Re: Checkpoint of DStream joined with RDD

[Lubomir Nerad]

Hi Ted, all,

do you have any advice regarding my questions in my initial email?

I tried Spark 1.5.2 and 1.6.0 without success. The problem seems to be 
that RDDs use some transient fields which are not restored when they 
are recovered from checkpoint files. In case of some RDD 
implementations it is SparkContext, but it can be also implementation 
specific Configuration object, etc. I see in the sources that in the 
case of DStream recovery, the DStreamGraph takes care of restoring 
StreamingContext in all its DStream-s. But I haven't found any similar 
mechanism for RDDs.


So my question is whether I am doing something wrong or this is a bug 
in Spark? If later, is there some workaround except for implementing a 
custom DStream which will return the same RDD every batch interval and 
joining at DStream level instead of RDD level in transform?


Thanks,
Lubo

On 18.3.2016 18:36, Ted Yu wrote:

This is the line where NPE came from:

if (conf.get(SCAN) != null) {

So Configuration instance was null.

On Fri, Mar 18, 2016 at 9:58 AM, Lubomir Nerad 
> wrote:


The HBase version is 1.0.1.1.

Thanks,
Lubo


On 18.3.2016 17:29, Ted Yu wrote:

I looked at the places in SparkContext.scala where NewHadoopRDD
is constrcuted.
It seems the Configuration object shouldn't be null.

Which hbase release are you using (so that I can see which line
the NPE came from) ?

Thanks

On Fri, Mar 18, 2016 at 8:05 AM, Lubomir Nerad
> wrote:

Hi,

I tried to replicate the example of joining DStream with
lookup RDD from

http://spark.apache.org/docs/latest/streaming-programming-guide.html#transform-operation.
It works fine, but when I enable checkpointing for the
StreamingContext and let the application to recover from a
previously created checkpoint, I always get an exception
during start and the whole application fails. I tried various
types of lookup RDD, but the result is the same.

Exception in the case of HBase RDD is:

Exception in thread "main" java.lang.NullPointerException
at

org.apache.hadoop.hbase.mapreduce.TableInputFormat.setConf(TableInputFormat.java:119)
at
org.apache.spark.rdd.NewHadoopRDD.getPartitions(NewHadoopRDD.scala:109)
at
org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:239)
at
org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:237)
at scala.Option.getOrElse(Option.scala:120)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:237)
at

org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)
at
org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:239)
at
org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:237)
at scala.Option.getOrElse(Option.scala:120)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:237)
at

org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)
at
org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:239)
at
org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:237)
at scala.Option.getOrElse(Option.scala:120)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:237)
at
org.apache.spark.Partitioner$$anonfun$2.apply(Partitioner.scala:58)
at
org.apache.spark.Partitioner$$anonfun$2.apply(Partitioner.scala:58)
at scala.math.Ordering$$anon$5.compare(Ordering.scala:122)
at
java.util.TimSort.countRunAndMakeAscending(TimSort.java:351)
at java.util.TimSort.sort(TimSort.java:216)
at java.util.Arrays.sort(Arrays.java:1438)
at scala.collection.SeqLike$class.sorted(SeqLike.scala:615)
at scala.collection.AbstractSeq.sorted(Seq.scala:40)
at scala.collection.SeqLike$class.sortBy(SeqLike.scala:594)
at scala.collection.AbstractSeq.sortBy(Seq.scala:40)
at
org.apache.spark.Partitioner$.defaultPartitioner(Partitioner.scala:58)
at

org.apache.spark.rdd.PairRDDFunctions$$anonfun$join$2.apply(PairRDDFunctions.scala:651)
at

org.apache.spark.rdd.PairRDDFunctions$$anonfun$join$2.apply(PairRDDFunctions.scala:651)
at

org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
at

org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:316)
at
org.apache.spark.rdd.PairRDDFunctions.join(PairRDDFunctions.scala:650)
at

Re: Checkpoint of DStream joined with RDD

[Ted Yu]

I looked at the places in SparkContext.scala where NewHadoopRDD is
constrcuted.
It seems the Configuration object shouldn't be null.

