Re: [DISCUSS]: KIP-161: streams record processing exception handlers

["Matthias J. Sax" <ma...@confluent.io>]

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 <Ja...@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.
>>>
>>
>>
> 

Fwd: Re: [DISCUSS]: KIP-161: streams record processing exception handlers

["Matthias J. Sax" <ma...@confluent.io>]

Should go to dev list, too.


-------- Forwarded Message --------
Subject: Re: [DISCUSS]: KIP-161: streams record processing exception
handlers
Date: Mon, 5 Jun 2017 19:19:42 +0200
From: Jan Filipiak <Ja...@trivago.com>
Reply-To: users@kafka.apache.org
To: users@kafka.apache.org

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.
> 2. Damian has convinced me that the KIP should just be for deserialisation from the network, not from local state store DBs. For the latter we'll follow the current way of failing since the DB is likely corrupt.
>
> 3. Dead letter queue option. There was never any intention here to do anything super clever like attempt to re-inject the failed records from the dead letter queue back into the system. Reasoning about when that'd be useful in light of all sorts of semantic breakings would be hard (arguably impossible). The idea was to just have a place to have all these dead records to help with subsequent debugging. We could also just log a whole bunch of info for a poison pill record and not have a dead letter queue at all. Perhaps that's a better, simpler, starting point.
+1
>
> 4. Agree with Jay on style, a DefaultHandler with some config options. Will add options to KIP. Also as part of this let's remove the threshold logger since it gets complex and arguably the ROI is low.
>
> 5. Jay's JSON example, where serialisation passes but the JSON message doesn't have the expected fields, is an interesting one. It's a bit complicated to handle this in the middle of processing. For example, some operators in the DAG might actually find the needed JSON fields and make progress, but other operators, for the same record, might not find their fields and will throw an exception.
>
> 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 sound?
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)
>
> 6. Will add an end-to-end example as Michael suggested.
>
> Thanks
> Eno
>
>
>
>> On 4 Jun 2017, at 02:35, Matthias J. Sax <ma...@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 <Ja...@trivago.com>
>>>> wrote:
>>>>
>>>>> IMHO your doing it wrong then. + building to much support into the kafka
>>>>> 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 giving
>>>>>> people options and there are times when you don't want to fail fast.
>>>>>>
>>>>>>
>>>>>> On Fri, 2 Jun 2017 at 11:00 Jan Filipiak <Ja...@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 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 <ma...@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.
>>>>>>>>>>>>
>>>>>>>>>>>>

Fwd: Re: [DISCUSS]: KIP-161: streams record processing exception handlers

["Matthias J. Sax" <ma...@confluent.io>]

Should go to dev list, too.


-------- Forwarded Message --------
Subject: Re: [DISCUSS]: KIP-161: streams record processing exception
handlers
Date: Mon, 5 Jun 2017 19:19:42 +0200
From: Jan Filipiak <Ja...@trivago.com>
Reply-To: users@kafka.apache.org
To: users@kafka.apache.org

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.
> 2. Damian has convinced me that the KIP should just be for deserialisation from the network, not from local state store DBs. For the latter we'll follow the current way of failing since the DB is likely corrupt.
>
> 3. Dead letter queue option. There was never any intention here to do anything super clever like attempt to re-inject the failed records from the dead letter queue back into the system. Reasoning about when that'd be useful in light of all sorts of semantic breakings would be hard (arguably impossible). The idea was to just have a place to have all these dead records to help with subsequent debugging. We could also just log a whole bunch of info for a poison pill record and not have a dead letter queue at all. Perhaps that's a better, simpler, starting point.
+1
>
> 4. Agree with Jay on style, a DefaultHandler with some config options. Will add options to KIP. Also as part of this let's remove the threshold logger since it gets complex and arguably the ROI is low.
>
> 5. Jay's JSON example, where serialisation passes but the JSON message doesn't have the expected fields, is an interesting one. It's a bit complicated to handle this in the middle of processing. For example, some operators in the DAG might actually find the needed JSON fields and make progress, but other operators, for the same record, might not find their fields and will throw an exception.
>
> 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 sound?
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)
>
> 6. Will add an end-to-end example as Michael suggested.
>
> Thanks
> Eno
>
>
>
>> On 4 Jun 2017, at 02:35, Matthias J. Sax <ma...@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 <Ja...@trivago.com>
>>>> wrote:
>>>>
>>>>> IMHO your doing it wrong then. + building to much support into the kafka
>>>>> 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 giving
>>>>>> people options and there are times when you don't want to fail fast.
>>>>>>
>>>>>>
>>>>>> On Fri, 2 Jun 2017 at 11:00 Jan Filipiak <Ja...@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 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 <ma...@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.
>>>>>>>>>>>>
>>>>>>>>>>>>

Re: [DISCUSS]: KIP-161: streams record processing exception handlers

[Eno Thereska <en...@gmail.com>]

As part of the PR review we decided to add a metric to keep track of the number of skipped records due to deserialization. I updated the KIP to reflect that.

