
>>> On Thu, Jun 25, 2015 at 12:24 PM, Silvio Fiorito <
>>> silvio.fior...@granturing.com> wrote:
>>>
>>>> Hi Deepak,
>>>>
>>>> Have you tried specifying the minimum partitions when you load the
>>>> file? I haven’t tried that myself against HDFS before, so I’m not sure if
>>>> it will affect data locality. Ideally not, it should still maintain data
>>>> locality but just more partitions. Once your job runs, you can check in the
>>>> Spark tasks web UI to ensure they’re all Node local.
>>>>
>>>> val details = sc.textFile(“hdfs://….”, 500)
>>>>
>>>> If you’re using something other than text file you can also specify
>>>> minimum partitions when using sc.hadoopFile.
>>>>
>>>> Thanks,
>>>> Silvio
>>>>
>>>> From: "ÐΞ€ρ@Ҝ (๏̯͡๏)"
>>>> Date: Thursday, June 25, 2015 at 3:10 PM
>>>> To: Akhil Das
>>>> Cc: user
>>>> Subject: Re:
>>>>
>>>> How can i increase the number of tasks from 174 to 500 without
>>>> running repartition.
>>>>
>>>> The input size is 512.0 MB (hadoop) / 4159106. Can this be reduced to
>>>> 64 MB so as to increase the number of tasks. Similar to split size that
>>>> increases the number of mappers in Hadoop M/R.
>>>>
>>>> On Thu, Jun 25, 2015 at 12:06 AM, Akhil Das <ak...@sigmoidanalytics.com
>>>> > wrote:
>>>>
>>>>> Look in the tuning section
>>>>> <https://spark.apache.org/docs/latest/tuning.html>, also you need to
>>>>> figure out whats taking time and where's your bottleneck etc. If
>>>>> everything
>>>>> is tuned properly, then you will need to throw more cores :)
>>>>>
>>>>> Thanks
>>>>> Best Regards
>>>>>
>>>>> On Thu, Jun 25, 2015 at 12:19 AM, ÐΞ€ρ@Ҝ (๏̯͡๏) <deepuj...@gmail.com>
>>>>> wrote:
>>>>>
>>>>>> Its taking an hour and on Hadoop it takes 1h 30m, is there a way to
>>>>>> make it run faster ?
>>>>>>
>>>>>> On Wed, Jun 24, 2015 at 11:39 AM, Akhil Das <
>>>>>> ak...@sigmoidanalytics.com> wrote:
>>>>>>
>>>>>>> Cool. :)
>>>>>>> On 24 Jun 2015 23:44, "ÐΞ€ρ@Ҝ (๏̯͡๏)" <deepuj...@gmail.com> wrote:
>>>>>>>
>>>>>>>> Its running now.
>>>>>>>>
>>>>>>>> On Wed, Jun 24, 2015 at 10:45 AM, ÐΞ€ρ@Ҝ (๏̯͡๏) <
>>>>>>>> deepuj...@gmail.com> wrote:
>>>>>>>>
>>>>>>>>> Now running with
>>>>>>>>>
>>>>>>>>> *--num-executors 9973 --driver-memory 14g --driver-java-options
>>>>>>>>> "-XX:MaxPermSize=512M -Xmx4096M -Xms4096M" --executor-memory 14g
>>>>>>>>> --executor-cores 1*
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> On Wed, Jun 24, 2015 at 10:34 AM, ÐΞ€ρ@Ҝ (๏̯͡๏) <
>>>>>>>>> deepuj...@gmail.com> wrote:
>>>>>>>>>
>>>>>>>>>> There are multiple of these
>>>>>>>>>>
>>>>>>>>>> 1)
>>>>>>>>>> 15/06/24 09:53:37 ERROR executor.Executor: Exception in task
>>>>>>>>>> 443.0 in stage 3.0 (TID 1767)
>>>>>>>>>> java.lang.OutOfMemoryError: GC overhead limit exceeded
>>>>>>>>>> at
>>>>>>>>>> sun.reflect.GeneratedSerializationConstructorAccessor1327.newInstance(Unknown
>>>>>>>>>> Source)
>>>>>>>>>> at java.lang.reflect.Constructor.newInstance(Constructor.java:526)
>>>>>>>>>> at
>>>>>>>>>> org.objenesis.instantiator.sun.SunReflectionFactoryInstantiator.newInstance(SunReflectionFactoryInstantiator.java:56)
>>>>>>>>>> at com.esotericsoftware.kryo.Kryo.newInstance(Kryo.java:1065)
>>>>>>>>>> at
>>>>>>>>>> com.esotericsoftware.kryo.serializers.FieldSerializer.create(FieldSerializer.java:228)
>>>>>>>>>> at
>>>>>>>>>> com.esotericsoftware.kryo.serializers.FieldSerializer.read(FieldSerializer.java:217)
>>>>>>>>>> at
