Re: Spark Streaming with Tachyon : Data Loss on Receiver Failure due to WAL error

[N B]

Hi Dibyendu,

I am not sure I understand completely. But are you suggesting that
currently there is no way to enable Checkpoint directory to be in Tachyon?

Thanks
Nikunj


On Fri, Sep 25, 2015 at 11:49 PM, Dibyendu Bhattacharya <
dibyendu.bhattach...@gmail.com> wrote:

> Hi,
>
> Recently I was working on a PR to use Tachyon as OFF_HEAP store for Spark
> Streaming and make sure Spark Streaming can recover from Driver failure and
> recover the blocks form Tachyon.
>
> The The Motivation for this PR is  :
>
> If Streaming application stores the blocks OFF_HEAP, it may not need any
> WAL like feature to recover from Driver failure. As long as the writing of
> blocks to Tachyon from Streaming receiver is durable, it should be
> recoverable from Tachyon directly on Driver failure.
> This can solve the issue of expensive WAL write and duplicating the blocks
> both in MEMORY and also WAL and also guarantee end to end No-Data-Loss
> channel using OFF_HEAP store.
>
> https://github.com/apache/spark/pull/8817
>
> This PR still under review . But having done various fail over testing in
> my environment , I see this PR worked perfectly fine without any data loss
> . Let see what TD and other have to say on this PR .
>
> Below is the configuration I used to test this PR ..
>
>
> Spark : 1.6 from Master
> Tachyon : 0.7.1
>
> SparkConfiguration Details :
>
> SparkConf conf = new SparkConf().setAppName("TestTachyon")
> .set("spark.streaming.unpersist", "true")
> .set("spark.local.dir", "/mnt1/spark/tincan")
> .set("tachyon.zookeeper.address","10.252.5.113:2182")
> .set("tachyon.usezookeeper","true")
> .set("spark.externalBlockStore.url", "tachyon-ft://
> ip-10-252-5-113.asskickery.us:19998")
> .set("spark.externalBlockStore.baseDir", "/sparkstreaming")
> .set("spark.externalBlockStore.folderName","pearson")
> .set("spark.externalBlockStore.dirId", "subpub")
>
> .set("spark.externalBlockStore.keepExternalBlockStoreDirOnShutdown","true");
>
> JavaStreamingContext jsc = new JavaStreamingContext(conf, new Duration(
> 1));
>
> String checkpointDirectory = "hdfs://
> 10.252.5.113:9000/user/hadoop/spark/wal";
>
> jsc.checkpoint(checkpointDirectory);
>
>
> //I am using the My Receiver Based Consumer (
> https://github.com/dibbhatt/kafka-spark-consumer) . But
> KafkaUtil.CreateStream will also work
>
> JavaDStream unionStreams = ReceiverLauncher.launch(
> jsc, props, numberOfReceivers, StorageLevel.OFF_HEAP());
>
>
>
>
> Regards,
> Dibyendu
>
> On Sat, Sep 26, 2015 at 11:59 AM, N B  wrote:
>
>> Hi Dibyendu,
>>
>> How does one go about configuring spark streaming to use tachyon as its
>> place for storing checkpoints? Also, can one do this with tachyon running
>> on a completely different node than where spark processes are running?
>>
>> Thanks
>> Nikunj
>>
>>
>> On Thu, May 21, 2015 at 8:35 PM, Dibyendu Bhattacharya <
>> dibyendu.bhattach...@gmail.com> wrote:
>>
>>> Hi Tathagata,
>>>
>>> Thanks for looking into this. Further investigating I found that the
>>> issue is with Tachyon does not support File Append. The streaming receiver
>>> which writes to WAL when failed, and again restarted, not able to append to
>>> same WAL file after restart.
>>>
>>> I raised this with Tachyon user group, and Haoyuan told that within 3
>>> months time Tachyon file append will be ready. Will revisit this issue
>>> again then .
>>>
>>> Regards,
>>> Dibyendu
>>>
>>>
>>> On Fri, May 22, 2015 at 12:24 AM, Tathagata Das 
>>> wrote:
>>>
 Looks like somehow the file size reported by the FSInputDStream of
 Tachyon's FileSystem interface, is returning zero.