Which hbase release are you using (so that I can see which line the NPE
came from) ?

Thanks

On Fri, Mar 18, 2016 at 8:05 AM, Lubomir Nerad 
wrote:

> Hi,
>
> I tried to replicate the example of joining DStream with lookup RDD from
> http://spark.apache.org/docs/latest/streaming-programming-guide.html#transform-operation.
> It works fine, but when I enable checkpointing for the StreamingContext and
> let the application to recover from a previously created checkpoint, I
> always get an exception during start and the whole application fails. I
> tried various types of lookup RDD, but the result is the same.
>
> Exception in the case of HBase RDD is:
>
> Exception in thread "main" java.lang.NullPointerException
> at
> org.apache.hadoop.hbase.mapreduce.TableInputFormat.setConf(TableInputFormat.java:119)
> at
> org.apache.spark.rdd.NewHadoopRDD.getPartitions(NewHadoopRDD.scala:109)
> at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:239)
> at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:237)
> at scala.Option.getOrElse(Option.scala:120)
> at org.apache.spark.rdd.RDD.partitions(RDD.scala:237)
> at
> org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)
> at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:239)
> at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:237)
> at scala.Option.getOrElse(Option.scala:120)
> at org.apache.spark.rdd.RDD.partitions(RDD.scala:237)
> at
> org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)
> at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:239)
> at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:237)
> at scala.Option.getOrElse(Option.scala:120)
> at org.apache.spark.rdd.RDD.partitions(RDD.scala:237)
> at org.apache.spark.Partitioner$$anonfun$2.apply(Partitioner.scala:58)
> at org.apache.spark.Partitioner$$anonfun$2.apply(Partitioner.scala:58)
> at scala.math.Ordering$$anon$5.compare(Ordering.scala:122)
> at java.util.TimSort.countRunAndMakeAscending(TimSort.java:351)
> at java.util.TimSort.sort(TimSort.java:216)
> at java.util.Arrays.sort(Arrays.java:1438)
> at scala.collection.SeqLike$class.sorted(SeqLike.scala:615)
> at scala.collection.AbstractSeq.sorted(Seq.scala:40)
> at scala.collection.SeqLike$class.sortBy(SeqLike.scala:594)
> at scala.collection.AbstractSeq.sortBy(Seq.scala:40)
> at
> org.apache.spark.Partitioner$.defaultPartitioner(Partitioner.scala:58)
> at
> org.apache.spark.rdd.PairRDDFunctions$$anonfun$join$2.apply(PairRDDFunctions.scala:651)
> at
> org.apache.spark.rdd.PairRDDFunctions$$anonfun$join$2.apply(PairRDDFunctions.scala:651)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111)
> at org.apache.spark.rdd.RDD.withScope(RDD.scala:316)
> at
> org.apache.spark.rdd.PairRDDFunctions.join(PairRDDFunctions.scala:650)
> at org.apache.spark.api.java.JavaPairRDD.join(JavaPairRDD.scala:546)
>
> I tried Spark 1.5.2 and 1.6.0 without success. The problem seems to be
> that RDDs use some transient fields which are not restored when they are
> recovered from checkpoint files. In case of some RDD implementations it is
> SparkContext, but it can be also implementation specific Configuration
> object, etc. I see in the sources that in the case of DStream recovery, the
> DStreamGraph takes care of restoring StreamingContext in all its DStream-s.
> But I haven't found any similar mechanism for RDDs.
>
> So my question is whether I am doing something wrong or this is a bug in
> Spark? If later, is there some workaround except for implementing a custom
> DStream which will return the same RDD every batch interval and joining at
> DStream level instead of RDD level in transform?
>
> I apologize if this has been discussed in the past and I missed it when
> looking into archive.
>
> Thanks,
> Lubo
>
>
> -
> To unsubscribe, e-mail: user-unsubscr...@spark.apache.org
> For additional commands, e-mail: user-h...@spark.apache.org
>
>

Re: Checkpoint of DStream joined with RDD

[Lubomir Nerad]

The HBase version is 1.0.1.1.

