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From Jan Filipiak <Jan.Filip...@trivago.com>
Subject Re: [DISCUSS]: KIP-161: streams record processing exception handlers
Date Fri, 02 Jun 2017 10:00:11 GMT
Hi

1.
That greatly complicates monitoring.  Fail Fast gives you that when you 
monitor only the lag of all your apps
you are completely covered. With that sort of new application Monitoring 
is very much more complicated as
you know need to monitor fail % of some special apps aswell. In my 
opinion that is a huge downside already.

2.
using a schema regerstry like Avrostuff it might not even be the record 
that is broken, it might be just your app
unable to fetch a schema it needs now know. Maybe you got partitioned 
away from that registry.

3. When you get alerted because of to high fail percentage. what are the 
steps you gonna do?
shut it down to buy time. fix the problem. spend way to much time to 
find a good reprocess offset.
Your timewindows are in bad shape anyways, and you pretty much lost.
This routine is nonsense.

Dead letter queues would be the worst possible addition to the kafka 
toolkit that I can think of. It just doesn't fit the architecture
of having clients falling behind is a valid option.

Further. I mentioned already the only bad pill ive seen so far is crc 
errors. any plans for those?

Best Jan






On 02.06.2017 11:34, Damian Guy wrote:
> I agree with what Matthias has said w.r.t failing fast. There are plenty of
> times when you don't want to fail-fast and must attempt to  make progress.
> The dead-letter queue is exactly for these circumstances. Of course if
> every record is failing, then you probably do want to give up.
>
> On Fri, 2 Jun 2017 at 07:56 Matthias J. Sax <matthias@confluent.io> wrote:
>
>> First a meta comment. KIP discussion should take place on the dev list
>> -- if user list is cc'ed please make sure to reply to both lists. Thanks.
>>
>> Thanks for making the scope of the KIP clear. Makes a lot of sense to
>> focus on deserialization exceptions for now.
>>
>> With regard to corrupted state stores, would it make sense to fail a
>> task and wipe out the store to repair it via recreation from the
>> changelog? That's of course a quite advance pattern, but I want to bring
>> it up to design the first step in a way such that we can get there (if
>> we think it's a reasonable idea).
>>
>> I also want to comment about fail fast vs making progress. I think that
>> fail-fast must not always be the best option. The scenario I have in
>> mind is like this: you got a bunch of producers that feed the Streams
>> input topic. Most producers work find, but maybe one producer miss
>> behaves and the data it writes is corrupted. You might not even be able
>> to recover this lost data at any point -- thus, there is no reason to
>> stop processing but you just skip over those records. Of course, you
>> need to fix the root cause, and thus you need to alert (either via logs
>> of the exception handler directly) and you need to start to investigate
>> to find the bad producer, shut it down and fix it.
>>
>> Here the dead letter queue comes into place. From my understanding, the
>> purpose of this feature is solely enable post debugging. I don't think
>> those record would be fed back at any point in time (so I don't see any
>> ordering issue -- a skipped record, with this regard, is just "fully
>> processed"). Thus, the dead letter queue should actually encode the
>> original records metadata (topic, partition offset etc) to enable such
>> debugging. I guess, this might also be possible if you just log the bad
>> records, but it would be harder to access (you first must find the
>> Streams instance that did write the log and extract the information from
>> there). Reading it from topic is much simpler.
>>
>> I also want to mention the following. Assume you have such a topic with
>> some bad records and some good records. If we always fail-fast, it's
>> going to be super hard to process the good data. You would need to write
>> an extra app that copied the data into a new topic filtering out the bad
>> records (or apply the map() workaround withing stream). So I don't think
>> that failing fast is most likely the best option in production is
>> necessarily, true.
>>
>> Or do you think there are scenarios, for which you can recover the
>> corrupted records successfully? And even if this is possible, it might
>> be a case for reprocessing instead of failing the whole application?
>> Also, if you think you can "repair" a corrupted record, should the
>> handler allow to return a "fixed" record? This would solve the ordering
>> problem.
>>
>>
>>
>> -Matthias
>>
>>
>>
>>
>> On 5/30/17 1:47 AM, Michael Noll wrote:
>>> Thanks for your work on this KIP, Eno -- much appreciated!
>>>
>>> - I think it would help to improve the KIP by adding an end-to-end code
>>> example that demonstrates, with the DSL and with the Processor API, how
>> the
>>> user would write a simple application that would then be augmented with
>> the
>>> proposed KIP changes to handle exceptions.  It should also become much
