Re: Spark Streaming and using Swift object store for checkpointing

2015-07-11 Thread algermissen1971

On 10 Jul 2015, at 23:10, algermissen1971  wrote:

> Hi,
> 
> initially today when moving my streaming application to the cluster the first 
> time I ran in to newbie error of using a local file system for checkpointing 
> and the RDD partition count differences (see exception below).
> 
> Having neither HDFS nor S3 (and the Cassandra-Connector not yet supporting 
> checkpointing[1]) I turned to Swift (which is already available in our 
> architecture).
> 
> I mounted Swift using cloudfuse[2] I see the checkpoint files on all three 
> cluster nodes - but still the job fails with the mentioned exception.
> 
> I experimented with cloudfuse caching settings but that does not *seem* to 
> help.
> 
> Can anyone shed some light on this issue and provide a hint what I might be 
> doing wrong here?

In case this helps somebody else, here is what made it work for me after 
playing with all the options:

cloudfuse -o 
username=,api_key=,direct_io,hard_remove,entry_timeout=1,big_writes,cache_timeout=1,use_snet=True
 /swift

It seems I also had to remove the ~/.cloudfuse files I created previously for 
the options to take effect.

Jan



> 
> Jan
> 
> [1] https://datastax-oss.atlassian.net/browse/SPARKC-13
> [2] https://github.com/redbo/cloudfuse
> 
> 
> 
> Exception:
> 
> org.apache.spark.SparkException: Checkpoint RDD CheckpointRDD[72] at print at 
> App.scala:47(0) has different number of partitions than original RDD 
> MapPartitionsRDD[70] at updateStateByKey at App.scala:47(2)
>   at 
> org.apache.spark.rdd.RDDCheckpointData.doCheckpoint(RDDCheckpointData.scala:103)
>   at 
> org.apache.spark.rdd.RDD$$anonfun$doCheckpoint$1.apply$mcV$sp(RDD.scala:1538)
>   at 
> org.apache.spark.rdd.RDD$$anonfun$doCheckpoint$1.apply(RDD.scala:1535)
>   at 
> org.apache.spark.rdd.RDD$$anonfun$doCheckpoint$1.apply(RDD.scala:1535)
>   at 
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:148)
>   at org.apache.spark.rdd.RDD.doCheckpoint(RDD.scala:1534)
>   at org.apache.spark.SparkContext.runJob(SparkContext.scala:1735)
>   at org.apache.spark.SparkContext.runJob(SparkContext.scala:1750)
>   at org.apache.spark.SparkContext.runJob(SparkContext.scala:1765)
>   at org.apache.spark.rdd.RDD$$anonfun$take$1.apply(RDD.scala:1272)
>   at 
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:148)
>   at 
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:109)
>   at org.apac
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Spark Streaming and using Swift object store for checkpointing

2015-07-10 Thread algermissen1971
Hi,

initially today when moving my streaming application to the cluster the first 
time I ran in to newbie error of using a local file system for checkpointing 
and the RDD partition count differences (see exception below).

Having neither HDFS nor S3 (and the Cassandra-Connector not yet supporting 
checkpointing[1]) I turned to Swift (which is already available in our 
architecture).

I mounted Swift using cloudfuse[2] I see the checkpoint files on all three 
cluster nodes - but still the job fails with the mentioned exception.

I experimented with cloudfuse caching settings but that does not *seem* to help.

Can anyone shed some light on this issue and provide a hint what I might be 
doing wrong here?

Jan

[1] https://datastax-oss.atlassian.net/browse/SPARKC-13
[2] https://github.com/redbo/cloudfuse



Exception:

org.apache.spark.SparkException: Checkpoint RDD CheckpointRDD[72] at print at 
App.scala:47(0) has different number of partitions than original RDD 
MapPartitionsRDD[70] at updateStateByKey at App.scala:47(2)
at 
org.apache.spark.rdd.RDDCheckpointData.doCheckpoint(RDDCheckpointData.scala:103)
at 
org.apache.spark.rdd.RDD$$anonfun$doCheckpoint$1.apply$mcV$sp(RDD.scala:1538)
at 
org.apache.spark.rdd.RDD$$anonfun$doCheckpoint$1.apply(RDD.scala:1535)
at 
org.apache.spark.rdd.RDD$$anonfun$doCheckpoint$1.apply(RDD.scala:1535)
at 
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:148)
at org.apache.spark.rdd.RDD.doCheckpoint(RDD.scala:1534)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1735)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1750)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1765)
at org.apache.spark.rdd.RDD$$anonfun$take$1.apply(RDD.scala:1272)
at 
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:148)
at 
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:109)
at org.apac
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