Thanks
Eno

> On Jun 23, 2017, at 10:59 AM, Eno Thereska <en...@gmail.com> wrote:
> 
> Done, thanks. I'll open a vote thread now.
> 
> Eno
>> On 23 Jun 2017, at 02:15, Matthias J. Sax <matthias@confluent.io <ma...@confluent.io>> wrote:
>> 
>> I also think, that one config is better, with two default
>> implementations: failing and log-and-continue
>> 
>> However, I think we should fail by default. Similar to timestamp
>> extractor as "silent" data loss is no good default behavior IMHO.
>> 
>> 
>> -Matthias
>> 
>> On 6/22/17 11:00 AM, Eno Thereska wrote:
>>> Answers inline: 
>>> 
>>>> On 22 Jun 2017, at 03:26, Guozhang Wang <wangguoz@gmail.com <ma...@gmail.com>> wrote:
>>>> 
>>>> Thanks for the updated KIP, some more comments:
>>>> 
>>>> 1.The config name is "default.deserialization.exception.handler" while the
>>>> interface class name is "RecordExceptionHandler", which is more general
>>>> than the intended purpose. Could we rename the class name accordingly?
>>> 
>>> Sure.
>>> 
>>> 
>>>> 
>>>> 2. Could you describe the full implementation of "DefaultExceptionHandler",
>>>> currently it is not clear to me how it is implemented with the configured
>>>> value.
>>>> 
>>>> In addition, I think we do not need to include an additional
>>>> "DEFAULT_DESERIALIZATION_EXCEPTION_RESPONSE_CONFIG" as the configure()
>>>> function is mainly used for users to pass any customized parameters that is
>>>> out of the Streams library; plus adding such additional config sounds
>>>> over-complicated for a default exception handler. Instead I'd suggest we
>>>> just provide two handlers (or three if people feel strong about the
>>>> LogAndThresholdExceptionHandler), one for FailOnExceptionHandler and one
>>>> for LogAndContinueOnExceptionHandler. And we can set
>>>> LogAndContinueOnExceptionHandler
>>>> by default.
>>>> 
>>> 
>>> That's what I had originally. Jay mentioned he preferred one default class, with config options.
>>> So with that approach, you'd have 2 config options, one for failing, one for continuing, and the one
>>> exception handler would take those options during it's configure() call.
>>> 
>>> After checking the other exception handlers in the code, I might revert back to what I originally had (2 default handlers) 
>>> as Guozhang also re-suggests, but still have the interface extend Configurable. Guozhang, you ok with that? In that case
>>> there is no need for the response config option.
>>> 
>>> Thanks
>>> Eno
>>> 
>>> 
>>>> 
>>>> Guozhang
>>>> 
>>>> 
>>>> 
>>>> 
>>>> 
>>>> 
>>>> 
>>>> 
>>>> On Wed, Jun 21, 2017 at 1:39 AM, Eno Thereska <eno.thereska@gmail.com <ma...@gmail.com> <mailto:eno.thereska@gmail.com <ma...@gmail.com>>>
>>>> wrote:
>>>> 
>>>>> Thanks Guozhang,
>>>>> 
>>>>> I’ve updated the KIP and hopefully addressed all the comments so far. In
>>>>> the process also changed the name of the KIP to reflect its scope better:
>>>>> https://cwiki.apache.org/confluence/display/KAFKA/KIP-161%3A+streams+ <https://cwiki.apache.org/confluence/display/KAFKA/KIP-161%3A+streams+><https://cwiki.apache.org/confluence/display/KAFKA/KIP-161%3A+streams+ <https://cwiki.apache.org/confluence/display/KAFKA/KIP-161%3A+streams+>>
>>>>> deserialization+exception+handlers <https://cwiki.apache.org/ <https://cwiki.apache.org/> <https://cwiki.apache.org/ <https://cwiki.apache.org/>>
>>>>> confluence/display/KAFKA/KIP-161:+streams+deserialization+
>>>>> exception+handlers>
>>>>> 
>>>>> Any other feedback appreciated, otherwise I’ll start the vote soon.
>>>>> 
>>>>> Thanks
>>>>> Eno
>>>>> 
>>>>>> On Jun 12, 2017, at 6:28 AM, Guozhang Wang <wangguoz@gmail.com <ma...@gmail.com>> wrote:
>>>>>> 
>>>>>> Eno, Thanks for bringing this proposal up and sorry for getting late on
>>>>>> this. Here are my two cents:
>>>>>> 
>>>>>> 1. First some meta comments regarding "fail fast" v.s. "making
>>>>> progress". I
>>>>>> agree that in general we should better "enforce user to do the right
>>>>> thing"
>>>>>> in system design, but we also need to keep in mind that Kafka is a
>>>>>> multi-tenant system, i.e. from a Streams app's pov you probably would not
>>>>>> control the whole streaming processing pipeline end-to-end. E.g. Your
>>>>> input
>>>>>> data may not be controlled by yourself; it could be written by another
>>>>> app,
>>>>>> or another team in your company, or even a different organization, and if
>>>>>> an error happens maybe you cannot fix "to do the right thing" just by
>>>>>> yourself in time. In such an environment I think it is important to leave
>>>>>> the door open to let users be more resilient. So I find the current
>>>>>> proposal which does leave the door open for either fail-fast or make
>>>>>> progress quite reasonable.
>>>>>> 
>>>>>> 2. On the other hand, if the question is whether we should provide a
>>>>>> built-in "send to bad queue" handler from the library, I think that might
>>>>>> be an overkill: with some tweaks (see my detailed comments below) on the
>>>>>> API we can allow users to implement such handlers pretty easily. In
>>>>> fact, I
>>>>>> feel even "LogAndThresholdExceptionHandler" is not necessary as a
>>>>> built-in
>>>>>> handler, as it would then require users to specify the threshold via
>>>>>> configs, etc. I think letting people provide such "eco-libraries" may be
>>>>>> better.
>>>>>> 
>>>>>> 3. Regarding the CRC error: today we validate CRC on both the broker end
>>>>>> upon receiving produce requests and on consumer end upon receiving fetch
>>>>>> responses; and if the CRC validation fails in the former case it would
>>>>> not
>>>>>> be appended to the broker logs. So if we do see a CRC failure on the
>>>>>> consumer side it has to be that either we have a flipped bit on the
>>>>> broker
>>>>>> disks or over the wire. For the first case it is fatal while for the
>>>>> second
>>>>>> it is retriable. Unfortunately we cannot tell which case it is when
>>>>> seeing
>>>>>> CRC validation failures. But in either case, just skipping and making
>>>>>> progress seems not a good choice here, and hence I would personally
>>>>> exclude
>>>>>> these errors from the general serde errors to NOT leave the door open of
>>>>>> making progress.
>>>>>> 
>>>>>> Currently such errors are thrown as KafkaException that wraps an
>>>>>> InvalidRecordException, which may be too general and we could consider
>>>>> just
>>>>>> throwing the InvalidRecordException directly. But that could be an
>>>>>> orthogonal discussion if we agrees that CRC failures should not be
>>>>>> considered in this KIP.
>>>>>> 
>>>>>> ----------------
>>>>>> 
>>>>>> Now some detailed comments:
>>>>>> 
>>>>>> 4. Could we consider adding the processor context in the handle()
>>>>> function
>>>>>> as well? This context will be wrapping as the source node that is about
>>>>> to
>>>>>> process the record. This could expose more info like which task / source
>>>>>> node sees this error, which timestamp of the message, etc, and also can
>>>>>> allow users to implement their handlers by exposing some metrics, by
>>>>>> calling context.forward() to implement the "send to bad queue" behavior
>>>>> etc.
>>>>>> 
>>>>>> 5. Could you add the string name of
>>>>>> StreamsConfig.DEFAULT_RECORD_EXCEPTION_HANDLER as well in the KIP?
>>>>>> Personally I find "default" prefix a bit misleading since we do not allow
>>>>>> users to override it per-node yet. But I'm okay either way as I can see
>>>>> we
>>>>>> may extend it in the future and probably would like to not rename the
>>>>>> config again. Also from the experience of `default partitioner` and
>>>>>> `default timestamp extractor` we may also make sure that the passed in
>>>>>> object can be either a string "class name" or a class object?
>>>>>> 
>>>>>> 
>>>>>> Guozhang
>>>>>> 
>>>>>> 
>>>>>> On Wed, Jun 7, 2017 at 2:16 PM, Jan Filipiak <Jan.Filipiak@trivago.com <ma...@trivago.com>>
>>>>>> wrote:
>>>>>> 
>>>>>>> Hi Eno,
>>>>>>> 
>>>>>>> On 07.06.2017 22:49, Eno Thereska wrote:
>>>>>>> 
>>>>>>>> Comments inline:
>>>>>>>> 
>>>>>>>> On 5 Jun 2017, at 18:19, Jan Filipiak <Jan.Filipiak@trivago.com <ma...@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?
>>>>>>>> 
>>>>>>> I don't think so. IMO you can not reasonably continue parsing when the
>>>>>>> checksum of a message is not correct. If you are not sure you got the
>>>>>>> correct length, how can you be sure to find the next record? I would
>>>>> always
>>>>>>> straight fail in all cases. Its to hard for me to understand why one
>>>>> would
>>>>>>> try to continue. I mentioned CRC's because thats the only bad pills I
>>>>> ever
>>>>>>> saw so far. But I am happy that it just stopped and I could check what
>>>>> was
>>>>>>> going on. This will also be invasive in the client code then.
>>>>>>> 
>>>>>>> If you ask me, I am always going to vote for "grind to halt" let the
>>>>>>> developers see what happened and let them fix it. It helps building good
>>>>>>> kafka experiences and better software and architectures. For me this is:
>>>>>>> "force the user todo the right thing". https://youtu.be/aAb7hSCtvGw? <https://youtu.be/aAb7hSCtvGw?>
>>>>> t=374
>>>>>>> eg. not letting unexpected input slip by.  Letting unexpected input
>>>>> slip by
>>>>>>> is what bought us 15+years of war of all sorts of ingestion attacks. I
>>>>>>> don't even dare to estimate how many missingrecords-search-teams going
>>>>> be
>>>>>>> formed, maybe some hackerone for stream apps :D
>>>>>>> 
>>>>>>> Best Jan
>>>>>>> 
>>>>>>> 
>>>>>>>> 
>>>>>>>>>> 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 sound?
>>>>>>>>>> 
>>>>>>>>> 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.
>>>>>>>> 
>>>>>>>> Thanks,
>>>>>>>> Eno
>>>>>>>> 
>>>>>>>> 
>>>>>>>> 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 <ma...@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 <ma...@trivago.com>>
>>>>>>>>>>>>> wrote:
>>>>>>>>>>>>> 
>>>>>>>>>>>>> IMHO your doing it wrong then. + building to much support into the
>>>>>>>>>>>>>> kafka
>>>>>>>>>>>>>> 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
>>>>>>>>>>>>>>> giving
>>>>>>>>>>>>>>> people options and there are times when you don't want to fail
>>>>>>>>>>>>>>> fast.
>>>>>>>>>>>>>>> 
>>>>>>>>>>>>>>> 
>>>>>>>>>>>>>>> On Fri, 2 Jun 2017 at 11:00 Jan Filipiak <
>>>>> Jan.Filipiak@trivago.com <ma...@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
>>>>>>>>>>>>>>>> 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 <ma...@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 <ma...@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.
>>>>>>>>>>>>>>>>>>>>> 
>>>>>>>>>>>>>>>>>>>>> 
>>>>>>>>>>>>>>>>>>>>> 
>>>>>>> 
>>>>>> 
>>>>>> 
>>>>>> --
>>>>>> -- Guozhang
>>>>> 
>>>>> 
>>>> 
>>>> 
>>>> -- 
>>>> -- Guozhang
> 

Re: [DISCUSS]: KIP-161: streams record processing exception handlers

[Eno Thereska <en...@gmail.com>]

Done, thanks. I'll open a vote thread now.

