@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 <t...@databricks.com> wrote:
> 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("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