>>>>>>>>>> com.esotericsoftware.kryo.Kryo.readClassAndObject(Kryo.java:729)
>>>>>>>>>> at
>>>>>>>>>> com.esotericsoftware.kryo.serializers.MapSerializer.read(MapSerializer.java:134)
>>>>>>>>>> at
>>>>>>>>>> com.esotericsoftware.kryo.serializers.MapSerializer.read(MapSerializer.java:17)
>>>>>>>>>> at com.esotericsoftware.kryo.Kryo.readObject(Kryo.java:648)
>>>>>>>>>> at
>>>>>>>>>> com.esotericsoftware.kryo.serializers.FieldSerializer$ObjectField.read(FieldSerializer.java:605)
>>>>>>>>>> at
>>>>>>>>>> com.esotericsoftware.kryo.serializers.FieldSerializer.read(FieldSerializer.java:221)
>>>>>>>>>> at com.esotericsoftware.kryo.Kryo.readObject(Kryo.java:648)
>>>>>>>>>> at
>>>>>>>>>> com.esotericsoftware.kryo.serializers.FieldSerializer$ObjectField.read(FieldSerializer.java:605)
>>>>>>>>>> at
>>>>>>>>>> com.esotericsoftware.kryo.serializers.FieldSerializer.read(FieldSerializer.java:221)
>>>>>>>>>> at com.esotericsoftware.kryo.Kryo.readObject(Kryo.java:648)
>>>>>>>>>> at
>>>>>>>>>> com.esotericsoftware.kryo.serializers.FieldSerializer$ObjectField.read(FieldSerializer.java:605)
>>>>>>>>>> at
>>>>>>>>>> com.esotericsoftware.kryo.serializers.FieldSerializer.read(FieldSerializer.java:221)
>>>>>>>>>> at
>>>>>>>>>> com.esotericsoftware.kryo.Kryo.readClassAndObject(Kryo.java:729)
>>>>>>>>>> at
>>>>>>>>>> com.twitter.chill.Tuple2Serializer.read(TupleSerializers.scala:42)
>>>>>>>>>> at
>>>>>>>>>> com.twitter.chill.Tuple2Serializer.read(TupleSerializers.scala:33)
>>>>>>>>>> at
>>>>>>>>>> com.esotericsoftware.kryo.Kryo.readClassAndObject(Kryo.java:729)
>>>>>>>>>> at
>>>>>>>>>> org.apache.spark.serializer.KryoDeserializationStream.readObject(KryoSerializer.scala:138)
>>>>>>>>>> at
>>>>>>>>>> org.apache.spark.serializer.DeserializationStream$$anon$1.getNext(Serializer.scala:133)
>>>>>>>>>> at
>>>>>>>>>> org.apache.spark.util.NextIterator.hasNext(NextIterator.scala:71)
>>>>>>>>>> at
>>>>>>>>>> org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:32)
>>>>>>>>>> at
>>>>>>>>>> scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
>>>>>>>>>> at
>>>>>>>>>> org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:32)
>>>>>>>>>> at
>>>>>>>>>> org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:39)
>>>>>>>>>> at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
>>>>>>>>>> at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
>>>>>>>>>> at
>>>>>>>>>> org.apache.spark.util.collection.ExternalAppendOnlyMap.insertAll(ExternalAppendOnlyMap.scala:125)
>>>>>>>>>> 15/06/24 09:53:37 ERROR actor.ActorSystemImpl: exception on
>>>>>>>>>> LARS? timer thread
>>>>>>>>>>
>>>>>>>>>> 2)
>>>>>>>>>> 15/06/24 09:53:37 ERROR actor.ActorSystemImpl: exception on LARS?
>>>>>>>>>> timer thread
>>>>>>>>>> java.lang.OutOfMemoryError: GC overhead limit exceeded
>>>>>>>>>> at
>>>>>>>>>> akka.dispatch.AbstractNodeQueue.<init>(AbstractNodeQueue.java:22)
>>>>>>>>>> at
>>>>>>>>>> akka.actor.LightArrayRevolverScheduler$TaskQueue.<init>(Scheduler.scala:443)
>>>>>>>>>> at
>>>>>>>>>> akka.actor.LightArrayRevolverScheduler$$anon$8.nextTick(Scheduler.scala:409)
>>>>>>>>>> at
>>>>>>>>>> akka.actor.LightArrayRevolverScheduler$$anon$8.run(Scheduler.scala:375)
>>>>>>>>>> at java.lang.Thread.run(Thread.java:745)
>>>>>>>>>> 3)
>>>>>>>>>> # java.lang.OutOfMemoryError: GC overhead limit exceeded
>>>>>>>>>> # -XX:OnOutOfMemoryError="kill %p"
>>>>>>>>>> # Executing /bin/sh -c "kill 20674"...
