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From Eno Thereska <eno.there...@gmail.com>
Subject Re: [DISCUSS]: KIP-161: streams record processing exception handlers
Date Wed, 07 Jun 2017 20:49:19 GMT
Comments inline:

> On 5 Jun 2017, at 18:19, Jan Filipiak <Jan.Filipiak@trivago.com> wrote:
> Hi
> just my few thoughts
> On 05.06.2017 11:44, Eno Thereska wrote:
>> Hi there,
>> Sorry for the late reply, I was out this past week. Looks like good progress was
made with the discussions either way. Let me recap a couple of points I saw into one big reply:
>> 1. Jan mentioned CRC errors. I think this is a good point. As these happen in Kafka,
before Kafka Streams gets a chance to inspect anything, I'd like to hear the opinion of more
Kafka folks like Ismael or Jason on this one. Currently the documentation is not great with
what to do once a CRC check has failed. From looking at the code, it looks like the client
gets a KafkaException (bubbled up from the fetcher) and currently we in streams catch this
as part of poll() and fail. It might be advantageous to treat CRC handling in a similar way
to serialisation handling (e.g., have the option to fail/skip). Let's see what the other folks
say. Worst-case we can do a separate KIP for that if it proved too hard to do in one go.
> there is no reasonable way to "skip" a crc error. How can you know the length you read
was anything reasonable? you might be completely lost inside your response.

On the client side, every record received is checked for validity. As it happens, if the CRC
check fails the exception is wrapped with a KafkaException that is thrown all the way to poll().
Assuming we change that and poll() throws a CRC exception, I was thinking we could treat it
similarly to a deserialize exception and pass it to the exception handler to decide what to
do. Default would be to fail. This might need a Kafka KIP btw and can be done separately from
this KIP, but Jan, would you find this useful?

>> At a minimum, handling this type of exception will need to involve the exactly-once
(EoS) logic. We'd still allow the option of failing or skipping, but EoS would need to clean
up by rolling back all the side effects from the processing so far. Matthias, how does this
> Eos will not help the record might be 5,6 repartitions down into the topology. I haven't
followed but I pray you made EoS optional! We don't need this and we don't want this and we
will turn it off if it comes. So I wouldn't recommend relying on it. The option to turn it
off is better than forcing it and still beeing unable to rollback badpills (as explained before)

Yeah as Matthias mentioned EoS is optional.


>> 6. Will add an end-to-end example as Michael suggested.
>> Thanks
>> Eno
>>> On 4 Jun 2017, at 02:35, Matthias J. Sax <matthias@confluent.io> wrote:
>>> What I don't understand is this:
>>>> From there on its the easiest way forward: fix, redeploy, start => done
>>> If you have many producers that work fine and a new "bad" producer
>>> starts up and writes bad data into your input topic, your Streams app
>>> dies but all your producers, including the bad one, keep writing.
>>> Thus, how would you fix this, as you cannot "remove" the corrupted date
>>> from the topic? It might take some time to identify the root cause and
>>> stop the bad producer. Up to this point you get good and bad data into
>>> your Streams input topic. If Streams app in not able to skip over those
>>> bad records, how would you get all the good data from the topic? Not
>>> saying it's not possible, but it's extra work copying the data with a
>>> new non-Streams consumer-producer-app into a new topic and than feed
>>> your Streams app from this new topic -- you also need to update all your
>>> upstream producers to write to the new topic.
>>> Thus, if you want to fail fast, you can still do this. And after you
>>> detected and fixed the bad producer you might just reconfigure your app
>>> to skip bad records until it reaches the good part of the data.
>>> Afterwards, you could redeploy with fail-fast again.
>>> Thus, for this pattern, I actually don't see any reason why to stop the
>>> Streams app at all. If you have a callback, and use the callback to
>>> raise an alert (and maybe get the bad data into a bad record queue), it
>>> will not take longer to identify and stop the "bad" producer. But for
>>> this case, you have zero downtime for your Streams app.
>>> This seems to be much simpler. Or do I miss anything?
>>> Having said this, I agree that the "threshold based callback" might be
>>> questionable. But as you argue for strict "fail-fast", I want to argue
>>> that this must not always be the best pattern to apply and that the
>>> overall KIP idea is super useful from my point of view.
>>> -Matthias
>>> On 6/3/17 11:57 AM, Jan Filipiak wrote:
>>>> Could not agree more!
>>>> But then I think the easiest is still: print exception and die.
>>>> From there on its the easiest way forward: fix, redeploy, start => done
>>>> All the other ways to recover a pipeline that was processing partially
>>>> all the time
>>>> and suddenly went over a "I cant take it anymore" threshold is not
>>>> straight forward IMO.
>>>> How to find the offset, when it became to bad when it is not the latest
>>>> commited one?
>>>> How to reset there? with some reasonable stuff in your rockses?
>>>> If one would do the following. The continuing Handler would measure for
>>>> a threshold and
>>>> would terminate after a certain threshold has passed (per task). Then
>>>> one can use offset commit/ flush intervals
>>>> to make reasonable assumption of how much is slipping by + you get an
>>>> easy recovery when it gets to bad
>>>> + you could also account for "in processing" records.
>>>> Setting this threshold to zero would cover all cases with 1
>>>> implementation. It is still beneficial to have it pluggable
>>>> Again CRC-Errors are the only bad pills we saw in production for now.
>>>> Best Jan
>>>> On 02.06.2017 17:37, Jay Kreps wrote:
>>>>> Jan, I agree with you philosophically. I think one practical challenge
>>>>> has
>>>>> to do with data formats. Many people use untyped events, so there is
>>>>> simply
>>>>> no guarantee on the form of the input. E.g. many companies use JSON
>>>>> without
>>>>> any kind of schema so it becomes very hard to assert anything about the
>>>>> input which makes these programs very fragile to the "one accidental
>>>>> message publication that creates an unsolvable problem.
>>>>> For that reason I do wonder if limiting to just serialization actually
>>>>> gets
>>>>> you a useful solution. For JSON it will help with the problem of
>>>>> non-parseable JSON, but sounds like it won't help in the case where the
>>>>> JSON is well-formed but does not have any of the fields you expect and
>>>>> depend on for your processing. I expect the reason for limiting the scope
>>>>> is it is pretty hard to reason about correctness for anything that
>>>>> stops in
>>>>> the middle of processing an operator DAG?
>>>>> -Jay
>>>>> On Fri, Jun 2, 2017 at 4:50 AM, Jan Filipiak <Jan.Filipiak@trivago.com>
>>>>> wrote:
>>>>>> IMHO your doing it wrong then. + building to much support into the
>>>>>> eco system is very counterproductive in fostering a happy userbase
>>>>>> On 02.06.2017 13:15, Damian Guy wrote:
>>>>>>> Jan, you have a choice to Fail fast if you want. This is about
>>>>>>> people options and there are times when you don't want to fail
>>>>>>> On Fri, 2 Jun 2017 at 11:00 Jan Filipiak <Jan.Filipiak@trivago.com>
>>>>>>> wrote:
>>>>>>> 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
>>>>>>>> away from that registry.
>>>>>>>> 3. When you get alerted because of to high fail percentage.
>>>>>>>> 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.
>>>>>>>>> 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
>>>>>>>>>> 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
>>>>>>>>>>> - 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 *
>>>>>>>>>>>         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
>>>>>>>>>> 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
>>>>>>>>>> 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,
>>>>>>>>>>>       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
>>>>>>>>>>>> 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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