 On Mon, May 11, 2015 at 4:38 AM, Dibyendu Bhattacharya <
 dibyendu.bhattach...@gmail.com> wrote:

> Just to follow up this thread further .
>
> I was doing some fault tolerant testing of Spark Streaming with
> Tachyon as OFF_HEAP block store. As I said in earlier email, I could able
> to solve the BlockNotFound exception when I used Hierarchical Storage
> of Tachyon ,  which is good.
>
> I continue doing some testing around storing the Spark Streaming WAL
> and CheckPoint files also in Tachyon . Here is few finding ..
>
>
> When I store the Spark Streaming Checkpoint location in Tachyon , the
> throughput is much higher . I tested the Driver and Receiver failure cases
> , and Spark Streaming is able to recover without any Data Loss on Driver
> failure.
>
> *But on Receiver failure , Spark Streaming looses data* as I see
> Exception while reading the WAL file from Tachyon "receivedData" location
>  for the same Receiver id which just failed.
>
> If I change the Checkpoint location back to HDFS , Spark Streaming can
> recover from both Driver and Receiver failure .
>
> Here is the Log details when Spark Streaming receiver failed ...I
> raised a JIRA for the same issue :
> 

Re: Spark Streaming with Tachyon : Data Loss on Receiver Failure due to WAL error

[Dibyendu Bhattacharya]

Hi,

Recently I was working on a PR to use Tachyon as OFF_HEAP store for Spark
Streaming and make sure Spark Streaming can recover from Driver failure and
recover the blocks form Tachyon.

The The Motivation for this PR is  :

If Streaming application stores the blocks OFF_HEAP, it may not need any
WAL like feature to recover from Driver failure. As long as the writing of
blocks to Tachyon from Streaming receiver is durable, it should be
recoverable from Tachyon directly on Driver failure.
This can solve the issue of expensive WAL write and duplicating the blocks
both in MEMORY and also WAL and also guarantee end to end No-Data-Loss
channel using OFF_HEAP store.

https://github.com/apache/spark/pull/8817

This PR still under review . But having done various fail over testing in
my environment , I see this PR worked perfectly fine without any data loss
. Let see what TD and other have to say on this PR .

Below is the configuration I used to test this PR ..


Spark : 1.6 from Master
Tachyon : 0.7.1

SparkConfiguration Details :

SparkConf conf = new SparkConf().setAppName("TestTachyon")
.set("spark.streaming.unpersist", "true")
.set("spark.local.dir", "/mnt1/spark/tincan")
.set("tachyon.zookeeper.address","10.252.5.113:2182")
.set("tachyon.usezookeeper","true")
.set("spark.externalBlockStore.url", "tachyon-ft://
ip-10-252-5-113.asskickery.us:19998")
.set("spark.externalBlockStore.baseDir", "/sparkstreaming")
.set("spark.externalBlockStore.folderName","pearson")
.set("spark.externalBlockStore.dirId", "subpub")

.set("spark.externalBlockStore.keepExternalBlockStoreDirOnShutdown","true");

JavaStreamingContext jsc = new JavaStreamingContext(conf, new Duration(
1));

String checkpointDirectory = "hdfs://10.252.5.113:9000/user/hadoop/spark/wal
";

jsc.checkpoint(checkpointDirectory);


//I am using the My Receiver Based Consumer (
https://github.com/dibbhatt/kafka-spark-consumer) . But
KafkaUtil.CreateStream will also work

JavaDStream unionStreams = ReceiverLauncher.launch(
jsc, props, numberOfReceivers, StorageLevel.OFF_HEAP());




Regards,
Dibyendu

On Sat, Sep 26, 2015 at 11:59 AM, N B  wrote:

