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From Robert Metzger <rmetz...@apache.org>
Subject Re: Frequent exceptions killing streaming job
Date Wed, 20 Jan 2016 10:59:43 GMT
Hey Nick,

I had a discussion with Stephan Ewen on how we could resolve the issue.
I filed a JIRA with our suggested approach:
https://issues.apache.org/jira/browse/FLINK-3264

By handling this directly in the KafkaConsumer, we would avoid fetching
data we can not handle anyways (discarding in the deserialization schema
would be more inefficient).

Let us know what you think about our suggested approach.

Sadly, it seems that the Kafka 0.9 consumer API does not yet support
requesting the latest offset of a TopicPartition. I'll ask about this on
their ML.




On Sun, Jan 17, 2016 at 8:28 PM, Nick Dimiduk <ndimiduk@gmail.com> wrote:

> On Sunday, January 17, 2016, Stephan Ewen <sewen@apache.org> wrote:
>
>> I agree, real time streams should never go down.
>>
>
>  Glad to hear that :)
>
>
>> [snip] Both should be supported.
>>
>
> Agreed.
>
>
>> Since we interpret streaming very broadly (also including analysis of
>> historic streams or timely data), the "backpressure/catch-up" mode seemed
>> natural as the first one to implement.
>>
>
> Indeed, this is what my job is doing. I have set it to, lacking a valid
> offset, start from the beginning. I have to presume that in my case the
> stream data is expiring faster than my consumers can keep up. However I
> haven't investigated proper monitoring yet.
>
>
>> The "load shedding" variant can probably even be realized in the Kafka
>> consumer, without complex modifications to the core Flink runtime itself.
>>
>
> I agree here as well. Indeed, this exception is being thrown from the
> consumer, not the runtime.
>
>
>
>> On Sun, Jan 17, 2016 at 12:42 AM, Nick Dimiduk <ndimiduk@gmail.com>
>> wrote:
>>
>>> This goes back to the idea that streaming applications should never go
>>> down. I'd much rather consume at max capacity and knowingly drop some
>>> portion of the incoming pipe than have the streaming job crash. Of course,
>>> once the job itself is robust, I still need the runtime to be robust --
>>> YARN vs (potential) Mesos vs standalone cluster will be my next
>>> consideration.
>>>
>>> I can share some details about my setup, but not at this time; in part
>>> because I don't have my metrics available at the moment and in part because
>>> this is a public, archived list.
>>>
>>> On Sat, Jan 16, 2016 at 8:23 AM, Stephan Ewen <sewen@apache.org> wrote:
>>>
>>>> @Robert: Is it possible to add a "fallback" strategy to the consumer?
>>>> Something like "if offsets cannot be found, use latest"?
>>>>
>>>> I would make this an optional feature to activate. I would think it is
>>>> quite surprising to users if records start being skipped in certain
>>>> situations. But I can see that this would be desirable sometimes.
>>>>
>>>> More control over skipping the records could be something to implement
>>>> in an extended version of the Kafka Consumer. A user could define a policy
>>>> that, in case consumer falls behind producer more than X (offsets), it
>>>> starts requesting the latest offsets (rather than the following), thereby
>>>> skipping a bunch of records.
>>>>
>>>>
>>>>
>>>> On Sat, Jan 16, 2016 at 3:14 PM, Robert Metzger <rmetzger@apache.org>
>>>> wrote:
>>>>
>>>>> Hi Nick,
>>>>>
>>>>> I'm sorry you ran into the issue. Is it possible that Flink's Kafka
>>>>> consumer falls back in the topic so far that the offsets it's requesting
>>>>> are invalid?
>>>>>
>>>>> For that, the retention time of Kafka has to be pretty short.
>>>>>
>>>>> Skipping records under load is something currently not supported by
>>>>> Flink itself. The only idea I had for handling this would be to give
the
>>>>> DeserializationSchema a call back to request the latest offset from Kafka
>>>>> to determine the lag. With that, the schema could determine a "dropping
>>>>> rate" to catch up.
>>>>> What would you as an application developer expect to handle the
>>>>> situation?
>>>>>
>>>>>
>>>>> Just out of curiosity: What's the throughput you have on the Kafka
>>>>> topic?
>>>>>
>>>>>
>>>>> On Fri, Jan 15, 2016 at 10:13 PM, Nick Dimiduk <ndimiduk@gmail.com>
>>>>> wrote:
>>>>>
>>>>>> Hi folks,
>>>>>>
>>>>>> I have a streaming job that consumes from of a kafka topic. The topic
>>>>>> is pretty active so the local-mode single worker is obviously not
able to
>>>>>> keep up with the fire-hose. I expect the job to skip records and
continue
>>>>>> on. However, I'm getting an exception from the LegacyFetcher which
kills
>>>>>> the job. This is very much *not* what I want. Any thoughts? The only
thing
>>>>>> I find when I search for this error message is a link back to FLINK-2656.
>>>>>> I'm running roughly 0.10-release/HEAD.
>>>>>>
>>>>>> Thanks a lot,
>>>>>> Nick
>>>>>>
>>>>>> java.lang.Exception: Found invalid offsets more than once in
>>>>>> partitions [FetchPartition {partition=X, offset=Y}] Exceptions:
>>>>>>         at
>>>>>> org.apache.flink.streaming.connectors.kafka.internals.LegacyFetcher.run(LegacyFetcher.java:242)
>>>>>>         at
>>>>>> org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer.run(FlinkKafkaConsumer.java:399)
>>>>>>         at
>>>>>> org.apache.flink.streaming.api.operators.StreamSource.run(StreamSource.java:58)
>>>>>>         at
>>>>>> org.apache.flink.streaming.runtime.tasks.SourceStreamTask.run(SourceStreamTask.java:55)
>>>>>>         at
>>>>>> org.apache.flink.streaming.runtime.tasks.StreamTask.invoke(StreamTask.java:218)
>>>>>>         at
>>>>>> org.apache.flink.runtime.taskmanager.Task.run(Task.java:584)
>>>>>>         at java.lang.Thread.run(Thread.java:745)
>>>>>> Caused by: java.lang.RuntimeException: Found invalid offsets more
>>>>>> than once in partitions [FetchPartition {partition=X, offset=Y}]
>>>>>> Exceptions:
>>>>>>         at
>>>>>> org.apache.flink.streaming.connectors.kafka.internals.LegacyFetcher$SimpleConsumerThread.run(LegacyFetcher.java:412)
>>>>>>
>>>>>>
>>>>>
>>>>
>>>
>>

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