Eno
> On 23 Jun 2017, at 02:15, Matthias J. Sax <ma...@confluent.io> wrote:
> 
> I also think, that one config is better, with two default
> implementations: failing and log-and-continue
> 
> However, I think we should fail by default. Similar to timestamp
> extractor as "silent" data loss is no good default behavior IMHO.
> 
> 
> -Matthias
> 
> On 6/22/17 11:00 AM, Eno Thereska wrote:
>> Answers inline: 
>> 
>>> On 22 Jun 2017, at 03:26, Guozhang Wang <wa...@gmail.com> wrote:
>>> 
>>> Thanks for the updated KIP, some more comments:
>>> 
>>> 1.The config name is "default.deserialization.exception.handler" while the
>>> interface class name is "RecordExceptionHandler", which is more general
>>> than the intended purpose. Could we rename the class name accordingly?
>> 
>> Sure.
>> 
>> 
>>> 
>>> 2. Could you describe the full implementation of "DefaultExceptionHandler",
>>> currently it is not clear to me how it is implemented with the configured
>>> value.
>>> 
>>> In addition, I think we do not need to include an additional
>>> "DEFAULT_DESERIALIZATION_EXCEPTION_RESPONSE_CONFIG" as the configure()
>>> function is mainly used for users to pass any customized parameters that is
>>> out of the Streams library; plus adding such additional config sounds
>>> over-complicated for a default exception handler. Instead I'd suggest we
>>> just provide two handlers (or three if people feel strong about the
>>> LogAndThresholdExceptionHandler), one for FailOnExceptionHandler and one
>>> for LogAndContinueOnExceptionHandler. And we can set
>>> LogAndContinueOnExceptionHandler
>>> by default.
>>> 
>> 
>> That's what I had originally. Jay mentioned he preferred one default class, with config options.
>> So with that approach, you'd have 2 config options, one for failing, one for continuing, and the one
>> exception handler would take those options during it's configure() call.
>> 
>> After checking the other exception handlers in the code, I might revert back to what I originally had (2 default handlers) 
>> as Guozhang also re-suggests, but still have the interface extend Configurable. Guozhang, you ok with that? In that case
>> there is no need for the response config option.
>> 
>> Thanks
>> Eno
>> 
>> 
>>> 
>>> Guozhang
>>> 
>>> 
>>> 
>>> 
>>> 
>>> 
>>> 
>>> 
>>> On Wed, Jun 21, 2017 at 1:39 AM, Eno Thereska <eno.thereska@gmail.com <ma...@gmail.com> <mailto:eno.thereska@gmail.com <ma...@gmail.com>>>
>>> wrote:
>>> 
>>>> Thanks Guozhang,
>>>> 
>>>> I’ve updated the KIP and hopefully addressed all the comments so far. In
>>>> the process also changed the name of the KIP to reflect its scope better:
>>>> https://cwiki.apache.org/confluence/display/KAFKA/KIP-161%3A+streams+ <https://cwiki.apache.org/confluence/display/KAFKA/KIP-161%3A+streams+><https://cwiki.apache.org/confluence/display/KAFKA/KIP-161%3A+streams+ <https://cwiki.apache.org/confluence/display/KAFKA/KIP-161%3A+streams+>>
>>>> deserialization+exception+handlers <https://cwiki.apache.org/ <https://cwiki.apache.org/> <https://cwiki.apache.org/ <https://cwiki.apache.org/>>
>>>> confluence/display/KAFKA/KIP-161:+streams+deserialization+
>>>> exception+handlers>
>>>> 
>>>> Any other feedback appreciated, otherwise I’ll start the vote soon.
>>>> 
>>>> Thanks
>>>> Eno
>>>> 
>>>>> On Jun 12, 2017, at 6:28 AM, Guozhang Wang <wangguoz@gmail.com <ma...@gmail.com>> wrote:
>>>>> 
>>>>> Eno, Thanks for bringing this proposal up and sorry for getting late on
>>>>> this. Here are my two cents:
>>>>> 
>>>>> 1. First some meta comments regarding "fail fast" v.s. "making
>>>> progress". I
>>>>> agree that in general we should better "enforce user to do the right
>>>> thing"
>>>>> in system design, but we also need to keep in mind that Kafka is a
>>>>> multi-tenant system, i.e. from a Streams app's pov you probably would not
>>>>> control the whole streaming processing pipeline end-to-end. E.g. Your
>>>> input
>>>>> data may not be controlled by yourself; it could be written by another
>>>> app,
>>>>> or another team in your company, or even a different organization, and if
>>>>> an error happens maybe you cannot fix "to do the right thing" just by
>>>>> yourself in time. In such an environment I think it is important to leave
>>>>> the door open to let users be more resilient. So I find the current
>>>>> proposal which does leave the door open for either fail-fast or make
>>>>> progress quite reasonable.
>>>>> 
>>>>> 2. On the other hand, if the question is whether we should provide a
>>>>> built-in "send to bad queue" handler from the library, I think that might
>>>>> be an overkill: with some tweaks (see my detailed comments below) on the
>>>>> API we can allow users to implement such handlers pretty easily. In
>>>> fact, I
>>>>> feel even "LogAndThresholdExceptionHandler" is not necessary as a
>>>> built-in
>>>>> handler, as it would then require users to specify the threshold via
>>>>> configs, etc. I think letting people provide such "eco-libraries" may be
>>>>> better.
>>>>> 
>>>>> 3. Regarding the CRC error: today we validate CRC on both the broker end
>>>>> upon receiving produce requests and on consumer end upon receiving fetch
>>>>> responses; and if the CRC validation fails in the former case it would
>>>> not
>>>>> be appended to the broker logs. So if we do see a CRC failure on the
>>>>> consumer side it has to be that either we have a flipped bit on the
>>>> broker
>>>>> disks or over the wire. For the first case it is fatal while for the
>>>> second
>>>>> it is retriable. Unfortunately we cannot tell which case it is when
>>>> seeing
>>>>> CRC validation failures. But in either case, just skipping and making
>>>>> progress seems not a good choice here, and hence I would personally
>>>> exclude
>>>>> these errors from the general serde errors to NOT leave the door open of
>>>>> making progress.
>>>>> 
>>>>> Currently such errors are thrown as KafkaException that wraps an
>>>>> InvalidRecordException, which may be too general and we could consider
>>>> just
>>>>> throwing the InvalidRecordException directly. But that could be an
>>>>> orthogonal discussion if we agrees that CRC failures should not be
>>>>> considered in this KIP.
>>>>> 
>>>>> ----------------
>>>>> 
>>>>> Now some detailed comments:
>>>>> 
>>>>> 4. Could we consider adding the processor context in the handle()
>>>> function
>>>>> as well? This context will be wrapping as the source node that is about
>>>> to
>>>>> process the record. This could expose more info like which task / source
>>>>> node sees this error, which timestamp of the message, etc, and also can
>>>>> allow users to implement their handlers by exposing some metrics, by
>>>>> calling context.forward() to implement the "send to bad queue" behavior
>>>> etc.
>>>>> 
>>>>> 5. Could you add the string name of
>>>>> StreamsConfig.DEFAULT_RECORD_EXCEPTION_HANDLER as well in the KIP?
>>>>> Personally I find "default" prefix a bit misleading since we do not allow
>>>>> users to override it per-node yet. But I'm okay either way as I can see
>>>> we
>>>>> may extend it in the future and probably would like to not rename the
>>>>> config again. Also from the experience of `default partitioner` and
>>>>> `default timestamp extractor` we may also make sure that the passed in
>>>>> object can be either a string "class name" or a class object?
>>>>> 
>>>>> 
>>>>> Guozhang
>>>>> 
>>>>> 
>>>>> On Wed, Jun 7, 2017 at 2:16 PM, Jan Filipiak <Jan.Filipiak@trivago.com <ma...@trivago.com>>
>>>>> wrote:
>>>>> 
>>>>>> Hi Eno,
>>>>>> 
>>>>>> On 07.06.2017 22:49, Eno Thereska wrote:
>>>>>> 
>>>>>>> Comments inline:
>>>>>>> 
>>>>>>> On 5 Jun 2017, at 18:19, Jan Filipiak <Jan.Filipiak@trivago.com <ma...@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?
>>>>>>> 
>>>>>> I don't think so. IMO you can not reasonably continue parsing when the
>>>>>> checksum of a message is not correct. If you are not sure you got the
>>>>>> correct length, how can you be sure to find the next record? I would
>>>> always
>>>>>> straight fail in all cases. Its to hard for me to understand why one
>>>> would
>>>>>> try to continue. I mentioned CRC's because thats the only bad pills I
>>>> ever
>>>>>> saw so far. But I am happy that it just stopped and I could check what
>>>> was
>>>>>> going on. This will also be invasive in the client code then.
>>>>>> 
>>>>>> If you ask me, I am always going to vote for "grind to halt" let the
>>>>>> developers see what happened and let them fix it. It helps building good
>>>>>> kafka experiences and better software and architectures. For me this is:
>>>>>> "force the user todo the right thing". https://youtu.be/aAb7hSCtvGw? <https://youtu.be/aAb7hSCtvGw?>
>>>> t=374
>>>>>> eg. not letting unexpected input slip by.  Letting unexpected input
>>>> slip by
>>>>>> is what bought us 15+years of war of all sorts of ingestion attacks. I
>>>>>> don't even dare to estimate how many missingrecords-search-teams going
>>>> be
>>>>>> formed, maybe some hackerone for stream apps :D
>>>>>> 
>>>>>> Best Jan
>>>>>> 
>>>>>> 
>>>>>>> 
>>>>>>>>> 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 sound?
>>>>>>>>> 
>>>>>>>> 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.
>>>>>>> 
>>>>>>> Thanks,
>>>>>>> Eno
>>>>>>> 
>>>>>>> 
>>>>>>> 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 <ma...@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 <ma...@trivago.com>>
>>>>>>>>>>>> wrote:
>>>>>>>>>>>> 
>>>>>>>>>>>> IMHO your doing it wrong then. + building to much support into the
>>>>>>>>>>>>> kafka
>>>>>>>>>>>>> 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
>>>>>>>>>>>>>> giving
>>>>>>>>>>>>>> people options and there are times when you don't want to fail
>>>>>>>>>>>>>> fast.
>>>>>>>>>>>>>> 
>>>>>>>>>>>>>> 
>>>>>>>>>>>>>> On Fri, 2 Jun 2017 at 11:00 Jan Filipiak <
>>>> Jan.Filipiak@trivago.com <ma...@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
>>>>>>>>>>>>>>> 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 <ma...@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.
>>>>>>>>>>>>>>>>>>>> 
>>>>>>>>>>>>>>>>>>>> 
>>>>>>>>>>>>>>>>>>>> 
>>>>>> 
>>>>> 
>>>>> 
>>>>> --
>>>>> -- Guozhang
>>>> 
>>>> 
>>> 
>>> 
>>> -- 
>>> -- Guozhang

Re: [DISCUSS]: KIP-161: streams record processing exception handlers

["Matthias J. Sax" <ma...@confluent.io>]

I also think, that one config is better, with two default
implementations: failing and log-and-continue

However, I think we should fail by default. Similar to timestamp
extractor as "silent" data loss is no good default behavior IMHO.