>>>>>>>>>> [ERROR] [06/24/2015 09:53:37.590] [Executor task launch worker-5]
>>>>>>>>>> [akka.tcp://
>>>>>>>>>> sparkdri...@phxdpehdc9dn2137.stratus.phx.ebay.com:47708/]
>>>>>>>>>> swallowing exception during message send
>>>>>>>>>> (akka.remote.RemoteTransportExceptionNoStackTrace)
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> On Wed, Jun 24, 2015 at 10:31 AM, ÐΞ€ρ@Ҝ (๏̯͡๏) <
>>>>>>>>>> deepuj...@gmail.com> wrote:
>>>>>>>>>>
>>>>>>>>>>> I see this
>>>>>>>>>>>
>>>>>>>>>>> java.lang.OutOfMemoryError: GC overhead limit exceeded
>>>>>>>>>>> at java.util.Arrays.copyOfRange(Arrays.java:2694)
>>>>>>>>>>> at java.lang.String.<init>(String.java:203)
>>>>>>>>>>> at java.lang.StringBuilder.toString(StringBuilder.java:405)
>>>>>>>>>>> at java.io.UnixFileSystem.resolve(UnixFileSystem.java:108)
>>>>>>>>>>> at java.io.File.<init>(File.java:367)
>>>>>>>>>>> at
>>>>>>>>>>> org.apache.spark.storage.DiskBlockManager.getFile(DiskBlockManager.scala:81)
>>>>>>>>>>> at
>>>>>>>>>>> org.apache.spark.storage.DiskBlockManager.getFile(DiskBlockManager.scala:84)
>>>>>>>>>>> at
>>>>>>>>>>> org.apache.spark.shuffle.IndexShuffleBlockManager.getIndexFile(IndexShuffleBlockManager.scala:60)
>>>>>>>>>>> at
>>>>>>>>>>> org.apache.spark.shuffle.IndexShuffleBlockManager.getBlockData(IndexShuffleBlockManager.scala:107)
>>>>>>>>>>> at
>>>>>>>>>>> org.apache.spark.storage.BlockManager.getBlockData(BlockManager.scala:304)
>>>>>>>>>>> at
>>>>>>>>>>> org.apache.spark.network.netty.NettyBlockRpcServer$$anonfun$2.apply(NettyBlockRpcServer.scala:57)
>>>>>>>>>>> at
>>>>>>>>>>> org.apache.spark.network.netty.NettyBlockRpcServer$$anonfun$2.apply(NettyBlockRpcServer.scala:57)
>>>>>>>>>>> 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.ArrayOps$ofRef.foreach(ArrayOps.scala:108)
>>>>>>>>>>> at
>>>>>>>>>>> scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
>>>>>>>>>>> at
>>>>>>>>>>> scala.collection.mutable.ArrayOps$ofRef.map(ArrayOps.scala:108)
>>>>>>>>>>> at
>>>>>>>>>>> org.apache.spark.network.netty.NettyBlockRpcServer.receive(NettyBlockRpcServer.scala:57)
>>>>>>>>>>> at
>>>>>>>>>>> org.apache.spark.network.server.TransportRequestHandler.processRpcRequest(TransportRequestHandler.java:124)
>>>>>>>>>>> at
>>>>>>>>>>> org.apache.spark.network.server.TransportRequestHandler.handle(TransportRequestHandler.java:97)
>>>>>>>>>>> at
>>>>>>>>>>> org.apache.spark.network.server.TransportChannelHandler.channelRead0(TransportChannelHandler.java:91)
>>>>>>>>>>> at
>>>>>>>>>>> org.apache.spark.network.server.TransportChannelHandler.channelRead0(TransportChannelHandler.java:44)
>>>>>>>>>>> at
>>>>>>>>>>> io.netty.channel.SimpleChannelInboundHandler.channelRead(SimpleChannelInboundHandler.java:105)
>>>>>>>>>>> at
>>>>>>>>>>> io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:333)
>>>>>>>>>>> at
>>>>>>>>>>> io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:319)
>>>>>>>>>>> at
>>>>>>>>>>> io.netty.handler.codec.MessageToMessageDecoder.channelRead(MessageToMessageDecoder.java:103)
>>>>>>>>>>> at
>>>>>>>>>>> io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:333)
>>>>>>>>>>> at
>>>>>>>>>>> io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:319)
>>>>>>>>>>> at
>>>>>>>>>>> io.netty.handler.codec.ByteToMessageDecoder.channelRead(ByteToMessageDecoder.java:163)
>>>>>>>>>>> at
>>>>>>>>>>> io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:333)
>>>>>>>>>>>
>>>>>>>>>>> On Wed, Jun 24, 2015 at 7:16 AM, Akhil Das <
>>>>>>>>>>> ak...@sigmoidanalytics.com> wrote:
>>>>>>>>>>>
>>>>>>>>>>>> Can you look a bit more in the error logs? It could be
>>>>>>>>>>>> getting killed because of OOM etc. One thing you can try is to set
>>>>>>>>>>>> the
>>>>>>>>>>>> spark.shuffle.blockTransferService to nio from netty.
