I believe that what differentiates reliable systems is individual components should fail fast when their preconditions aren't met, and other components should be responsible for monitoring them.
If a user of the direct stream thinks that your approach of restarting and ignoring data loss is the right thing to do, they can monitor the job (which they should be doing in any case) and restart. If a user of your library thinks that my approach of failing (so they KNOW there was data loss and can adjust their system) is the right thing to do, how do they do that? On Wed, Dec 2, 2015 at 9:49 PM, Dibyendu Bhattacharya < dibyendu.bhattach...@gmail.com> wrote: > Well, even if you do correct retention and increase speed, > OffsetOutOfRange can still come depends on how your downstream processing > is. And if that happen , there is No Other way to recover old messages . So > best bet here from Streaming Job point of view is to start from earliest > offset rather bring down the streaming job . In many cases goal for a > streaming job is not to shut down and exit in case of any failure. I > believe that is what differentiate a always running streaming job. > > Dibyendu > > On Thu, Dec 3, 2015 at 8:26 AM, Cody Koeninger <c...@koeninger.org> wrote: > >> No, silently restarting from the earliest offset in the case of offset >> out of range exceptions during a streaming job is not the "correct way of >> recovery". >> >> If you do that, your users will be losing data without knowing why. It's >> more like a "way of ignoring the problem without actually addressing it". >> >> The only really correct way to deal with that situation is to recognize >> why it's happening, and either increase your Kafka retention or increase >> the speed at which you are consuming. >> >> On Wed, Dec 2, 2015 at 7:13 PM, Dibyendu Bhattacharya < >> dibyendu.bhattach...@gmail.com> wrote: >> >>> This consumer which I mentioned does not silently throw away data. If >>> offset out of range it start for earliest offset and that is correct way of >>> recovery from this error. >>> >>> Dibyendu >>> On Dec 2, 2015 9:56 PM, "Cody Koeninger" <c...@koeninger.org> wrote: >>> >>>> Again, just to be clear, silently throwing away data because your >>>> system isn't working right is not the same as "recover from any Kafka >>>> leader changes and offset out of ranges issue". >>>> >>>> >>>> >>>> On Tue, Dec 1, 2015 at 11:27 PM, Dibyendu Bhattacharya < >>>> dibyendu.bhattach...@gmail.com> wrote: >>>> >>>>> Hi, if you use Receiver based consumer which is available in >>>>> spark-packages ( >>>>> http://spark-packages.org/package/dibbhatt/kafka-spark-consumer) , >>>>> this has all built in failure recovery and it can recover from any Kafka >>>>> leader changes and offset out of ranges issue. >>>>> >>>>> Here is the package form github : >>>>> https://github.com/dibbhatt/kafka-spark-consumer >>>>> >>>>> >>>>> Dibyendu >>>>> >>>>> On Wed, Dec 2, 2015 at 5:28 AM, swetha kasireddy < >>>>> swethakasire...@gmail.com> wrote: >>>>> >>>>>> How to avoid those Errors with receiver based approach? Suppose we >>>>>> are OK with at least once processing and use receiver based approach >>>>>> which >>>>>> uses ZooKeeper but not query Kafka directly, would these errors(Couldn't >>>>>> find leader offsets for >>>>>> Set([test_stream,5]))) be avoided? >>>>>> >>>>>> On Tue, Dec 1, 2015 at 3:40 PM, Cody Koeninger <c...@koeninger.org> >>>>>> wrote: >>>>>> >>>>>>> KafkaRDD.scala , handleFetchErr >>>>>>> >>>>>>> On Tue, Dec 1, 2015 at 3:39 PM, swetha kasireddy < >>>>>>> swethakasire...@gmail.com> wrote: >>>>>>> >>>>>>>> Hi Cody, >>>>>>>> >>>>>>>> How to look at Option 2(see the following)? Which portion of the >>>>>>>> code in Spark Kafka Direct to look at to handle this issue specific to >>>>>>>> our >>>>>>>> requirements. >>>>>>>> >>>>>>>> >>>>>>>> 2.Catch that exception and somehow force things to "reset" for that >>>>>>>> partition And how would it handle the offsets already calculated in >>>>>>>> the >>>>>>>> backlog (if there is one)? >>>>>>>> >>>>>>>> On Tue, Dec 1, 2015 at 6:51 AM, Cody Koeninger <c...@koeninger.org> >>>>>>>> wrote: >>>>>>>> >>>>>>>>> If you're consistently getting offset out of range exceptions, >>>>>>>>> it's probably because messages are getting deleted before you've >>>>>>>>> processed >>>>>>>>> them. >>>>>>>>> >>>>>>>>> The only real way to deal with this is give kafka more retention, >>>>>>>>> consume faster, or both. >>>>>>>>> >>>>>>>>> If you're just looking for a quick "fix" for an infrequent issue, >>>>>>>>> option 4 is probably easiest. I wouldn't do that automatically / >>>>>>>>> silently, >>>>>>>>> because you're losing data. >>>>>>>>> >>>>>>>>> On Mon, Nov 30, 2015 at 6:22 PM, SRK <swethakasire...