Thanks,
Lubo

On 18.3.2016 17:29, Ted Yu wrote:
I looked at the places in SparkContext.scala where NewHadoopRDD is 
constrcuted.

It seems the Configuration object shouldn't be null.

Which hbase release are you using (so that I can see which line the 
NPE came from) ?


Thanks

On Fri, Mar 18, 2016 at 8:05 AM, Lubomir Nerad 
> wrote:


Hi,

I tried to replicate the example of joining DStream with lookup
RDD from

http://spark.apache.org/docs/latest/streaming-programming-guide.html#transform-operation.
It works fine, but when I enable checkpointing for the
StreamingContext and let the application to recover from a
previously created checkpoint, I always get an exception during
start and the whole application fails. I tried various types of
lookup RDD, but the result is the same.

Exception in the case of HBase RDD is:

Exception in thread "main" java.lang.NullPointerException
at

org.apache.hadoop.hbase.mapreduce.TableInputFormat.setConf(TableInputFormat.java:119)
at
org.apache.spark.rdd.NewHadoopRDD.getPartitions(NewHadoopRDD.scala:109)
at
org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:239)
at
org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:237)
at scala.Option.getOrElse(Option.scala:120)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:237)
at

org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)
at
org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:239)
at
org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:237)
at scala.Option.getOrElse(Option.scala:120)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:237)
at

org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)
at
org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:239)
at
org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:237)
at scala.Option.getOrElse(Option.scala:120)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:237)
at
org.apache.spark.Partitioner$$anonfun$2.apply(Partitioner.scala:58)
at
org.apache.spark.Partitioner$$anonfun$2.apply(Partitioner.scala:58)
at scala.math.Ordering$$anon$5.compare(Ordering.scala:122)
at java.util.TimSort.countRunAndMakeAscending(TimSort.java:351)
at java.util.TimSort.sort(TimSort.java:216)
at java.util.Arrays.sort(Arrays.java:1438)
at scala.collection.SeqLike$class.sorted(SeqLike.scala:615)
at scala.collection.AbstractSeq.sorted(Seq.scala:40)
at scala.collection.SeqLike$class.sortBy(SeqLike.scala:594)
at scala.collection.AbstractSeq.sortBy(Seq.scala:40)
at
org.apache.spark.Partitioner$.defaultPartitioner(Partitioner.scala:58)
at

org.apache.spark.rdd.PairRDDFunctions$$anonfun$join$2.apply(PairRDDFunctions.scala:651)
at

org.apache.spark.rdd.PairRDDFunctions$$anonfun$join$2.apply(PairRDDFunctions.scala:651)
at

org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
at

org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:316)
at
org.apache.spark.rdd.PairRDDFunctions.join(PairRDDFunctions.scala:650)
at
org.apache.spark.api.java.JavaPairRDD.join(JavaPairRDD.scala:546)

I tried Spark 1.5.2 and 1.6.0 without success. The problem seems
to be that RDDs use some transient fields which are not restored
when they are recovered from checkpoint files. In case of some RDD
implementations it is SparkContext, but it can be also
implementation specific Configuration object, etc. I see in the
sources that in the case of DStream recovery, the DStreamGraph
takes care of restoring StreamingContext in all its DStream-s. But
I haven't found any similar mechanism for RDDs.

So my question is whether I am doing something wrong or this is a
bug in Spark? If later, is there some workaround except for
implementing a custom DStream which will return the same RDD every
batch interval and joining at DStream level instead of RDD level
in transform?

I apologize if this has been discussed in the past and I missed it
when looking into archive.

Thanks,
Lubo


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To unsubscribe, e-mail: user-unsubscr...@spark.apache.org

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Re: Checkpoint of DStream joined with RDD

[Ted Yu]

This is the line where NPE came from:

if (conf.get(SCAN) != null) {

So Configuration instance was null.

