Are you getting this error in local mode?

On Tue, Sep 22, 2015 at 7:34 AM, srungarapu vamsi <srungarapu1...@gmail.com>
wrote:

> Yes, I tried ssc.checkpoint("checkpoint"), it works for me as long as i
> don't use reduceByKeyAndWindow.
>
> When i start using "reduceByKeyAndWindow" it complains me with the error
> "Exception in thread "main" org.apache.spark.SparkException: Invalid
> checkpoint directory: file:/home/ubuntu/checkpoint/342e3171-01f3-48$
> 2-97be-e3862eb5c944/rdd-8"
>
> The stack trace is as below:
>
> Exception in thread "main" org.apache.spark.SparkException: Invalid
> checkpoint directory: file:/home/ubuntu/checkpoint/342e3171[22/9706$
> 2-97be-e3862eb5c944/rdd-8
>         at
> org.apache.spark.rdd.CheckpointRDD.getPartitions(CheckpointRDD.scala:54)
>         at
> org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:219)
>         at
> org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:217)
>         at scala.Option.getOrElse(Option.scala:120)
>         at org.apache.spark.rdd.RDD.partitions(RDD.scala:217)
>         at
> org.apache.spark.rdd.RDDCheckpointData.doCheckpoint(RDDCheckpointData.scala:97)
>         at org.apache.spark.rdd.RDD.doCheckpoint(RDD.scala:1415)
>         at
> org.apache.spark.rdd.RDD$$anonfun$doCheckpoint$1.apply(RDD.scala:1417)
>         at
> org.apache.spark.rdd.RDD$$anonfun$doCheckpoint$1.apply(RDD.scala:1417)
>         at scala.collection.immutable.List.foreach(List.scala:318)
>         at org.apache.spark.rdd.RDD.doCheckpoint(RDD.scala:1417)
>         at
> org.apache.spark.rdd.RDD$$anonfun$doCheckpoint$1.apply(RDD.scala:1417)
>         at
> org.apache.spark.rdd.RDD$$anonfun$doCheckpoint$1.apply(RDD.scala:1417)
>         at scala.collection.immutable.List.foreach(List.scala:318)
>         at org.apache.spark.rdd.RDD.doCheckpoint(RDD.scala:1417)
>         at
> org.apache.spark.rdd.RDD$$anonfun$doCheckpoint$1.apply(RDD.scala:1417)
>         at
> org.apache.spark.rdd.RDD$$anonfun$doCheckpoint$1.apply(RDD.scala:1417)
>         at
> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
>         at
> scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
>         at org.apache.spark.rdd.RDD.doCheckpoint(RDD.scala:1417)
>         at
> org.apache.spark.rdd.RDD$$anonfun$doCheckpoint$1.apply(RDD.scala:1417)
>         at
> org.apache.spark.rdd.RDD$$anonfun$doCheckpoint$1.apply(RDD.scala:1417)
>         at
> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
>         at
> scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
>         at org.apache.spark.rdd.RDD.doCheckpoint(RDD.scala:1417)
>         at
> org.apache.spark.rdd.RDD$$anonfun$doCheckpoint$1.apply(RDD.scala:1417)
>         at
> org.apache.spark.rdd.RDD$$anonfun$doCheckpoint$1.apply(RDD.scala:1417)
>         at
> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
>         at
> scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
>         at org.apache.spark.rdd.RDD.doCheckpoint(RDD.scala:1417)
>         at
> org.apache.spark.rdd.RDD$$anonfun$doCheckpoint$1.apply(RDD.scala:1417)
>         at
> org.apache.spark.rdd.RDD$$anonfun$doCheckpoint$1.apply(RDD.scala:1417)
>         at
> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
>         at
> scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
>         at org.apache.spark.rdd.RDD.doCheckpoint(RDD.scala:1417)
>         at org.apache.spark.SparkContext.runJob(SparkContext.scala:1468)
>         at org.apache.spark.SparkContext.runJob(SparkContext.scala:1483)
>         at org.apache.spark.SparkContext.runJob(SparkContext.scala:1504)
>         at
> com.datastax.spark.connector.streaming.DStreamFunctions$$anonfun$saveToCassandra$1.apply(DStreamFunctions.scala:33)
>         at
> com.datastax.spark.connector.streaming.DStreamFunctions$$anonfun$saveToCassandra$1.apply(DStreamFunctions.scala:33)
>         at
> org.apache.spark.streaming.dstream.DStream$$anonfun$foreachRDD$1.apply(DStream.scala:534)
>         at
> org.apache.spark.streaming.dstream.DStream$$anonfun$foreachRDD$1.apply(DStream.scala:534)
>         at
> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply$mcV$sp(ForEachDStream.scala:42)
>         at
> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:40)
>         at
> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:40)
>         at scala.util.Try$.apply(Try.scala:161)
>         at org.apache.spark.streaming.scheduler.Job.run(Job.scala:32)
>         at
> org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply$mcV$sp(JobScheduler.scala:176)
>         at
> org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:176)
>         at
> org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:176)
>         at scala.util.DynamicVariable.withValue(DynamicVariable.scala:57)
>         at
> org.apache.spark.streaming.scheduler.JobScheduler$JobHandler.run(JobScheduler.scala:175)
>         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)
>
> On Tue, Sep 22, 2015 at 6:49 PM, Adrian Tanase <atan...@adobe.com> wrote:
>
>> Have you tried simply ssc.checkpoint("checkpointā€¯)? This should create it
>> in the local folder, has always worked for me when in development on local
>> mode.
>>
>> For the others (/tmp/..) make sure you have rights to write there.
>>
>> -adrian
>>
>> From: srungarapu vamsi
>> Date: Tuesday, September 22, 2015 at 7:59 AM
>> To: user
>> Subject: Invalid checkpoint url
>>
>> I am using reduceByKeyAndWindow (with inverse reduce function) in my
>> code.
>> In order to use this, it seems the checkpointDirectory which i have to
>> use should be hadoop compatible file system.
>> Does that mean that, i should setup hadoop on my system.
>> I googled about this and i found in a S.O answer that i need not setup
>> hdfs but the checkpoint directory should be HDFS copatible.
>>
>> I am a beginner in this area. I am running my spark streaming application
>> on ubuntu 14.04, spark -1.3.1
>> If at all i need not setup hdfs and ext4 is hdfs compatible, then how
>> does my checkpoint directory look like?
>>
>> i tried all these:
>> ssc.checkpoint("/tmp/checkpoint")
>> ssc.checkpoint("hdfs:///tmp/checkpoint")
>> ssc.checkpoint("file:///tmp/checkpoint")
>>
>> But none of them worked for me.
>>
>> --
>> /Vamsi
>>
>
>
>
> --
> /Vamsi
>

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