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From Vishal Santoshi <vishal.santo...@gmail.com>
Subject Re: Failing to recover once checkpoint fails
Date Fri, 06 Oct 2017 13:59:16 GMT
Thank you for confirming.


 I think this is a critical bug. In essence any checkpoint store (
hdfs/S3/File)  will loose state if it is unavailable at resume. This
becomes all the more painful with your confirming that  "failed checkpoints
killing the job"  b'coz essentially it mean that if remote store in
unavailable  during checkpoint than you have lost state ( till of course
you have a retry of none or an unbounded retry delay, a delay that you
*hope* the store revives in ) .. Remember  the first retry failure  will
cause new state according the code as written iff the remote store is down.
We would rather have a configurable property that establishes  our desire
to abort something like a "abort_retry_on_chkretrevalfailure"


In our case it is very important that we do not undercount a window, one
reason we use flink and it's awesome failure guarantees, as various alarms
sound ( we do anomaly detection on the time series ).

Please create a jira ticket for us to follow or we could do it.


PS Not aborting on checkpointing, till a configurable limit is very
important too.


On Fri, Oct 6, 2017 at 2:36 AM, Aljoscha Krettek <aljoscha@apache.org>
wrote:

> Hi Vishal,
>
> I think you're right! And thanks for looking into this so deeply.
>
> With your last mail your basically saying, that the checkpoint could not
> be restored because your HDFS was temporarily down. If Flink had not
> deleted that checkpoint it might have been possible to restore it at a
> later point, right?
>
> Regarding failed checkpoints killing the job: yes, this is currently the
> expected behaviour but there are plans to change this.
>
> Best,
> Aljoscha
>
> On 5. Oct 2017, at 17:40, Vishal Santoshi <vishal.santoshi@gmail.com>
> wrote:
>
> I think this is the offending piece. There is a catch all Exception, which
> IMHO should understand a recoverable exception from an unrecoverable on.
>
>
> try {
> completedCheckpoint = retrieveCompletedCheckpoint(checkpointStateHandle);
> if (completedCheckpoint != null) {
> completedCheckpoints.add(completedCheckpoint);
> }
> } catch (Exception e) {
> LOG.warn("Could not retrieve checkpoint. Removing it from the completed "
> +
> "checkpoint store.", e);
> // remove the checkpoint with broken state handle
> removeBrokenStateHandle(checkpointStateHandle.f1, checkpointStateHandle.
> f0);
> }
>
> On Thu, Oct 5, 2017 at 10:57 AM, Vishal Santoshi <
> vishal.santoshi@gmail.com> wrote:
>
>> So this is the issue and tell us that it is wrong. ZK had some state (
>> backed by hdfs ) that referred to a checkpoint ( the same exact last
>> successful checkpoint that was successful before NN screwed us ). When the
>> JM tried to recreate the state and b'coz NN was down failed to retrieve the
>> CHK handle from hdfs and conveniently ( and I think very wrongly ) removed
>> the CHK from being considered and cleaned the pointer ( though failed as
>> was NN was down and is obvious from the dangling file in recovery ) . The
>> metadata itself was on hdfs and failure in retrieving should have been a
>> stop all, not going to trying doing magic exception rather than starting
>> from a blank state.
>>
>> org.apache.flink.util.FlinkException: Could not retrieve checkpoint
>> 44286 from state handle under /0000000000000044286. This indicates that the
>> retrieved state handle is broken. Try cleaning the state handle store.
>>
>>
>>
>>
>>
>>
>> On Thu, Oct 5, 2017 at 10:13 AM, Vishal Santoshi <
>> vishal.santoshi@gmail.com> wrote:
>>
>>> Also note that  the zookeeper recovery did  ( sadly on the same hdfs
>>> cluster ) also showed the same behavior. It had the pointers to the chk
>>> point  ( I  think that is what it does, keeps metadata of where the
>>> checkpoint etc  ) .  It too decided to keep the recovery file from the
>>> failed state.
>>>
>>> -rw-r--r--   3 root hadoop       7041 2017-10-04 13:55
>>> /flink-recovery/prod/completedCheckpoint6c9096bb9ed4
>>>
>>> -rw-r--r--   3 root hadoop       7044 2017-10-05 10:07
>>> /flink-recovery/prod/completedCheckpoint7c5a19300092
>>>
>>> This is getting a little interesting. What say you :)
>>>
>>>
>>>
>>> On Thu, Oct 5, 2017 at 9:26 AM, Vishal Santoshi <
>>> vishal.santoshi@gmail.com> wrote:
>>>
>>>> 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(SafetyNetWrapperFileSystem.java:111)
>>>>>>>>         at org.apache.flink.runtime.state.filesystem.
>>>>>>>> FsCheckpointStreamFactory.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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