On Fri, Mar 18, 2016 at 9:58 AM, Lubomir Nerad 
wrote:

> The HBase version is 1.0.1.1.
>
> Thanks,
> Lubo
>
>
> On 18.3.2016 17:29, Ted Yu wrote:
>
> I looked at the places in SparkContext.scala where NewHadoopRDD is
> constrcuted.
> It seems the Configuration object shouldn't be null.
>
> Which hbase release are you using (so that I can see which line the NPE
> came from) ?
>
> Thanks
>
> On Fri, Mar 18, 2016 at 8:05 AM, Lubomir Nerad 
> wrote:
>
>> Hi,
>>
>> I tried to replicate the example of joining DStream with lookup RDD from
>> http://spark.apache.org/docs/latest/streaming-programming-guide.html#transform-operation.
>> It works fine, but when I enable checkpointing for the StreamingContext and
>> let the application to recover from a previously created checkpoint, I
>> always get an exception during start and the whole application fails. I
>> tried various types of lookup RDD, but the result is the same.
>>
>> Exception in the case of HBase RDD is:
>>
>> Exception in thread "main" java.lang.NullPointerException
>> at
>> org.apache.hadoop.hbase.mapreduce.TableInputFormat.setConf(TableInputFormat.java:119)
>> at
>> org.apache.spark.rdd.NewHadoopRDD.getPartitions(NewHadoopRDD.scala:109)
>> at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:239)
>> at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:237)
>> at scala.Option.getOrElse(Option.scala:120)
>> at org.apache.spark.rdd.RDD.partitions(RDD.scala:237)
>> at
>> org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)
>> at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:239)
>> at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:237)
>> at scala.Option.getOrElse(Option.scala:120)
>> at org.apache.spark.rdd.RDD.partitions(RDD.scala:237)
>> at
>> org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)
>> at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:239)
>> at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:237)
>> at scala.Option.getOrElse(Option.scala:120)
>> at org.apache.spark.rdd.RDD.partitions(RDD.scala:237)
>> at org.apache.spark.Partitioner$$anonfun$2.apply(Partitioner.scala:58)
>> at org.apache.spark.Partitioner$$anonfun$2.apply(Partitioner.scala:58)
>> at scala.math.Ordering$$anon$5.compare(Ordering.scala:122)
>> at java.util.TimSort.countRunAndMakeAscending(TimSort.java:351)
>> at java.util.TimSort.sort(TimSort.java:216)
>> at java.util.Arrays.sort(Arrays.java:1438)
>> at scala.collection.SeqLike$class.sorted(SeqLike.scala:615)
>> at scala.collection.AbstractSeq.sorted(Seq.scala:40)
>> at scala.collection.SeqLike$class.sortBy(SeqLike.scala:594)
>> at scala.collection.AbstractSeq.sortBy(Seq.scala:40)
>> at
>> org.apache.spark.Partitioner$.defaultPartitioner(Partitioner.scala:58)
>> at
>> org.apache.spark.rdd.PairRDDFunctions$$anonfun$join$2.apply(PairRDDFunctions.scala:651)
>> at
>> org.apache.spark.rdd.PairRDDFunctions$$anonfun$join$2.apply(PairRDDFunctions.scala:651)
>> at
>> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
>> at
>> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111)
>> at org.apache.spark.rdd.RDD.withScope(RDD.scala:316)
>> at
>> org.apache.spark.rdd.PairRDDFunctions.join(PairRDDFunctions.scala:650)
>> at org.apache.spark.api.java.JavaPairRDD.join(JavaPairRDD.scala:546)
>>
>> I tried Spark 1.5.2 and 1.6.0 without success. The problem seems to be
>> that RDDs use some transient fields which are not restored when they are
>> recovered from checkpoint files. In case of some RDD implementations it is
>> SparkContext, but it can be also implementation specific Configuration
>> object, etc. I see in the sources that in the case of DStream recovery, the
>> DStreamGraph takes care of restoring StreamingContext in all its DStream-s.
>> But I haven't found any similar mechanism for RDDs.
>>
>> So my question is whether I am doing something wrong or this is a bug in
>> Spark? If later, is there some workaround except for implementing a custom
>> DStream which will return the same RDD every batch interval and joining at
>> DStream level instead of RDD level in transform?
>>
>> I apologize if this has been discussed in the past and I missed it when
>> looking into archive.
>>
>> Thanks,
>> Lubo
>>
>>
>> -
>> To unsubscribe, e-mail: user-unsubscr...@spark.apache.org
>> For additional commands, e-mail: user-h...@spark.apache.org
>>
>>
>
>