> Hi Dibyendu,
>
> How does one go about configuring spark streaming to use tachyon as its
> place for storing checkpoints? Also, can one do this with tachyon running
> on a completely different node than where spark processes are running?
>
> Thanks
> Nikunj
>
>
> On Thu, May 21, 2015 at 8:35 PM, Dibyendu Bhattacharya <
> dibyendu.bhattach...@gmail.com> wrote:
>
>> Hi Tathagata,
>>
>> Thanks for looking into this. Further investigating I found that the
>> issue is with Tachyon does not support File Append. The streaming receiver
>> which writes to WAL when failed, and again restarted, not able to append to
>> same WAL file after restart.
>>
>> I raised this with Tachyon user group, and Haoyuan told that within 3
>> months time Tachyon file append will be ready. Will revisit this issue
>> again then .
>>
>> Regards,
>> Dibyendu
>>
>>
>> On Fri, May 22, 2015 at 12:24 AM, Tathagata Das 
>> wrote:
>>
>>> Looks like somehow the file size reported by the FSInputDStream of
>>> Tachyon's FileSystem interface, is returning zero.
>>>
>>> On Mon, May 11, 2015 at 4:38 AM, Dibyendu Bhattacharya <
>>> dibyendu.bhattach...@gmail.com> wrote:
>>>
 Just to follow up this thread further .

 I was doing some fault tolerant testing of Spark Streaming with Tachyon
 as OFF_HEAP block store. As I said in earlier email, I could able to solve
 the BlockNotFound exception when I used Hierarchical Storage of
 Tachyon ,  which is good.

 I continue doing some testing around storing the Spark Streaming WAL
 and CheckPoint files also in Tachyon . Here is few finding ..


 When I store the Spark Streaming Checkpoint location in Tachyon , the
 throughput is much higher . I tested the Driver and Receiver failure cases
 , and Spark Streaming is able to recover without any Data Loss on Driver
 failure.

 *But on Receiver failure , Spark Streaming looses data* as I see
 Exception while reading the WAL file from Tachyon "receivedData" location
  for the same Receiver id which just failed.

 If I change the Checkpoint location back to HDFS , Spark Streaming can
 recover from both Driver and Receiver failure .

 Here is the Log details when Spark Streaming receiver failed ...I
 raised a JIRA for the same issue :
 https://issues.apache.org/jira/browse/SPARK-7525



 INFO : org.apache.spark.scheduler.DAGScheduler - *Executor lost: 2
 (epoch 1)*
 INFO : org.apache.spark.storage.BlockManagerMasterEndpoint - Trying to
 remove executor 2 from BlockManagerMaster.
 INFO : org.apache.spark.storage.BlockManagerMasterEndpoint - Removing
 block manager BlockManagerId(2, 10.252.5.54, 45789)
 INFO : org.apache.spark.storage.BlockManagerMaster - Removed 2

Re: Spark Streaming with Tachyon : Data Loss on Receiver Failure due to WAL error

[N B]

Hi Dibyendu,

Thanks. I believe I understand why it has been an issue using S3 for
checkpoints based on your explanation. But does this limitation apply only
if recovery is needed in case of driver failure?

What if we are not interested in recovery after a driver failure. However,
just for the purposes of running streaming pipelines that do
reduceByKeyAndWindow() and updateStateByKey() operations it needs to have a
checkpoint directory configured.

Do you think this usage will also run into issues if an S3 location is
provided for the checkpoint directory. We will not use it to do any
explicit recovery like I stated above.

Thanks
Nikunj



On Sat, Sep 26, 2015 at 3:13 AM, Dibyendu Bhattacharya <
dibyendu.bhattach...@gmail.com> wrote:

> In Spark Streaming , Checkpoint Directory is used for two purpose
>
> 1. Metadata checkpointing
>
> 2. Data checkpointing
>
> If you enable WAL to recover from Driver failure, Spark Streaming will
> also write the Received Blocks in WAL which stored in checkpoint directory.
>
> For streaming solution to recover from any failure without any data loss ,
> you need to enable Meta Data Check pointing and WAL.  You do not need to
> enable Data Check pointing.
>
> From my experiments and the PR I mentioned , I configured the Meta Data
> Check Pointing in HDFS , and stored the Received Blocks OFF_HEAP.  And I
> did not use any WAL . The PR I proposed would recover from Driver fail-over
> without using any WAL like feature because Blocks are already available in
> Tachyon. The Meta Data Checkpoint helps to recover the meta data about past
> received blocks.
>
> Now the question is , can I configure Tachyon as my Metadata Checkpoint
> location ? I tried that , and Streaming application writes the
> receivedBlockMeataData to Tachyon, but on driver failure, it can not
> recover the received block meta data from Tachyon. I sometime see Zero size
> files in Tachyon checkpoint location , and it can not recover past events .
> I need to understand what is the issue of storing meta data in Tachyon .
> That needs a different JIRA I guess.
>
> Let me know I am able to explain the current scenario around Spark
> Streaming and Tachyon .
>
> Regards,
> Dibyendu
>
>
>
>
> On Sat, Sep 26, 2015 at 1:04 PM, N B  wrote:
>
>> Hi Dibyendu,
>>
>> I am not sure I understand completely. But are you suggesting that
>> currently there is no way to enable Checkpoint directory to be in Tachyon?
>>
>> Thanks
>> Nikunj
>>
>>
>> On Fri, Sep 25, 2015 at 11:49 PM, Dibyendu Bhattacharya <
>> dibyendu.bhattach...@gmail.com> wrote:
>>
>>> Hi,
>>>
>>> Recently I was working on a PR to use Tachyon as OFF_HEAP store for
>>> Spark Streaming and make sure Spark Streaming can recover from Driver
>>> failure and recover the blocks form Tachyon.
>>>
>>> The The Motivation for this PR is  :
>>>
>>> If Streaming application stores the blocks OFF_HEAP, it may not need any
>>> WAL like feature to recover from Driver failure. As long as the writing of
>>> blocks to Tachyon from Streaming receiver is durable, it should be
>>> recoverable from Tachyon directly on Driver failure.
>>> This can solve the issue of expensive WAL write and duplicating the
>>> blocks both in MEMORY and also WAL and also guarantee end to end
>>> No-Data-Loss channel using OFF_HEAP store.
>>>
>>> https://github.com/apache/spark/pull/8817
>>>
>>> This PR still under review . But having done various fail over testing
>>> in my environment , I see this PR worked perfectly fine without any data
>>> loss . Let see what TD and other have to say on this PR .
>>>
>>> Below is the configuration I used to test this PR ..
>>>
>>>
>>> Spark : 1.6 from Master
>>> Tachyon : 0.7.1
>>>
>>> SparkConfiguration Details :
>>>
>>> SparkConf conf = new SparkConf().setAppName("TestTachyon")
>>> .set("spark.streaming.unpersist", "true")
>>> .set("spark.local.dir", "/mnt1/spark/tincan")
>>> .set("tachyon.zookeeper.address","10.252.5.113:2182")
>>> .set("tachyon.usezookeeper","true")
>>> .set("spark.externalBlockStore.url", "tachyon-ft://
>>> ip-10-252-5-113.asskickery.us:19998")
>>> .set("spark.externalBlockStore.baseDir", "/sparkstreaming")
>>> .set("spark.externalBlockStore.folderName","pearson")
>>> .set("spark.externalBlockStore.dirId", "subpub")
>>>
>>> .set("spark.externalBlockStore.keepExternalBlockStoreDirOnShutdown","true");
>>>
>>> JavaStreamingContext jsc = new JavaStreamingContext(conf, new Duration(
>>> 1));
>>>
>>> String checkpointDirectory = "hdfs://
>>> 10.252.5.113:9000/user/hadoop/spark/wal";
>>>
>>> jsc.checkpoint(checkpointDirectory);
>>>
>>>
>>> //I am using the My Receiver Based Consumer (
>>> https://github.com/dibbhatt/kafka-spark-consumer) . But
>>> KafkaUtil.CreateStream will also work
>>>
>>> JavaDStream unionStreams = ReceiverLauncher.launch(
>>> jsc, props, numberOfReceivers, StorageLevel.OFF_HEAP());
>>>
>>>
>>>
>>>
>>> Regards,
>>> Dibyendu
>>>
>>> On Sat, Sep 26, 2015 at 11:59 AM, N B 

Re: Spark Streaming with Tachyon : Data Loss on Receiver Failure due to WAL error

[N B]

I wanted to add that we are not configuring the WAL in our scenario.