-Matthias

On 6/22/17 11:00 AM, Eno Thereska wrote:
> Answers inline: 
> 
>> On 22 Jun 2017, at 03:26, Guozhang Wang <wa...@gmail.com> wrote:
>>
>> Thanks for the updated KIP, some more comments:
>>
>> 1.The config name is "default.deserialization.exception.handler" while the
>> interface class name is "RecordExceptionHandler", which is more general
>> than the intended purpose. Could we rename the class name accordingly?
> 
> Sure.
> 
> 
>>
>> 2. Could you describe the full implementation of "DefaultExceptionHandler",
>> currently it is not clear to me how it is implemented with the configured
>> value.
>>
>> In addition, I think we do not need to include an additional
>> "DEFAULT_DESERIALIZATION_EXCEPTION_RESPONSE_CONFIG" as the configure()
>> function is mainly used for users to pass any customized parameters that is
>> out of the Streams library; plus adding such additional config sounds
>> over-complicated for a default exception handler. Instead I'd suggest we
>> just provide two handlers (or three if people feel strong about the
>> LogAndThresholdExceptionHandler), one for FailOnExceptionHandler and one
>> for LogAndContinueOnExceptionHandler. And we can set
>> LogAndContinueOnExceptionHandler
>> by default.
>>
> 
> That's what I had originally. Jay mentioned he preferred one default class, with config options.
> So with that approach, you'd have 2 config options, one for failing, one for continuing, and the one
> exception handler would take those options during it's configure() call.
> 
> After checking the other exception handlers in the code, I might revert back to what I originally had (2 default handlers) 
> as Guozhang also re-suggests, but still have the interface extend Configurable. Guozhang, you ok with that? In that case
> there is no need for the response config option.
> 
> Thanks
> Eno
> 
> 
>>
>> Guozhang
>>
>>
>>
>>
>>
>>
>>
>>
>> On Wed, Jun 21, 2017 at 1:39 AM, Eno Thereska <eno.thereska@gmail.com <ma...@gmail.com>>
>> wrote:
>>
>>> Thanks Guozhang,
>>>
>>> I’ve updated the KIP and hopefully addressed all the comments so far. In
>>> the process also changed the name of the KIP to reflect its scope better:
>>> https://cwiki.apache.org/confluence/display/KAFKA/KIP-161%3A+streams+ <https://cwiki.apache.org/confluence/display/KAFKA/KIP-161%3A+streams+>
>>> deserialization+exception+handlers <https://cwiki.apache.org/ <https://cwiki.apache.org/>
>>> confluence/display/KAFKA/KIP-161:+streams+deserialization+
>>> exception+handlers>
>>>
>>> Any other feedback appreciated, otherwise I’ll start the vote soon.
>>>
>>> Thanks
>>> Eno
>>>
>>>> On Jun 12, 2017, at 6:28 AM, Guozhang Wang <wa...@gmail.com> wrote:
>>>>
>>>> Eno, Thanks for bringing this proposal up and sorry for getting late on
>>>> this. Here are my two cents:
>>>>
>>>> 1. First some meta comments regarding "fail fast" v.s. "making
>>> progress". I
>>>> agree that in general we should better "enforce user to do the right
>>> thing"
>>>> in system design, but we also need to keep in mind that Kafka is a
>>>> multi-tenant system, i.e. from a Streams app's pov you probably would not
>>>> control the whole streaming processing pipeline end-to-end. E.g. Your
>>> input
>>>> data may not be controlled by yourself; it could be written by another
>>> app,
>>>> or another team in your company, or even a different organization, and if
>>>> an error happens maybe you cannot fix "to do the right thing" just by
>>>> yourself in time. In such an environment I think it is important to leave
>>>> the door open to let users be more resilient. So I find the current
>>>> proposal which does leave the door open for either fail-fast or make
>>>> progress quite reasonable.
>>>>
>>>> 2. On the other hand, if the question is whether we should provide a
>>>> built-in "send to bad queue" handler from the library, I think that might
>>>> be an overkill: with some tweaks (see my detailed comments below) on the
>>>> API we can allow users to implement such handlers pretty easily. In
>>> fact, I
>>>> feel even "LogAndThresholdExceptionHandler" is not necessary as a
>>> built-in
>>>> handler, as it would then require users to specify the threshold via
>>>> configs, etc. I think letting people provide such "eco-libraries" may be
>>>> better.
>>>>
>>>> 3. Regarding the CRC error: today we validate CRC on both the broker end
>>>> upon receiving produce requests and on consumer end upon receiving fetch
>>>> responses; and if the CRC validation fails in the former case it would
>>> not
>>>> be appended to the broker logs. So if we do see a CRC failure on the
>>>> consumer side it has to be that either we have a flipped bit on the
>>> broker
>>>> disks or over the wire. For the first case it is fatal while for the
>>> second
>>>> it is retriable. Unfortunately we cannot tell which case it is when
>>> seeing
>>>> CRC validation failures. But in either case, just skipping and making
>>>> progress seems not a good choice here, and hence I would personally
>>> exclude
>>>> these errors from the general serde errors to NOT leave the door open of
>>>> making progress.
>>>>
>>>> Currently such errors are thrown as KafkaException that wraps an
>>>> InvalidRecordException, which may be too general and we could consider
>>> just
>>>> throwing the InvalidRecordException directly. But that could be an
>>>> orthogonal discussion if we agrees that CRC failures should not be
>>>> considered in this KIP.
>>>>
>>>> ----------------
>>>>
>>>> Now some detailed comments:
>>>>
>>>> 4. Could we consider adding the processor context in the handle()
>>> function
>>>> as well? This context will be wrapping as the source node that is about
>>> to
>>>> process the record. This could expose more info like which task / source
>>>> node sees this error, which timestamp of the message, etc, and also can
>>>> allow users to implement their handlers by exposing some metrics, by
>>>> calling context.forward() to implement the "send to bad queue" behavior
>>> etc.
>>>>
>>>> 5. Could you add the string name of
>>>> StreamsConfig.DEFAULT_RECORD_EXCEPTION_HANDLER as well in the KIP?
>>>> Personally I find "default" prefix a bit misleading since we do not allow
>>>> users to override it per-node yet. But I'm okay either way as I can see
>>> we
>>>> may extend it in the future and probably would like to not rename the
>>>> config again. Also from the experience of `default partitioner` and
>>>> `default timestamp extractor` we may also make sure that the passed in
>>>> object can be either a string "class name" or a class object?
>>>>
>>>>
>>>> Guozhang
>>>>
>>>>
>>>> On Wed, Jun 7, 2017 at 2:16 PM, Jan Filipiak <Ja...@trivago.com>
>>>> wrote:
>>>>
>>>>> Hi Eno,
>>>>>
>>>>> On 07.06.2017 22:49, Eno Thereska wrote:
>>>>>
>>>>>> Comments inline:
>>>>>>
>>>>>> On 5 Jun 2017, at 18:19, Jan Filipiak <Ja...@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?
>>>>>>
>>>>> I don't think so. IMO you can not reasonably continue parsing when the
>>>>> checksum of a message is not correct. If you are not sure you got the
>>>>> correct length, how can you be sure to find the next record? I would
>>> always
>>>>> straight fail in all cases. Its to hard for me to understand why one
>>> would
>>>>> try to continue. I mentioned CRC's because thats the only bad pills I
>>> ever
>>>>> saw so far. But I am happy that it just stopped and I could check what
>>> was
>>>>> going on. This will also be invasive in the client code then.
>>>>>
>>>>> If you ask me, I am always going to vote for "grind to halt" let the
>>>>> developers see what happened and let them fix it. It helps building good
>>>>> kafka experiences and better software and architectures. For me this is:
>>>>> "force the user todo the right thing". https://youtu.be/aAb7hSCtvGw?
>>> t=374
>>>>> eg. not letting unexpected input slip by.  Letting unexpected input
>>> slip by
>>>>> is what bought us 15+years of war of all sorts of ingestion attacks. I
>>>>> don't even dare to estimate how many missingrecords-search-teams going
>>> be
>>>>> formed, maybe some hackerone for stream apps :D
>>>>>
>>>>> Best Jan
>>>>>
>>>>>
>>>>>>
>>>>>>>> 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 sound?
>>>>>>>>
>>>>>>> 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.
>>>>>>
>>>>>> Thanks,
>>>>>> Eno
>>>>>>
>>>>>>
>>>>>> 6. Will add an end-to-end example as Michael suggested.
>>>>>>>>
>>>>>>>> Thanks
>>>>>>>> Eno
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>> On 4 Jun 2017, at 02:35, Matthias J. Sax <ma...@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
>>>>>>>>>>>> kafka
>>>>>>>>>>>> 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
>>>>>>>>>>>>> giving
>>>>>>>>>>>>> people options and there are times when you don't want to fail
>>>>>>>>>>>>> fast.
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>> 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
>>>>>>>>>>>>>> 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.
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>
>>>>>
>>>>
>>>>
>>>> --
>>>> -- Guozhang
>>>
>>>
>>
>>
>> -- 
>> -- Guozhang
> 
> 

Re: [DISCUSS]: KIP-161: streams record processing exception handlers

[Eno Thereska <en...@gmail.com>]

Answers inline: 

> On 22 Jun 2017, at 03:26, Guozhang Wang <wa...@gmail.com> wrote:
> 
> Thanks for the updated KIP, some more comments:
> 
> 1.The config name is "default.deserialization.exception.handler" while the
> interface class name is "RecordExceptionHandler", which is more general
> than the intended purpose. Could we rename the class name accordingly?