>>> clearer then that e.g. the KIP would lead to different code paths for the
>>> happy case and any failure scenarios.
>>>
>>> - Do we have sufficient information available to make informed decisions
>> on
>>> what to do next?  For example, do we know in which part of the topology
>> the
>>> record failed? `ConsumerRecord` gives us access to topic, partition,
>>> offset, timestamp, etc., but what about topology-related information
>> (e.g.
>>> what is the associated state store, if any)?
>>>
>>> - Only partly on-topic for the scope of this KIP, but this is about the
>>> bigger picture: This KIP would give users the option to send corrupted
>>> records to dead letter queue (quarantine topic).  But, what pattern would
>>> we advocate to process such a dead letter queue then, e.g. how to allow
>> for
>>> retries with backoff ("If the first record in the dead letter queue fails
>>> again, then try the second record for the time being and go back to the
>>> first record at a later time").  Jay and Jan already alluded to ordering
>>> problems that will be caused by dead letter queues. As I said, retries
>>> might be out of scope but perhaps the implications should be considered
>> if
>>> possible?
>>>
>>> Also, I wrote the text below before reaching the point in the
>> conversation
>>> that this KIP's scope will be limited to exceptions in the category of
>>> poison pills / deserialization errors.  But since Jay brought up user
>> code
>>> errors again, I decided to include it again.
>>>
>>> ----------------------------snip----------------------------
>>> A meta comment: I am not sure about this split between the code for the
>>> happy path (e.g. map/filter/... in the DSL) from the failure path (using
>>> exception handlers).  In Scala, for example, we can do:
>>>
>>>      scala> val computation = scala.util.Try(1 / 0)
>>>      computation: scala.util.Try[Int] =
>>> Failure(java.lang.ArithmeticException: / by zero)
>>>
>>>      scala> computation.getOrElse(42)
>>>      res2: Int = 42
>>>
>>> Another example with Scala's pattern matching, which is similar to
>>> `KStream#branch()`:
>>>
>>>      computation match {
>>>        case scala.util.Success(x) => x * 5
>>>        case scala.util.Failure(_) => 42
>>>      }
>>>
>>> (The above isn't the most idiomatic way to handle this in Scala, but
>> that's
>>> not the point I'm trying to make here.)
>>>
>>> Hence the question I'm raising here is: Do we want to have an API where
>> you
>>> code "the happy path", and then have a different code path for failures
>>> (using exceptions and handlers);  or should we treat both Success and
>>> Failure in the same way?
>>>
>>> I think the failure/exception handling approach (as proposed in this KIP)
>>> is well-suited for errors in the category of deserialization problems aka
>>> poison pills, partly because the (default) serdes are defined through
>>> configuration (explicit serdes however are defined through API calls).
>>>
>>> However, I'm not yet convinced that the failure/exception handling
>> approach
>>> is the best idea for user code exceptions, e.g. if you fail to guard
>>> against NPE in your lambdas or divide a number by zero.
>>>
>>>      scala> val stream = Seq(1, 2, 3, 4, 5)
>>>      stream: Seq[Int] = List(1, 2, 3, 4, 5)
>>>
>>>      // Here: Fallback to a sane default when encountering failed records
>>>      scala>     stream.map(x => Try(1/(3 - x))).flatMap(t =>
>>> Seq(t.getOrElse(42)))
>>>      res19: Seq[Int] = List(0, 1, 42, -1, 0)
>>>
>>>      // Here: Skip over failed records
>>>      scala> stream.map(x => Try(1/(3 - x))).collect{ case Success(s) =>
s
>> }
>>>      res20: Seq[Int] = List(0, 1, -1, 0)
>>>
>>> The above is more natural to me than using error handlers to define how
>> to
>>> deal with failed records (here, the value `3` causes an arithmetic
>>> exception).  Again, it might help the KIP if we added an end-to-end
>> example
>>> for such user code errors.
>>> ----------------------------snip----------------------------
>>>
>>>
>>>
>>>
>>> On Tue, May 30, 2017 at 9:24 AM, Jan Filipiak <Jan.Filipiak@trivago.com>
>>> wrote:
>>>
>>>> Hi Jay,
>>>>
>>>> Eno mentioned that he will narrow down the scope to only ConsumerRecord
>>>> deserialisation.
>>>>
>>>> I am working with Database Changelogs only. I would really not like to
>> see
>>>> a dead letter queue or something
>>>> similliar. how am I expected to get these back in order. Just grind to
>>>> hold an call me on the weekend. I'll fix it
>>>> then in a few minutes rather spend 2 weeks ordering dead letters. (where
>>>> reprocessing might be even the faster fix)
>>>>
>>>> Best Jan
>>>>
>>>>
>>>>
>>>>
>>>> On 29.05.2017 20:23, Jay Kreps wrote:
>>>>
>>>>>      - I think we should hold off on retries unless we have worked out
>> the
>>>>>      full usage pattern, people can always implement their own. I think
>>>>> the idea
>>>>>      is that you send the message to some kind of dead letter queue and
>>>>> then
>>>>>      replay these later. This obviously destroys all semantic guarantees
>>>>> we are
>>>>>      working hard to provide right now, which may be okay.
>>>>>
>>>>
>>


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