Thanks again,
Nikunj


On Sat, Sep 26, 2015 at 11:35 AM, N B  wrote:

> Hi Dibyendu,
>
> Thanks. I believe I understand why it has been an issue using S3 for
> checkpoints based on your explanation. But does this limitation apply only
> if recovery is needed in case of driver failure?
>
> What if we are not interested in recovery after a driver failure. However,
> just for the purposes of running streaming pipelines that do
> reduceByKeyAndWindow() and updateStateByKey() operations it needs to have a
> checkpoint directory configured.
>
> Do you think this usage will also run into issues if an S3 location is
> provided for the checkpoint directory. We will not use it to do any
> explicit recovery like I stated above.
>
> Thanks
> Nikunj
>
>
>
> On Sat, Sep 26, 2015 at 3:13 AM, Dibyendu Bhattacharya <
> dibyendu.bhattach...@gmail.com> wrote:
>
>> In Spark Streaming , Checkpoint Directory is used for two purpose
>>
>> 1. Metadata checkpointing
>>
>> 2. Data checkpointing
>>
>> If you enable WAL to recover from Driver failure, Spark Streaming will
>> also write the Received Blocks in WAL which stored in checkpoint directory.
>>
>> For streaming solution to recover from any failure without any data loss
>> , you need to enable Meta Data Check pointing and WAL.  You do not need to
>> enable Data Check pointing.
>>
>> From my experiments and the PR I mentioned , I configured the Meta Data
>> Check Pointing in HDFS , and stored the Received Blocks OFF_HEAP.  And I
>> did not use any WAL . The PR I proposed would recover from Driver fail-over
>> without using any WAL like feature because Blocks are already available in
>> Tachyon. The Meta Data Checkpoint helps to recover the meta data about past
>> received blocks.
>>
>> Now the question is , can I configure Tachyon as my Metadata Checkpoint
>> location ? I tried that , and Streaming application writes the
>> receivedBlockMeataData to Tachyon, but on driver failure, it can not
>> recover the received block meta data from Tachyon. I sometime see Zero size
>> files in Tachyon checkpoint location , and it can not recover past events .
>> I need to understand what is the issue of storing meta data in Tachyon .
>> That needs a different JIRA I guess.
>>
>> Let me know I am able to explain the current scenario around Spark
>> Streaming and Tachyon .
>>
>> Regards,
>> Dibyendu
>>
>>
>>
>>
>> On Sat, Sep 26, 2015 at 1:04 PM, N B  wrote:
>>
>>> Hi Dibyendu,
>>>
>>> I am not sure I understand completely. But are you suggesting that
>>> currently there is no way to enable Checkpoint directory to be in Tachyon?
>>>
>>> Thanks
>>> Nikunj
>>>
>>>
>>> On Fri, Sep 25, 2015 at 11:49 PM, Dibyendu Bhattacharya <
>>> dibyendu.bhattach...@gmail.com> wrote:
>>>
 Hi,

 Recently I was working on a PR to use Tachyon as OFF_HEAP store for
 Spark Streaming and make sure Spark Streaming can recover from Driver
 failure and recover the blocks form Tachyon.

 The The Motivation for this PR is  :

 If Streaming application stores the blocks OFF_HEAP, it may not need
 any WAL like feature to recover from Driver failure. As long as the writing
 of blocks to Tachyon from Streaming receiver is durable, it should be
 recoverable from Tachyon directly on Driver failure.
 This can solve the issue of expensive WAL write and duplicating the
 blocks both in MEMORY and also WAL and also guarantee end to end
 No-Data-Loss channel using OFF_HEAP store.

 https://github.com/apache/spark/pull/8817

 This PR still under review . But having done various fail over testing
 in my environment , I see this PR worked perfectly fine without any data
 loss . Let see what TD and other have to say on this PR .

 Below is the configuration I used to test this PR ..