>>>>>>>>>>>>
>>>>>>>>>>>> Thanks
>>>>>>>>>>>> Best Regards
>>>>>>>>>>>>
>>>>>>>>>>>> On Wed, Jun 24, 2015 at 5:46 AM, ÐΞ€ρ@Ҝ (๏̯͡๏) <
>>>>>>>>>>>> deepuj...@gmail.com> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>>> I have a Spark job that has 7 stages. The first 3 stage
>>>>>>>>>>>>> complete and the fourth stage beings (joins two RDDs). This stage
>>>>>>>>>>>>> has
>>>>>>>>>>>>> multiple task failures all the below exception.
>>>>>>>>>>>>>
>>>>>>>>>>>>> Multiple tasks (100s) of them get the same exception with
>>>>>>>>>>>>> different hosts. How can all the host suddenly stop responding
>>>>>>>>>>>>> when few
>>>>>>>>>>>>> moments ago 3 stages ran successfully. If I re-run the three
>>>>>>>>>>>>> stages will
>>>>>>>>>>>>> again run successfully. I cannot think of it being a cluster
>>>>>>>>>>>>> issue.
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>> Any suggestions ?
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>> Spark Version : 1.3.1
>>>>>>>>>>>>>
>>>>>>>>>>>>> Exception:
>>>>>>>>>>>>>
>>>>>>>>>>>>> org.apache.spark.shuffle.FetchFailedException: Failed to connect
>>>>>>>>>>>>> to HOST
>>>>>>>>>>>>> at
>>>>>>>>>>>>> org.apache.spark.shuffle.hash.BlockStoreShuffleFetcher$.org$apache$spark$shuffle$hash$BlockStoreShuffleFetcher$$unpackBlock$1(BlockStoreShuffleFetcher.scala:67)
>>>>>>>>>>>>> at
>>>>>>>>>>>>> org.apache.spark.shuffle.hash.BlockStoreShuffleFetcher$$anonfun$3.apply(BlockStoreShuffleFetcher.scala:83)
>>>>>>>>>>>>> at
>>>>>>>>>>>>> org.apache.spark.shuffle.hash.BlockStoreShuffleFetcher$$anonfun$3.apply(BlockStoreShuffleFetcher.scala:83)
>>>>>>>>>>>>> at
>>>>>>>>>>>>> scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
>>>>>>>>>>>>> at
>>>>>>>>>>>>> org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:32)
>>>>>>>>>>>>> at
>>>>>>>>>>>>> org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:39)
>>>>>>>>>>>>> at
>>>>>>>>>>>>> scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
>>>>>>>>>>>>> at
>>>>>>>>>>>>> scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
>>>>>>>>>>>>> at
>>>>>>>>>>>>> org.apache.spark.util.collection.ExternalAppendOnlyMap.insertAll(ExternalAppendOnlyMap.scala:125)
>>>>>>>>>>>>> at org.apache.sp
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>> --
>>>>>>>>>>>>> Deepak
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> --
>>>>>>>>>>> Deepak
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> --
>>>>>>>>>> Deepak
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> --
>>>>>>>>> Deepak
>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>> --
>>>>>>>> Deepak
>>>>>>>>
>>>>>>>>
>>>>>>
>>>>>>
>>>>>> --
>>>>>> Deepak
>>>>>>
>>>>>>
>>>>>
>>>>
>>>>
>>>> --
>>>> Deepak
>>>>
>>>>
>>>
>>>
>>> --
>>> Deepak
>>>
>>>
>>
>>
>> --
>> Deepak
>>
>>
>
>
> --
> Deepak
>
>
--
Deepak
