Bingo! That is the problem. The solution is now obvious I presume :)
On Tue, Sep 22, 2015 at 9:41 PM, srungarapu vamsi <[email protected]>
wrote:
> @Das,
> No, i am getting in the cluster mode.
> I think i understood why i am getting this error, please correct me if i
> am wrong.
> Reason is:
> checkpointing writes rdd to disk, so this checkpointing happens on all
> workers. Whenever, spark has to read back the rdd , checkpoint directory
> should be reachable to all the workers and should be a common place where
> workers can write to and read from. This asks for commonly accessible file
> system like nfs or hdfs or s3.
> So, if i give ssc.checkpoint("some local directory"), since workers are
> not able to read the rdds from the other worker's checkpoint directory , i
> am facing the above mentioned error.
> With this understanding, i am creating a t2 medium, hdfs 2.7.1 node and
> pointing the check point directory to "hdfs://ip:port/path/to/directory"
>
> Please correct me if i am wrong.
>
> On Wed, Sep 23, 2015 at 4:53 AM, Tathagata Das <[email protected]>
> wrote:
>
>> Are you getting this error in local mode?
>>
>>
>> On Tue, Sep 22, 2015 at 7:34 AM, srungarapu vamsi <
>> [email protected]> 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 <[email protected]>
>>> wrote:
>>>
>>>> Have you tried simply ssc.checkpoint("checkpoint”)? 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
>>>
>>
>>
>
>
> --
> /Vamsi
>