 Spark : 1.6 from Master
 Tachyon : 0.7.1

 SparkConfiguration Details :

 SparkConf conf = new SparkConf().setAppName("TestTachyon")
 .set("spark.streaming.unpersist", "true")
 .set("spark.local.dir", "/mnt1/spark/tincan")
 .set("tachyon.zookeeper.address","10.252.5.113:2182")
 .set("tachyon.usezookeeper","true")
 .set("spark.externalBlockStore.url", "tachyon-ft://
 ip-10-252-5-113.asskickery.us:19998")
 .set("spark.externalBlockStore.baseDir", "/sparkstreaming")
 .set("spark.externalBlockStore.folderName","pearson")
 .set("spark.externalBlockStore.dirId", "subpub")

 .set("spark.externalBlockStore.keepExternalBlockStoreDirOnShutdown","true");

 JavaStreamingContext jsc = new JavaStreamingContext(conf, new Duration(
 1));

 String checkpointDirectory = "hdfs://
 10.252.5.113:9000/user/hadoop/spark/wal";

 jsc.checkpoint(checkpointDirectory);


 //I am using the My Receiver Based Consumer (
 

Re: Spark Streaming with Tachyon : Data Loss on Receiver Failure due to WAL error

[Tathagata Das]

Looks like somehow the file size reported by the FSInputDStream of
Tachyon's FileSystem interface, is returning zero.

On Mon, May 11, 2015 at 4:38 AM, Dibyendu Bhattacharya 
dibyendu.bhattach...@gmail.com wrote:

 Just to follow up this thread further .

 I was doing some fault tolerant testing of Spark Streaming with Tachyon as
 OFF_HEAP block store. As I said in earlier email, I could able to solve the
 BlockNotFound exception when I used Hierarchical Storage of Tachyon ,
  which is good.

 I continue doing some testing around storing the Spark Streaming WAL and
 CheckPoint files also in Tachyon . Here is few finding ..


 When I store the Spark Streaming Checkpoint location in Tachyon , the
 throughput is much higher . I tested the Driver and Receiver failure cases
 , and Spark Streaming is able to recover without any Data Loss on Driver
 failure.

 *But on Receiver failure , Spark Streaming looses data* as I see
 Exception while reading the WAL file from Tachyon receivedData location
  for the same Receiver id which just failed.

 If I change the Checkpoint location back to HDFS , Spark Streaming can
 recover from both Driver and Receiver failure .

 Here is the Log details when Spark Streaming receiver failed ...I raised a
 JIRA for the same issue : https://issues.apache.org/jira/browse/SPARK-7525



 INFO : org.apache.spark.scheduler.DAGScheduler - *Executor lost: 2 (epoch
 1)*
 INFO : org.apache.spark.storage.BlockManagerMasterEndpoint - Trying to
 remove executor 2 from BlockManagerMaster.
 INFO : org.apache.spark.storage.BlockManagerMasterEndpoint - Removing
 block manager BlockManagerId(2, 10.252.5.54, 45789)
 INFO : org.apache.spark.storage.BlockManagerMaster - Removed 2
 successfully in removeExecutor
 INFO : org.apache.spark.streaming.scheduler.ReceiverTracker - *Registered
 receiver for stream 2 from 10.252.5.62*:47255
 WARN : org.apache.spark.scheduler.TaskSetManager - Lost task 2.1 in stage
 103.0 (TID 421, 10.252.5.62): org.apache.spark.SparkException: *Could not
 read data from write ahead log record
 FileBasedWriteAheadLogSegment(tachyon-ft://10.252.5.113:19998/tachyon/checkpoint/receivedData/2/log-1431341091711-1431341151711,645603894,10891919
 http://10.252.5.113:19998/tachyon/checkpoint/receivedData/2/log-1431341091711-1431341151711,645603894,10891919)*
 at org.apache.spark.streaming.rdd.WriteAheadLogBackedBlockRDD.org
 $apache$spark$streaming$rdd$WriteAheadLogBackedBlockRDD$$getBlockFromWriteAheadLog$1(WriteAheadLogBackedBlockRDD.scala:144)
 at
 org.apache.spark.streaming.rdd.WriteAheadLogBackedBlockRDD$$anonfun$compute$1.apply(WriteAheadLogBackedBlockRDD.scala:168)
 at
 org.apache.spark.streaming.rdd.WriteAheadLogBackedBlockRDD$$anonfun$compute$1.apply(WriteAheadLogBackedBlockRDD.scala:168)
 at scala.Option.getOrElse(Option.scala:120)
 at
 org.apache.spark.streaming.rdd.WriteAheadLogBackedBlockRDD.compute(WriteAheadLogBackedBlockRDD.scala:168)
 at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277)
 at org.apache.spark.rdd.RDD.iterator(RDD.scala:244)
 at org.apache.spark.rdd.UnionRDD.compute(UnionRDD.scala:87)
 at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277)
 at org.apache.spark.rdd.RDD.iterator(RDD.scala:244)
 at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:63)
 at org.apache.spark.scheduler.Task.run(Task.scala:70)
 at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213)
 at
 java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
 at
 java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
 at java.lang.Thread.run(Thread.java:744)
 Caused by: java.lang.IllegalArgumentException:* Seek position is past
 EOF: 645603894, fileSize = 0*
 at tachyon.hadoop.HdfsFileInputStream.seek(HdfsFileInputStream.java:239)
 at org.apache.hadoop.fs.FSDataInputStream.seek(FSDataInputStream.java:37)
 at
 org.apache.spark.streaming.util.FileBasedWriteAheadLogRandomReader.read(FileBasedWriteAheadLogRandomReader.scala:37)
 at
 org.apache.spark.streaming.util.FileBasedWriteAheadLog.read(FileBasedWriteAheadLog.scala:104)
 at org.apache.spark.streaming.rdd.WriteAheadLogBackedBlockRDD.org
 $apache$spark$streaming$rdd$WriteAheadLogBackedBlockRDD$$getBlockFromWriteAheadLog$1(WriteAheadLogBackedBlockRDD.scala:141)
 ... 15 more

