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From Stephan Ewen <se...@apache.org>
Subject Re: Frequent exceptions killing streaming job
Date Fri, 26 Feb 2016 11:17:25 GMT
Was the contended lock part of Flink's runtime, or the application code? If
it was part of the Flink Runtime, can you share what you found?

On Thu, Feb 25, 2016 at 6:03 PM, Nick Dimiduk <ndimiduk@gmail.com> wrote:

> For what it's worth, I dug into the TM logs and found that this exception
> was not the root cause, merely a symptom of other backpressure building in
> the flow (actually, lock contention in another part of the stack). While
> Flink was helpful in finding and bubbling up this stack to the UI, it was
> ultimately missleading, caused me to overlook proper evaluation of the
> failure.
>
> On Wed, Jan 20, 2016 at 2:59 AM, Robert Metzger <rmetzger@apache.org>
> wrote:
>
>> 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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