Sure.


> 
> 2. Could you describe the full implementation of "DefaultExceptionHandler",
> currently it is not clear to me how it is implemented with the configured
> value.
> 
> In addition, I think we do not need to include an additional
> "DEFAULT_DESERIALIZATION_EXCEPTION_RESPONSE_CONFIG" as the configure()
> function is mainly used for users to pass any customized parameters that is
> out of the Streams library; plus adding such additional config sounds
> over-complicated for a default exception handler. Instead I'd suggest we
> just provide two handlers (or three if people feel strong about the
> LogAndThresholdExceptionHandler), one for FailOnExceptionHandler and one
> for LogAndContinueOnExceptionHandler. And we can set
> LogAndContinueOnExceptionHandler
> by default.
> 

That's what I had originally. Jay mentioned he preferred one default class, with config options.
So with that approach, you'd have 2 config options, one for failing, one for continuing, and the one
exception handler would take those options during it's configure() call.

After checking the other exception handlers in the code, I might revert back to what I originally had (2 default handlers) 
as Guozhang also re-suggests, but still have the interface extend Configurable. Guozhang, you ok with that? In that case
there is no need for the response config option.

Thanks
Eno


> 
> Guozhang
> 
> 
> 
> 
> 
> 
> 
> 
> On Wed, Jun 21, 2017 at 1:39 AM, Eno Thereska <eno.thereska@gmail.com <ma...@gmail.com>>
> wrote:
> 
>> Thanks Guozhang,
>> 
>> I’ve updated the KIP and hopefully addressed all the comments so far. In
>> the process also changed the name of the KIP to reflect its scope better:
>> https://cwiki.apache.org/confluence/display/KAFKA/KIP-161%3A+streams+ <https://cwiki.apache.org/confluence/display/KAFKA/KIP-161%3A+streams+>
>> deserialization+exception+handlers <https://cwiki.apache.org/ <https://cwiki.apache.org/>
>> confluence/display/KAFKA/KIP-161:+streams+deserialization+
>> exception+handlers>
>> 
>> Any other feedback appreciated, otherwise I’ll start the vote soon.
>> 
>> Thanks
>> Eno
>> 
>>> On Jun 12, 2017, at 6:28 AM, Guozhang Wang <wa...@gmail.com> wrote:
>>> 
>>> Eno, Thanks for bringing this proposal up and sorry for getting late on
>>> this. Here are my two cents:
>>> 
>>> 1. First some meta comments regarding "fail fast" v.s. "making
>> progress". I
>>> agree that in general we should better "enforce user to do the right
>> thing"
>>> in system design, but we also need to keep in mind that Kafka is a
>>> multi-tenant system, i.e. from a Streams app's pov you probably would not
>>> control the whole streaming processing pipeline end-to-end. E.g. Your
>> input
>>> data may not be controlled by yourself; it could be written by another
>> app,
>>> or another team in your company, or even a different organization, and if
>>> an error happens maybe you cannot fix "to do the right thing" just by
>>> yourself in time. In such an environment I think it is important to leave
>>> the door open to let users be more resilient. So I find the current
>>> proposal which does leave the door open for either fail-fast or make
>>> progress quite reasonable.
>>> 
>>> 2. On the other hand, if the question is whether we should provide a
>>> built-in "send to bad queue" handler from the library, I think that might
>>> be an overkill: with some tweaks (see my detailed comments below) on the
>>> API we can allow users to implement such handlers pretty easily. In
>> fact, I
>>> feel even "LogAndThresholdExceptionHandler" is not necessary as a
>> built-in
>>> handler, as it would then require users to specify the threshold via
>>> configs, etc. I think letting people provide such "eco-libraries" may be
>>> better.
>>> 
>>> 3. Regarding the CRC error: today we validate CRC on both the broker end
>>> upon receiving produce requests and on consumer end upon receiving fetch
>>> responses; and if the CRC validation fails in the former case it would
>> not
>>> be appended to the broker logs. So if we do see a CRC failure on the
>>> consumer side it has to be that either we have a flipped bit on the
>> broker
>>> disks or over the wire. For the first case it is fatal while for the
>> second
>>> it is retriable. Unfortunately we cannot tell which case it is when
>> seeing
>>> CRC validation failures. But in either case, just skipping and making
>>> progress seems not a good choice here, and hence I would personally
>> exclude
>>> these errors from the general serde errors to NOT leave the door open of
>>> making progress.
>>> 
>>> Currently such errors are thrown as KafkaException that wraps an
>>> InvalidRecordException, which may be too general and we could consider
>> just
>>> throwing the InvalidRecordException directly. But that could be an
>>> orthogonal discussion if we agrees that CRC failures should not be
>>> considered in this KIP.
>>> 
>>> ----------------
>>> 
>>> Now some detailed comments:
>>> 
>>> 4. Could we consider adding the processor context in the handle()
>> function
>>> as well? This context will be wrapping as the source node that is about
>> to
>>> process the record. This could expose more info like which task / source
>>> node sees this error, which timestamp of the message, etc, and also can
>>> allow users to implement their handlers by exposing some metrics, by
>>> calling context.forward() to implement the "send to bad queue" behavior
>> etc.
>>> 
>>> 5. Could you add the string name of
>>> StreamsConfig.DEFAULT_RECORD_EXCEPTION_HANDLER as well in the KIP?
>>> Personally I find "default" prefix a bit misleading since we do not allow
>>> users to override it per-node yet. But I'm okay either way as I can see
>> we
>>> may extend it in the future and probably would like to not rename the
>>> config again. Also from the experience of `default partitioner` and
>>> `default timestamp extractor` we may also make sure that the passed in
>>> object can be either a string "class name" or a class object?
>>> 
>>> 
>>> Guozhang
>>> 
>>> 
>>> On Wed, Jun 7, 2017 at 2:16 PM, Jan Filipiak <Ja...@trivago.com>
>>> wrote:
>>> 
>>>> Hi Eno,
>>>> 
>>>> On 07.06.2017 22:49, Eno Thereska wrote:
>>>> 
>>>>> Comments inline:
>>>>> 
>>>>> On 5 Jun 2017, at 18:19, Jan Filipiak <Ja...@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?
>>>>> 
>>>> I don't think so. IMO you can not reasonably continue parsing when the
>>>> checksum of a message is not correct. If you are not sure you got the
>>>> correct length, how can you be sure to find the next record? I would
>> always
>>>> straight fail in all cases. Its to hard for me to understand why one
>> would
>>>> try to continue. I mentioned CRC's because thats the only bad pills I
>> ever
>>>> saw so far. But I am happy that it just stopped and I could check what
>> was
>>>> going on. This will also be invasive in the client code then.
>>>> 
>>>> If you ask me, I am always going to vote for "grind to halt" let the
>>>> developers see what happened and let them fix it. It helps building good
>>>> kafka experiences and better software and architectures. For me this is:
>>>> "force the user todo the right thing". https://youtu.be/aAb7hSCtvGw?
>> t=374
>>>> eg. not letting unexpected input slip by.  Letting unexpected input
>> slip by
>>>> is what bought us 15+years of war of all sorts of ingestion attacks. I
>>>> don't even dare to estimate how many missingrecords-search-teams going
>> be
>>>> formed, maybe some hackerone for stream apps :D
>>>> 
>>>> Best Jan
>>>> 
>>>> 
>>>>> 
>>>>>>> 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 sound?
>>>>>>> 
>>>>>> 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.
>>>>> 
>>>>> Thanks,
>>>>> Eno
>>>>> 
>>>>> 
>>>>> 6. Will add an end-to-end example as Michael suggested.
>>>>>>> 
>>>>>>> Thanks
>>>>>>> Eno
>>>>>>> 
>>>>>>> 
>>>>>>> 
>>>>>>> On 4 Jun 2017, at 02:35, Matthias J. Sax <ma...@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
>>>>>>>>>>> kafka
>>>>>>>>>>> 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
>>>>>>>>>>>> giving
>>>>>>>>>>>> people options and there are times when you don't want to fail
>>>>>>>>>>>> fast.
>>>>>>>>>>>> 
>>>>>>>>>>>> 
>>>>>>>>>>>> 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
>>>>>>>>>>>>> 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.
>>>>>>>>>>>>>>>>>> 
>>>>>>>>>>>>>>>>>> 
>>>>>>>>>>>>>>>>>> 
>>>> 
>>> 
>>> 
>>> --
>>> -- Guozhang
>> 
>> 
> 
> 
> -- 
> -- Guozhang

Re: [DISCUSS]: KIP-161: streams record processing exception handlers

[Eno Thereska <en...@gmail.com>]

Answers inline: 

> On 22 Jun 2017, at 03:26, Guozhang Wang <wa...@gmail.com> wrote:
> 
> Thanks for the updated KIP, some more comments:
> 
> 1.The config name is "default.deserialization.exception.handler" while the
> interface class name is "RecordExceptionHandler", which is more general
> than the intended purpose. Could we rename the class name accordingly?

