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.santo...@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.santo...@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.santo...@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 <fhue...@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.santo...@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.santo...@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. >>>>>> >>>>> >>>>> >>>> >>> >> >
