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From Vishal Santoshi <vishal.santo...@gmail.com>
Subject Re: Failing to recover once checkpoint fails
Date Thu, 05 Oct 2017 13:26:19 GMT
Another thing I noted was this thing

drwxr-xr-x   - root hadoop          0 2017-10-04 13:54
/flink-checkpoints/prod/c4af8dfa864e2f9a51764de9f0725b39/chk-44286

drwxr-xr-x   - root hadoop          0 2017-10-05 09:15
/flink-checkpoints/prod/c4af8dfa864e2f9a51764de9f0725b39/chk-45428


Generally what Flink does IMHO is that it replaces the chk point directory
with a new one. I see it happening now. Every minute it replaces the old
directory.  In this job's case however, it did not delete the 2017-10-04
13:54  and hence the chk-44286 directory.  This was the last chk-44286 (  I
think  )  successfully created before NN had issues but as is usual did not
delete this  chk-44286. It looks as if it started with a blank slate
???????? Does this strike a chord ?????

On Thu, Oct 5, 2017 at 8:56 AM, Vishal Santoshi <vishal.santoshi@gmail.com>
wrote:

> Hello Fabian,
>                       First of all congratulations on this fabulous
> framework. I have worked with GDF and though GDF has some natural pluses
> Flink's state management is far more advanced. With kafka as a source it
> negates issues GDF has ( GDF integration with pub/sub is organic and that
> is to be expected but non FIFO pub/sub is an issue with windows on event
> time etc )
>
>                    Coming back to this issue. We have that same kafka
> topic feeding a streaming druid datasource and we do not see any issue
> there, so so data loss on the source, kafka is not applicable. I am totally
> certain that the "retention" time was not an issue. It is 4 days of
> retention and we fixed this issue within 30 minutes. We could replay kafka
> with a new consumer group.id and that worked fine.
>
>
> Note these properties and see if they strike a chord.
>
> * The setCommitOffsetsOnCheckpoints(boolean) for kafka consumers is the
> default true. I bring this up to see whether flink will in any circumstance
> drive consumption on the kafka perceived offset rather than the one in the
> checkpoint.
>
> * The state.backend.fs.memory-threshold: 0 has not been set.  The state
> is big enough though therefore IMHO no way the state is stored along with
> the meta data in JM ( or ZK ? ) . The reason I bring this up is to make
> sure when you say that the size has to be less than 1024bytes , you are
> talking about cumulative state of the pipeine.
>
> * We have a good sense of SP ( save point )  and CP ( checkpoint ) and
> certainly understand that they actually are not dissimilar. However in this
> case there were multiple attempts to restart the pipe before it finally
> succeeded.
>
> * Other hdfs related poperties.
>
>  state.backend.fs.checkpointdir: hdfs:///flink-checkpoints/<%=
> flink_hdfs_root %>
>
>  state.savepoints.dir: hdfs:///flink-savepoints/<%= flink_hdfs_root %>
>
>  recovery.zookeeper.storageDir: hdfs:///flink-recovery/<%= flink_hdfs_root %>
>
>
>
> Do these make sense ? Is there anything else I should look at.  Please
> also note that it is the second time this has happened. The first time I
> was vacationing and was not privy to the state of the flink pipeline, but
> the net effect were similar. The counts for the first window after an
> internal restart dropped.
>
>
>
>
> Thank you for you patience and regards,
>
> Vishal
>
>
>
>
>
>
>
>
>
>
>
> On Thu, Oct 5, 2017 at 5:01 AM, Fabian Hueske <fhueske@gmail.com> wrote:
>
>> Hi Vishal,
>>
>> window operators are always stateful because the operator needs to
>> remember previously received events (WindowFunction) or intermediate
>> results (ReduceFunction).
>> Given the program you described, a checkpoint should include the Kafka
>> consumer offset and the state of the window operator. If the program
>> eventually successfully (i.e., without an error) recovered from the last
>> checkpoint, all its state should have been restored. Since the last
>> checkpoint was before HDFS went into safe mode, the program would have been
>> reset to that point. If the Kafka retention time is less than the time it
>> took to fix HDFS you would have lost data because it would have been
>> removed from Kafka. If that's not the case, we need to investigate this
>> further because a checkpoint recovery must not result in state loss.
>>
>> Restoring from a savepoint is not so much different from automatic
>> checkpoint recovery. Given that you have a completed savepoint, you can
>> restart the job from that point. The main difference is that checkpoints
>> are only used for internal recovery and usually discarded once the job is
>> terminated while savepoints are retained.
>>
>> Regarding your question if a failed checkpoint should cause the job to
>> fail and recover I'm not sure what the current status is.
>> Stefan (in CC) should know what happens if a checkpoint fails.
>>
>> Best, Fabian
>>
>> 2017-10-05 2:20 GMT+02:00 Vishal Santoshi <vishal.santoshi@gmail.com>:
>>
>>> To add to it, my pipeline is a simple
>>>
>>> keyBy(0)
>>>         .timeWindow(Time.of(window_size, TimeUnit.MINUTES))
>>>         .allowedLateness(Time.of(late_by, TimeUnit.SECONDS))
>>>         .reduce(new ReduceFunction(), new WindowFunction())
>>>
>>>
>>> On Wed, Oct 4, 2017 at 8:19 PM, Vishal Santoshi <
>>> vishal.santoshi@gmail.com> wrote:
>>>
>>>> Hello folks,
>>>>
>>>> As far as I know checkpoint failure should be ignored and retried with
>>>> potentially larger state. I had this situation
>>>>
>>>> * hdfs went into a safe mode b'coz of Name Node issues
>>>> * exception was thrown
>>>>
>>>>     org.apache.hadoop.ipc.RemoteException(org.apache.hadoop.ipc.StandbyException):
>>>> Operation category WRITE is not supported in state standby. Visit
>>>> https://s.apache.org/sbnn-error
>>>>     ..................
>>>>
>>>>     at org.apache.flink.runtime.fs.hdfs.HadoopFileSystem.mkdirs(Had
>>>> oopFileSystem.java:453)
>>>>         at org.apache.flink.core.fs.SafetyNetWrapperFileSystem.mkdirs(S
>>>> afetyNetWrapperFileSystem.java:111)
>>>>         at org.apache.flink.runtime.state.filesystem.FsCheckpointStream
>>>> Factory.createBasePath(FsCheckpointStreamFactory.java:132)
>>>>
>>>> * The pipeline came back after a few restarts and checkpoint failures,
>>>> after the hdfs issues were resolved.
>>>>
>>>> I would not have worried about the restart, but it was evident that I
>>>> lost my operator state. Either it was my kafka consumer that kept on
>>>> advancing it's offset between a start and the next checkpoint failure ( a
>>>> minute's worth ) or the the operator that had partial aggregates was lost.
>>>> I have a 15 minute window of counts on a keyed operator
>>>>
>>>> I am using ROCKS DB and of course have checkpointing turned on.
>>>>
>>>> The questions thus are
>>>>
>>>> * Should a pipeline be restarted if checkpoint fails ?
>>>> * Why on restart did the operator state did not recreate ?
>>>> * Is the nature of the exception thrown have to do with any of this
>>>> b'coz suspend and resume from a save point work as expected ?
>>>> * And though I am pretty sure, are operators like the Window operator
>>>> stateful by drfault and thus if I have timeWindow(Time.of(window_size,
>>>> TimeUnit.MINUTES)).reduce(new ReduceFunction(), new WindowFunction()), the
>>>> state is managed by flink ?
>>>>
>>>> Thanks.
>>>>
>>>
>>>
>>
>

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