Sure.


> 
> 2. Could you describe the full implementation of "DefaultExceptionHandler",
> currently it is not clear to me how it is implemented with the configured
> value.
> 
> In addition, I think we do not need to include an additional
> "DEFAULT_DESERIALIZATION_EXCEPTION_RESPONSE_CONFIG" as the configure()
> function is mainly used for users to pass any customized parameters that is
> out of the Streams library; plus adding such additional config sounds
> over-complicated for a default exception handler. Instead I'd suggest we
> just provide two handlers (or three if people feel strong about the
> LogAndThresholdExceptionHandler), one for FailOnExceptionHandler and one
> for LogAndContinueOnExceptionHandler. And we can set
> LogAndContinueOnExceptionHandler
> by default.
> 

That's what I had originally. Jay mentioned he preferred one default class, with config options.
So with that approach, you'd have 2 config options, one for failing, one for continuing, and the one
exception handler would take those options during it's configure() call.

After checking the other exception handlers in the code, I might revert back to what I originally had (2 default handlers) 
as Guozhang also re-suggests, but still have the interface extend Configurable. Guozhang, you ok with that? In that case
there is no need for the response config option.

Thanks
Eno


> 
> Guozhang
> 
> 
> 
> 
> 
> 
> 
> 
> On Wed, Jun 21, 2017 at 1:39 AM, Eno Thereska <eno.thereska@gmail.com <ma...@gmail.com>>
> wrote:
> 
>> Thanks Guozhang,
>> 
>> I’ve updated the KIP and hopefully addressed all the comments so far. In
>> the process also changed the name of the KIP to reflect its scope better:
>> https://cwiki.apache.org/confluence/display/KAFKA/KIP-161%3A+streams+ <https://cwiki.apache.org/confluence/display/KAFKA/KIP-161%3A+streams+>
>> deserialization+exception+handlers <https://cwiki.apache.org/ <https://cwiki.apache.org/>
>> confluence/display/KAFKA/KIP-161:+streams+deserialization+
>> exception+handlers>
>> 
>> Any other feedback appreciated, otherwise I’ll start the vote soon.
>> 
>> Thanks
>> Eno
>> 
>>> On Jun 12, 2017, at 6:28 AM, Guozhang Wang <wa...@gmail.com> wrote:
>>> 
>>> Eno, Thanks for bringing this proposal up and sorry for getting late on
>>> this. Here are my two cents:
>>> 
>>> 1. First some meta comments regarding "fail fast" v.s. "making
>> progress". I
>>> agree that in general we should better "enforce user to do the right
>> thing"
>>> in system design, but we also need to keep in mind that Kafka is a
>>> multi-tenant system, i.e. from a Streams app's pov you probably would not
>>> control the whole streaming processing pipeline end-to-end. E.g. Your
>> input
>>> data may not be controlled by yourself; it could be written by another
>> app,
>>> or another team in your company, or even a different organization, and if
>>> an error happens maybe you cannot fix "to do the right thing" just by
>>> yourself in time. In such an environment I think it is important to leave
>>> the door open to let users be more resilient. So I find the current
>>> proposal which does leave the door open for either fail-fast or make
>>> progress quite reasonable.
>>> 
>>> 2. On the other hand, if the question is whether we should provide a
>>> built-in "send to bad queue" handler from the library, I think that might
>>> be an overkill: with some tweaks (see my detailed comments below) on the
>>> API we can allow users to implement such handlers pretty easily. In
>> fact, I
>>> feel even "LogAndThresholdExceptionHandler" is not necessary as a
>> built-in
>>> handler, as it would then require users to specify the threshold via
>>> configs, etc. I think letting people provide such "eco-libraries" may be
>>> better.
>>> 
>>> 3. Regarding the CRC error: today we validate CRC on both the broker end
>>> upon receiving produce requests and on consumer end upon receiving fetch
>>> responses; and if the CRC validation fails in the former case it would
>> not
>>> be appended to the broker logs. So if we do see a CRC failure on the
>>> consumer side it has to be that either we have a flipped bit on the
>> broker
>>> disks or over the wire. For the first case it is fatal while for the
>> second
>>> it is retriable. Unfortunately we cannot tell which case it is when
>> seeing
>>> CRC validation failures. But in either case, just skipping and making
>>> progress seems not a good choice here, and hence I would personally
>> exclude
>>> these errors from the general serde errors to NOT leave the door open of
>>> making progress.
>>> 
>>> Currently such errors are thrown as KafkaException that wraps an
>>> InvalidRecordException, which may be too general and we could consider
>> just
>>> throwing the InvalidRecordException directly. But that could be an
>>> orthogonal discussion if we agrees that CRC failures should not be
>>> considered in this KIP.
>>> 
>>> ----------------
>>> 
>>> Now some detailed comments:
>>> 
>>> 4. Could we consider adding the processor context in the handle()
>> function
>>> as well? This context will be wrapping as the source node that is about
>> to
>>> process the record. This could expose more info like which task / source
>>> node sees this error, which timestamp of the message, etc, and also can
>>> allow users to implement their handlers by exposing some metrics, by
>>> calling context.forward() to implement the "send to bad queue" behavior
>> etc.
>>> 
>>> 5. Could you add the string name of
>>> StreamsConfig.DEFAULT_RECORD_EXCEPTION_HANDLER as well in the KIP?
>>> Personally I find "default" prefix a bit misleading since we do not allow
>>> users to override it per-node yet. But I'm okay either way as I can see
>> we
>>> may extend it in the future and probably would like to not rename the
>>> config again. Also from the experience of `default partitioner` and
>>> `default timestamp extractor` we may also make sure that the passed in
>>> object can be either a string "class name" or a class object?
>>> 
>>> 
>>> Guozhang
>>> 
>>> 
>>> On Wed, Jun 7, 2017 at 2:16 PM, Jan Filipiak <Ja...@trivago.com>
>>> wrote:
>>> 
>>>> Hi Eno,
>>>> 
>>>> On 07.06.2017 22:49, Eno Thereska wrote:
>>>> 
>>>>> Comments inline:
>>>>> 
>>>>> On 5 Jun 2017, at 18:19, Jan Filipiak <Ja...@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?
>>>>> 
>>>> I don't think so. IMO you can not reasonably continue parsing when the
>>>> checksum of a message is not correct. If you are not sure you got the
>>>> correct length, how can you be sure to find the next record? I would
>> always
>>>> straight fail in all cases. Its to hard for me to understand why one
>> would
>>>> try to continue. I mentioned CRC's because thats the only bad pills I
>> ever
>>>> saw so far. But I am happy that it just stopped and I could check what
>> was
>>>> going on. This will also be invasive in the client code then.
>>>> 
>>>> If you ask me, I am always going to vote for "grind to halt" let the
>>>> developers see what happened and let them fix it. It helps building good
>>>> kafka experiences and better software and architectures. For me this is:
>>>> "force the user todo the right thing". https://youtu.be/aAb7hSCtvGw?
>> t=374
>>>> eg. not letting unexpected input slip by.  Letting unexpected input
>> slip by
>>>> is what bought us 15+years of war of all sorts of ingestion attacks. I
>>>> don't even dare to estimate how many missingrecords-search-teams going
>> be
>>>> formed, maybe some hackerone for stream apps :D
>>>> 
>>>> Best Jan
>>>> 
>>>> 
>>>>> 
>>>>>>> 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 sound?
>>>>>>> 
>>>>>> 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.
>>>>> 
>>>>> Thanks,
>>>>> Eno
>>>>> 
>>>>> 
>>>>> 6. Will add an end-to-end example as Michael suggested.
>>>>>>> 
>>>>>>> Thanks
>>>>>>> Eno
>>>>>>> 
>>>>>>> 
>>>>>>> 
>>>>>>> On 4 Jun 2017, at 02:35, Matthias J. Sax <ma...@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
>>>>>>>>>>> kafka
>>>>>>>>>>> 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
>>>>>>>>>>>> giving
>>>>>>>>>>>> people options and there are times when you don't want to fail
>>>>>>>>>>>> fast.
>>>>>>>>>>>> 
>>>>>>>>>>>> 
>>>>>>>>>>>> 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
>>>>>>>>>>>>> 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.
>>>>>>>>>>>>>>>>>> 
>>>>>>>>>>>>>>>>>> 
>>>>>>>>>>>>>>>>>> 
>>>> 
>>> 
>>> 
>>> --
>>> -- Guozhang
>> 
>> 
> 
> 
> -- 
> -- Guozhang

Re: [DISCUSS]: KIP-161: streams record processing exception handlers

[Guozhang Wang <wa...@gmail.com>]

Thanks for the updated KIP, some more comments:

1.The config name is "default.deserialization.exception.handler" while the
interface class name is "RecordExceptionHandler", which is more general
than the intended purpose. Could we rename the class name accordingly?