 INFO : org.apache.spark.scheduler.TaskSetManager - Starting task 2.2 in
 stage 103.0 (TID 422, 10.252.5.61, ANY, 1909 bytes)
 INFO : org.apache.spark.scheduler.TaskSetManager - Lost task 2.2 in stage
 103.0 (TID 422) on executor 10.252.5.61: org.apache.spark.SparkException
 (Could not read data from write ahead log record
 FileBasedWriteAheadLogSegment(tachyon-ft://
 10.252.5.113:19998/tachyon/checkpoint/receivedData/2/log-1431341091711-1431341151711,645603894,10891919))
 [duplicate 1]
 INFO : org.apache.spark.scheduler.TaskSetManager - Starting task 2.3 in
 stage 103.0 (TID 423, 10.252.5.62, ANY, 1909 bytes)
 INFO : org.apache.spark.deploy.client.AppClient$ClientActor - 

Re: Spark Streaming with Tachyon : Data Loss on Receiver Failure due to WAL error

[Dibyendu Bhattacharya]

Hi Tathagata,

Thanks for looking into this. Further investigating I found that the issue
is with Tachyon does not support File Append. The streaming receiver which
writes to WAL when failed, and again restarted, not able to append to same
WAL file after restart.

I raised this with Tachyon user group, and Haoyuan told that within 3
months time Tachyon file append will be ready. Will revisit this issue
again then .

Regards,
Dibyendu


On Fri, May 22, 2015 at 12:24 AM, Tathagata Das t...@databricks.com wrote:

 Looks like somehow the file size reported by the FSInputDStream of
 Tachyon's FileSystem interface, is returning zero.

 On Mon, May 11, 2015 at 4:38 AM, Dibyendu Bhattacharya 
 dibyendu.bhattach...@gmail.com wrote:

 Just to follow up this thread further .

 I was doing some fault tolerant testing of Spark Streaming with Tachyon
 as OFF_HEAP block store. As I said in earlier email, I could able to solve
 the BlockNotFound exception when I used Hierarchical Storage of Tachyon
 ,  which is good.

 I continue doing some testing around storing the Spark Streaming WAL and
 CheckPoint files also in Tachyon . Here is few finding ..


 When I store the Spark Streaming Checkpoint location in Tachyon , the
 throughput is much higher . I tested the Driver and Receiver failure cases
 , and Spark Streaming is able to recover without any Data Loss on Driver
 failure.