@gmail.com> >>>>>>>>> wrote: >>>>>>>>> >>>>>>>>>> Hi, >>>>>>>>>> >>>>>>>>>> So, our Streaming Job fails with the following errors. If you see >>>>>>>>>> the errors >>>>>>>>>> below, they are all related to Kafka losing offsets and >>>>>>>>>> OffsetOutOfRangeException. >>>>>>>>>> >>>>>>>>>> What are the options we have other than fixing Kafka? We would >>>>>>>>>> like to do >>>>>>>>>> something like the following. How can we achieve 1 and 2 with >>>>>>>>>> Spark Kafka >>>>>>>>>> Direct? >>>>>>>>>> >>>>>>>>>> 1.Need to see a way to skip some offsets if they are not >>>>>>>>>> available after the >>>>>>>>>> max retries are reached..in that case there might be data loss. >>>>>>>>>> >>>>>>>>>> 2.Catch that exception and somehow force things to "reset" for >>>>>>>>>> that >>>>>>>>>> partition And how would it handle the offsets already calculated >>>>>>>>>> in the >>>>>>>>>> backlog (if there is one)? >>>>>>>>>> >>>>>>>>>> 3.Track the offsets separately, restart the job by providing the >>>>>>>>>> offsets. >>>>>>>>>> >>>>>>>>>> 4.Or a straightforward approach would be to monitor the log for >>>>>>>>>> this error, >>>>>>>>>> and if it occurs more than X times, kill the job, remove the >>>>>>>>>> checkpoint >>>>>>>>>> directory, and restart. >>>>>>>>>> >>>>>>>>>> ERROR DirectKafkaInputDStream: >>>>>>>>>> ArrayBuffer(kafka.common.UnknownException, >>>>>>>>>> org.apache.spark.SparkException: Couldn't find leader offsets for >>>>>>>>>> Set([test_stream,5])) >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> java.lang.ClassNotFoundException: >>>>>>>>>> kafka.common.NotLeaderForPartitionException >>>>>>>>>> >>>>>>>>>> at >>>>>>>>>> org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1699) >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> java.util.concurrent.RejectedExecutionException: Task >>>>>>>>>> >>>>>>>>>> org.apache.spark.streaming.CheckpointWriter$CheckpointWriteHandler@a48c5a8 >>>>>>>>>> rejected from java.util.concurrent.ThreadPoolExecutor@543258e0 >>>>>>>>>> [Terminated, >>>>>>>>>> pool size = 0, active threads = 0, queued tasks = 0, completed >>>>>>>>>> tasks = >>>>>>>>>> 12112] >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> org.apache.spark.SparkException: Job aborted due to stage >>>>>>>>>> failure: Task 10 >>>>>>>>>> in stage 52.0 failed 4 times, most recent failure: Lost task 10.3 >>>>>>>>>> in stage >>>>>>>>>> 52.0 (TID 255, 172.16.97.97): UnknownReason >>>>>>>>>> >>>>>>>>>> Exception in thread "streaming-job-executor-0" java.lang.Error: >>>>>>>>>> java.lang.InterruptedException >>>>>>>>>> >>>>>>>>>> Caused by: java.lang.InterruptedException >>>>>>>>>> >>>>>>>>>> java.lang.ClassNotFoundException: >>>>>>>>>> kafka.common.OffsetOutOfRangeException >>>>>>>>>> >>>>>>>>>> at >>>>>>>>>> org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1699) >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> org.apache.spark.SparkException: Job aborted due to stage >>>>>>>>>> failure: Task 7 in >>>>>>>>>> stage 33.0 failed 4 times, most recent failure: Lost task 7.3 in >>>>>>>>>> stage 33.0 >>>>>>>>>> (TID 283, 172.16.97.103): UnknownReason >>>>>>>>>> >>>>>>>>>> java.lang.ClassNotFoundException: >>>>>>>>>> kafka.common.OffsetOutOfRangeException >>>>>>>>>> >>>>>>>>>> at >>>>>>>>>> org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1699) >>>>>>>>>> >>>>>>>>>> java.lang.ClassNotFoundException: >>>>>>>>>> kafka.common.OffsetOutOfRangeException >>>>>>>>>> >>>>>>>>>> at >>>>>>>>>> org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1699) >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> -- >>>>>>>>>> View this message in context: >>>>>>>>>> http://apache-spark-user-list.1001560.n3.nabble.com/Recovery-for-Spark-Streaming-Kafka-Direct-in-case-of-issues-with-Kafka-tp25524.html >>>>>>>>>> Sent from the Apache Spark User List mailing list archive at >>>>>>>>>> Nabble.com. >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> --------------------------------------------------------------------- >>>>>>>>>> To unsubscribe, e-mail: user-unsubscr...@spark.apache.org >>>>>>>>>> For additional commands, e-mail: user-h...@spark.apache.org >>>>>>>>>> >>>>>>>>>> >>>>>>>>> >>>>>>>> >>>>>>> >>>>>> >>>>> >>>> >> >