2. Could you describe the full implementation of "DefaultExceptionHandler",
currently it is not clear to me how it is implemented with the configured
value.

In addition, I think we do not need to include an additional
"DEFAULT_DESERIALIZATION_EXCEPTION_RESPONSE_CONFIG" as the configure()
function is mainly used for users to pass any customized parameters that is
out of the Streams library; plus adding such additional config sounds
over-complicated for a default exception handler. Instead I'd suggest we
just provide two handlers (or three if people feel strong about the
LogAndThresholdExceptionHandler), one for FailOnExceptionHandler and one
for LogAndContinueOnExceptionHandler. And we can set
LogAndContinueOnExceptionHandler
by default.


Guozhang








On Wed, Jun 21, 2017 at 1:39 AM, Eno Thereska <en...@gmail.com>
wrote:

> Thanks Guozhang,
>
> I’ve updated the KIP and hopefully addressed all the comments so far. In
> the process also changed the name of the KIP to reflect its scope better:
> https://cwiki.apache.org/confluence/display/KAFKA/KIP-161%3A+streams+
> deserialization+exception+handlers <https://cwiki.apache.org/
> confluence/display/KAFKA/KIP-161:+streams+deserialization+
> exception+handlers>
>
> Any other feedback appreciated, otherwise I’ll start the vote soon.
>
> Thanks
> Eno
>
> > On Jun 12, 2017, at 6:28 AM, Guozhang Wang <wa...@gmail.com> wrote:
> >
> > Eno, Thanks for bringing this proposal up and sorry for getting late on
> > this. Here are my two cents:
> >
> > 1. First some meta comments regarding "fail fast" v.s. "making
> progress". I
> > agree that in general we should better "enforce user to do the right
> thing"
> > in system design, but we also need to keep in mind that Kafka is a
> > multi-tenant system, i.e. from a Streams app's pov you probably would not
> > control the whole streaming processing pipeline end-to-end. E.g. Your
> input
> > data may not be controlled by yourself; it could be written by another
> app,
> > or another team in your company, or even a different organization, and if
> > an error happens maybe you cannot fix "to do the right thing" just by
> > yourself in time. In such an environment I think it is important to leave
> > the door open to let users be more resilient. So I find the current
> > proposal which does leave the door open for either fail-fast or make
> > progress quite reasonable.
> >
> > 2. On the other hand, if the question is whether we should provide a
> > built-in "send to bad queue" handler from the library, I think that might
> > be an overkill: with some tweaks (see my detailed comments below) on the
> > API we can allow users to implement such handlers pretty easily. In
> fact, I
> > feel even "LogAndThresholdExceptionHandler" is not necessary as a
> built-in
> > handler, as it would then require users to specify the threshold via
> > configs, etc. I think letting people provide such "eco-libraries" may be
> > better.
> >
> > 3. Regarding the CRC error: today we validate CRC on both the broker end
> > upon receiving produce requests and on consumer end upon receiving fetch
> > responses; and if the CRC validation fails in the former case it would
> not
> > be appended to the broker logs. So if we do see a CRC failure on the
> > consumer side it has to be that either we have a flipped bit on the
> broker
> > disks or over the wire. For the first case it is fatal while for the
> second
> > it is retriable. Unfortunately we cannot tell which case it is when
> seeing
> > CRC validation failures. But in either case, just skipping and making
> > progress seems not a good choice here, and hence I would personally
> exclude
> > these errors from the general serde errors to NOT leave the door open of
> > making progress.
> >
> > Currently such errors are thrown as KafkaException that wraps an
> > InvalidRecordException, which may be too general and we could consider
> just
> > throwing the InvalidRecordException directly. But that could be an
> > orthogonal discussion if we agrees that CRC failures should not be
> > considered in this KIP.
> >
> > ----------------
> >
> > Now some detailed comments:
> >
> > 4. Could we consider adding the processor context in the handle()
> function
> > as well? This context will be wrapping as the source node that is about
> to
> > process the record. This could expose more info like which task / source
> > node sees this error, which timestamp of the message, etc, and also can
> > allow users to implement their handlers by exposing some metrics, by
> > calling context.forward() to implement the "send to bad queue" behavior
> etc.
> >
> > 5. Could you add the string name of
> > StreamsConfig.DEFAULT_RECORD_EXCEPTION_HANDLER as well in the KIP?
> > Personally I find "default" prefix a bit misleading since we do not allow
> > users to override it per-node yet. But I'm okay either way as I can see
> we
> > may extend it in the future and probably would like to not rename the
> > config again. Also from the experience of `default partitioner` and
> > `default timestamp extractor` we may also make sure that the passed in
> > object can be either a string "class name" or a class object?
> >
> >
> > Guozhang
> >
> >
> > On Wed, Jun 7, 2017 at 2:16 PM, Jan Filipiak <Ja...@trivago.com>
> > wrote:
> >
> >> Hi Eno,
> >>
> >> On 07.06.2017 22:49, Eno Thereska wrote:
> >>
> >>> Comments inline:
> >>>
> >>> On 5 Jun 2017, at 18:19, Jan Filipiak <Ja...@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?
> >>>
> >> I don't think so. IMO you can not reasonably continue parsing when the
> >> checksum of a message is not correct. If you are not sure you got the
> >> correct length, how can you be sure to find the next record? I would
> always
> >> straight fail in all cases. Its to hard for me to understand why one
> would
> >> try to continue. I mentioned CRC's because thats the only bad pills I
> ever
> >> saw so far. But I am happy that it just stopped and I could check what
> was
> >> going on. This will also be invasive in the client code then.
> >>
> >> If you ask me, I am always going to vote for "grind to halt" let the
> >> developers see what happened and let them fix it. It helps building good
> >> kafka experiences and better software and architectures. For me this is:
> >> "force the user todo the right thing". https://youtu.be/aAb7hSCtvGw?
> t=374
> >> eg. not letting unexpected input slip by.  Letting unexpected input
> slip by
> >> is what bought us 15+years of war of all sorts of ingestion attacks. I
> >> don't even dare to estimate how many missingrecords-search-teams going
> be
> >> formed, maybe some hackerone for stream apps :D
> >>
> >> Best Jan
> >>
> >>
> >>>
> >>>>> 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 sound?
> >>>>>
> >>>> 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.
> >>>
> >>> Thanks,
> >>> Eno
> >>>
> >>>
> >>> 6. Will add an end-to-end example as Michael suggested.
> >>>>>
> >>>>> Thanks
> >>>>> Eno
> >>>>>
> >>>>>
> >>>>>
> >>>>> On 4 Jun 2017, at 02:35, Matthias J. Sax <ma...@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
> >>>>>>>>> kafka
> >>>>>>>>> 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
> >>>>>>>>>> giving
> >>>>>>>>>> people options and there are times when you don't want to fail
> >>>>>>>>>> fast.
> >>>>>>>>>>
> >>>>>>>>>>
> >>>>>>>>>> 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
> >>>>>>>>>>> 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.
> >>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>
> >>
> >
> >
> > --
> > -- Guozhang
>
>


-- 
-- Guozhang

Re: [DISCUSS]: KIP-161: streams record processing exception handlers

[Guozhang Wang <wa...@gmail.com>]

Thanks for the updated KIP, some more comments:

1.The config name is "default.deserialization.exception.handler" while the
interface class name is "RecordExceptionHandler", which is more general
than the intended purpose. Could we rename the class name accordingly?

2. Could you describe the full implementation of "DefaultExceptionHandler",
currently it is not clear to me how it is implemented with the configured
value.

In addition, I think we do not need to include an additional
"DEFAULT_DESERIALIZATION_EXCEPTION_RESPONSE_CONFIG" as the configure()
function is mainly used for users to pass any customized parameters that is
out of the Streams library; plus adding such additional config sounds
over-complicated for a default exception handler. Instead I'd suggest we
just provide two handlers (or three if people feel strong about the
LogAndThresholdExceptionHandler), one for FailOnExceptionHandler and one
for LogAndContinueOnExceptionHandler. And we can set
LogAndContinueOnExceptionHandler
by default.