 *But on Receiver failure , Spark Streaming looses data* as I see
 Exception while reading the WAL file from Tachyon receivedData location
  for the same Receiver id which just failed.

 If I change the Checkpoint location back to HDFS , Spark Streaming can
 recover from both Driver and Receiver failure .

 Here is the Log details when Spark Streaming receiver failed ...I raised
 a JIRA for the same issue :
 https://issues.apache.org/jira/browse/SPARK-7525



 INFO : org.apache.spark.scheduler.DAGScheduler - *Executor lost: 2
 (epoch 1)*
 INFO : org.apache.spark.storage.BlockManagerMasterEndpoint - Trying to
 remove executor 2 from BlockManagerMaster.
 INFO : org.apache.spark.storage.BlockManagerMasterEndpoint - Removing
 block manager BlockManagerId(2, 10.252.5.54, 45789)
 INFO : org.apache.spark.storage.BlockManagerMaster - Removed 2
 successfully in removeExecutor
 INFO : org.apache.spark.streaming.scheduler.ReceiverTracker - *Registered
 receiver for stream 2 from 10.252.5.62*:47255
 WARN : org.apache.spark.scheduler.TaskSetManager - Lost task 2.1 in stage
 103.0 (TID 421, 10.252.5.62): org.apache.spark.SparkException: *Could
 not read data from write ahead log record
 FileBasedWriteAheadLogSegment(tachyon-ft://10.252.5.113:19998/tachyon/checkpoint/receivedData/2/log-1431341091711-1431341151711,645603894,10891919
 http://10.252.5.113:19998/tachyon/checkpoint/receivedData/2/log-1431341091711-1431341151711,645603894,10891919)*
 at org.apache.spark.streaming.rdd.WriteAheadLogBackedBlockRDD.org
 $apache$spark$streaming$rdd$WriteAheadLogBackedBlockRDD$$getBlockFromWriteAheadLog$1(WriteAheadLogBackedBlockRDD.scala:144)
 at
 org.apache.spark.streaming.rdd.WriteAheadLogBackedBlockRDD$$anonfun$compute$1.apply(WriteAheadLogBackedBlockRDD.scala:168)
 at
 org.apache.spark.streaming.rdd.WriteAheadLogBackedBlockRDD$$anonfun$compute$1.apply(WriteAheadLogBackedBlockRDD.scala:168)
 at scala.Option.getOrElse(Option.scala:120)
 at
 org.apache.spark.streaming.rdd.WriteAheadLogBackedBlockRDD.compute(WriteAheadLogBackedBlockRDD.scala:168)
 at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277)
 at org.apache.spark.rdd.RDD.iterator(RDD.scala:244)
 at org.apache.spark.rdd.UnionRDD.compute(UnionRDD.scala:87)
 at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277)
 at org.apache.spark.rdd.RDD.iterator(RDD.scala:244)
 at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:63)
 at org.apache.spark.scheduler.Task.run(Task.scala:70)
 at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213)
 at
 java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
 at
 java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
 at java.lang.Thread.run(Thread.java:744)
 Caused by: java.lang.IllegalArgumentException:* Seek position is past
 EOF: 645603894, fileSize = 0*
 at tachyon.hadoop.HdfsFileInputStream.seek(HdfsFileInputStream.java:239)
 at org.apache.hadoop.fs.FSDataInputStream.seek(FSDataInputStream.java:37)
 at
 org.apache.spark.streaming.util.FileBasedWriteAheadLogRandomReader.read(FileBasedWriteAheadLogRandomReader.scala:37)
 at
 org.apache.spark.streaming.util.FileBasedWriteAheadLog.read(FileBasedWriteAheadLog.scala:104)
 at org.apache.spark.streaming.rdd.WriteAheadLogBackedBlockRDD.org
 $apache$spark$streaming$rdd$WriteAheadLogBackedBlockRDD$$getBlockFromWriteAheadLog$1(WriteAheadLogBackedBlockRDD.scala:141)
 ... 15 more

 INFO : org.apache.spark.scheduler.TaskSetManager - Starting task 2.2 in
 stage 103.0 (TID 422, 10.252.5.61, ANY, 1909 bytes)
 INFO :