Checkpoint of DStream joined with RDD

[Lubomir Nerad]

Hi,

I tried to replicate the example of joining DStream with lookup RDD from 
http://spark.apache.org/docs/latest/streaming-programming-guide.html#transform-operation. 
It works fine, but when I enable checkpointing for the StreamingContext 
and let the application to recover from a previously created checkpoint, 
I always get an exception during start and the whole application fails. 
I tried various types of lookup RDD, but the result is the same.


Exception in the case of HBase RDD is:

Exception in thread "main" java.lang.NullPointerException
at 
org.apache.hadoop.hbase.mapreduce.TableInputFormat.setConf(TableInputFormat.java:119)
at 
org.apache.spark.rdd.NewHadoopRDD.getPartitions(NewHadoopRDD.scala:109)

at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:239)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:237)
at scala.Option.getOrElse(Option.scala:120)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:237)
at 
org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)

at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:239)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:237)
at scala.Option.getOrElse(Option.scala:120)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:237)
at 
org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)

at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:239)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:237)
at scala.Option.getOrElse(Option.scala:120)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:237)
at org.apache.spark.Partitioner$$anonfun$2.apply(Partitioner.scala:58)
at org.apache.spark.Partitioner$$anonfun$2.apply(Partitioner.scala:58)
at scala.math.Ordering$$anon$5.compare(Ordering.scala:122)
at java.util.TimSort.countRunAndMakeAscending(TimSort.java:351)
at java.util.TimSort.sort(TimSort.java:216)
at java.util.Arrays.sort(Arrays.java:1438)
at scala.collection.SeqLike$class.sorted(SeqLike.scala:615)
at scala.collection.AbstractSeq.sorted(Seq.scala:40)
at scala.collection.SeqLike$class.sortBy(SeqLike.scala:594)
at scala.collection.AbstractSeq.sortBy(Seq.scala:40)
at 
org.apache.spark.Partitioner$.defaultPartitioner(Partitioner.scala:58)
at 
org.apache.spark.rdd.PairRDDFunctions$$anonfun$join$2.apply(PairRDDFunctions.scala:651)
at 
org.apache.spark.rdd.PairRDDFunctions$$anonfun$join$2.apply(PairRDDFunctions.scala:651)
at 
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
at 
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111)

at org.apache.spark.rdd.RDD.withScope(RDD.scala:316)
at 
org.apache.spark.rdd.PairRDDFunctions.join(PairRDDFunctions.scala:650)

at org.apache.spark.api.java.JavaPairRDD.join(JavaPairRDD.scala:546)

I tried Spark 1.5.2 and 1.6.0 without success. The problem seems to be 
that RDDs use some transient fields which are not restored when they are 
recovered from checkpoint files. In case of some RDD implementations it 
is SparkContext, but it can be also implementation specific 
Configuration object, etc. I see in the sources that in the case of 
DStream recovery, the DStreamGraph takes care of restoring 
StreamingContext in all its DStream-s. But I haven't found any similar 
mechanism for RDDs.


So my question is whether I am doing something wrong or this is a bug in 
Spark? If later, is there some workaround except for implementing a 
custom DStream which will return the same RDD every batch interval and 
joining at DStream level instead of RDD level in transform?


I apologize if this has been discussed in the past and I missed it when 
looking into archive.


Thanks,
Lubo


-
To unsubscribe, e-mail: user-unsubscr...@spark.apache.org
For additional commands, e-mail: user-h...@spark.apache.org