Guozhang








On Wed, Jun 21, 2017 at 1:39 AM, Eno Thereska <en...@gmail.com>
wrote:

> Thanks Guozhang,
>
> I’ve updated the KIP and hopefully addressed all the comments so far. In
> the process also changed the name of the KIP to reflect its scope better:
> https://cwiki.apache.org/confluence/display/KAFKA/KIP-161%3A+streams+
> deserialization+exception+handlers <https://cwiki.apache.org/
> confluence/display/KAFKA/KIP-161:+streams+deserialization+
> exception+handlers>
>
> Any other feedback appreciated, otherwise I’ll start the vote soon.
>
> Thanks
> Eno
>
> > On Jun 12, 2017, at 6:28 AM, Guozhang Wang <wa...@gmail.com> wrote:
> >
> > Eno, Thanks for bringing this proposal up and sorry for getting late on
> > this. Here are my two cents:
> >
> > 1. First some meta comments regarding "fail fast" v.s. "making
> progress". I
> > agree that in general we should better "enforce user to do the right
> thing"
> > in system design, but we also need to keep in mind that Kafka is a
> > multi-tenant system, i.e. from a Streams app's pov you probably would not
> > control the whole streaming processing pipeline end-to-end. E.g. Your
> input
> > data may not be controlled by yourself; it could be written by another
> app,
> > or another team in your company, or even a different organization, and if
> > an error happens maybe you cannot fix "to do the right thing" just by
> > yourself in time. In such an environment I think it is important to leave
> > the door open to let users be more resilient. So I find the current
> > proposal which does leave the door open for either fail-fast or make
> > progress quite reasonable.
> >
> > 2. On the other hand, if the question is whether we should provide a
> > built-in "send to bad queue" handler from the library, I think that might
> > be an overkill: with some tweaks (see my detailed comments below) on the
> > API we can allow users to implement such handlers pretty easily. In
> fact, I
> > feel even "LogAndThresholdExceptionHandler" is not necessary as a
> built-in
> > handler, as it would then require users to specify the threshold via
> > configs, etc. I think letting people provide such "eco-libraries" may be
> > better.
> >
> > 3. Regarding the CRC error: today we validate CRC on both the broker end
> > upon receiving produce requests and on consumer end upon receiving fetch
> > responses; and if the CRC validation fails in the former case it would
> not
> > be appended to the broker logs. So if we do see a CRC failure on the
> > consumer side it has to be that either we have a flipped bit on the
> broker
> > disks or over the wire. For the first case it is fatal while for the
> second
> > it is retriable. Unfortunately we cannot tell which case it is when
> seeing
> > CRC validation failures. But in either case, just skipping and making
> > progress seems not a good choice here, and hence I would personally
> exclude
> > these errors from the general serde errors to NOT leave the door open of
> > making progress.
> >
> > Currently such errors are thrown as KafkaException that wraps an
> > InvalidRecordException, which may be too general and we could consider
> just
> > throwing the InvalidRecordException directly. But that could be an
> > orthogonal discussion if we agrees that CRC failures should not be
> > considered in this KIP.
> >
> > ----------------
> >
> > Now some detailed comments:
> >
> > 4. Could we consider adding the processor context in the handle()
> function
> > as well? This context will be wrapping as the source node that is about
> to
> > process the record. This could expose more info like which task / source
> > node sees this error, which timestamp of the message, etc, and also can
> > allow users to implement their handlers by exposing some metrics, by
> > calling context.forward() to implement the "send to bad queue" behavior
> etc.
> >
> > 5. Could you add the string name of
> > StreamsConfig.DEFAULT_RECORD_EXCEPTION_HANDLER as well in the KIP?
> > Personally I find "default" prefix a bit misleading since we do not allow
> > users to override it per-node yet. But I'm okay either way as I can see
> we
> > may extend it in the future and probably would like to not rename the
> > config again. Also from the experience of `default partitioner` and
> > `default timestamp extractor` we may also make sure that the passed in
> > object can be either a string "class name" or a class object?
> >
> >
> > Guozhang
> >
> >
> > On Wed, Jun 7, 2017 at 2:16 PM, Jan Filipiak <Ja...@trivago.com>
> > wrote:
> >
> >> Hi Eno,
> >>
> >> On 07.06.2017 22:49, Eno Thereska wrote:
> >>
> >>> Comments inline:
> >>>
> >>> On 5 Jun 2017, at 18:19, Jan Filipiak <Ja...@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?
> >>>
> >> I don't think so. IMO you can not reasonably continue parsing when the
> >> checksum of a message is not correct. If you are not sure you got the
> >> correct length, how can you be sure to find the next record? I would
> always
> >> straight fail in all cases. Its to hard for me to understand why one
> would
> >> try to continue. I mentioned CRC's because thats the only bad pills I
> ever
> >> saw so far. But I am happy that it just stopped and I could check what
> was
> >> going on. This will also be invasive in the client code then.
> >>
> >> If you ask me, I am always going to vote for "grind to halt" let the
> >> developers see what happened and let them fix it. It helps building good
> >> kafka experiences and better software and architectures. For me this is:
> >> "force the user todo the right thing". https://youtu.be/aAb7hSCtvGw?
> t=374
> >> eg. not letting unexpected input slip by.  Letting unexpected input
> slip by
> >> is what bought us 15+years of war of all sorts of ingestion attacks. I
> >> don't even dare to estimate how many missingrecords-search-teams going
> be
> >> formed, maybe some hackerone for stream apps :D
> >>
> >> Best Jan
> >>
> >>
> >>>
> >>>>> 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 sound?
> >>>>>
> >>>> 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.
> >>>
> >>> Thanks,
> >>> Eno
> >>>
> >>>
> >>> 6. Will add an end-to-end example as Michael suggested.
> >>>>>
> >>>>> Thanks
> >>>>> Eno
> >>>>>
> >>>>>
> >>>>>
> >>>>> On 4 Jun 2017, at 02:35, Matthias J. Sax <ma...@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
> >>>>>>>>> kafka
> >>>>>>>>> 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
> >>>>>>>>>> giving
> >>>>>>>>>> people options and there are times when you don't want to fail
> >>>>>>>>>> fast.
> >>>>>>>>>>
> >>>>>>>>>>
> >>>>>>>>>> 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
> >>>>>>>>>>> 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.
> >>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>
> >>
> >
> >
> > --
> > -- Guozhang
>
>


-- 
-- Guozhang

Re: [DISCUSS]: KIP-161: streams record processing exception handlers

[Eno Thereska <en...@gmail.com>]

Thanks Guozhang,

I’ve updated the KIP and hopefully addressed all the comments so far. In the process also changed the name of the KIP to reflect its scope better: https://cwiki.apache.org/confluence/display/KAFKA/KIP-161%3A+streams+deserialization+exception+handlers <https://cwiki.apache.org/confluence/display/KAFKA/KIP-161:+streams+deserialization+exception+handlers>

Any other feedback appreciated, otherwise I’ll start the vote soon.

Thanks
Eno

> On Jun 12, 2017, at 6:28 AM, Guozhang Wang <wa...@gmail.com> wrote:
> 
> Eno, Thanks for bringing this proposal up and sorry for getting late on
> this. Here are my two cents:
> 
> 1. First some meta comments regarding "fail fast" v.s. "making progress". I
> agree that in general we should better "enforce user to do the right thing"
> in system design, but we also need to keep in mind that Kafka is a
> multi-tenant system, i.e. from a Streams app's pov you probably would not
> control the whole streaming processing pipeline end-to-end. E.g. Your input
> data may not be controlled by yourself; it could be written by another app,
> or another team in your company, or even a different organization, and if
> an error happens maybe you cannot fix "to do the right thing" just by
> yourself in time. In such an environment I think it is important to leave
> the door open to let users be more resilient. So I find the current
> proposal which does leave the door open for either fail-fast or make
> progress quite reasonable.
> 
> 2. On the other hand, if the question is whether we should provide a
> built-in "send to bad queue" handler from the library, I think that might
> be an overkill: with some tweaks (see my detailed comments below) on the
> API we can allow users to implement such handlers pretty easily. In fact, I
> feel even "LogAndThresholdExceptionHandler" is not necessary as a built-in
> handler, as it would then require users to specify the threshold via
> configs, etc. I think letting people provide such "eco-libraries" may be
> better.
> 
> 3. Regarding the CRC error: today we validate CRC on both the broker end
> upon receiving produce requests and on consumer end upon receiving fetch
> responses; and if the CRC validation fails in the former case it would not
> be appended to the broker logs. So if we do see a CRC failure on the
> consumer side it has to be that either we have a flipped bit on the broker
> disks or over the wire. For the first case it is fatal while for the second
> it is retriable. Unfortunately we cannot tell which case it is when seeing
> CRC validation failures. But in either case, just skipping and making
> progress seems not a good choice here, and hence I would personally exclude
> these errors from the general serde errors to NOT leave the door open of
> making progress.
> 
> Currently such errors are thrown as KafkaException that wraps an
> InvalidRecordException, which may be too general and we could consider just
> throwing the InvalidRecordException directly. But that could be an
> orthogonal discussion if we agrees that CRC failures should not be
> considered in this KIP.
> 
> ----------------
> 
> Now some detailed comments:
> 
> 4. Could we consider adding the processor context in the handle() function
> as well? This context will be wrapping as the source node that is about to
> process the record. This could expose more info like which task / source
> node sees this error, which timestamp of the message, etc, and also can
> allow users to implement their handlers by exposing some metrics, by
> calling context.forward() to implement the "send to bad queue" behavior etc.
> 
> 5. Could you add the string name of
> StreamsConfig.DEFAULT_RECORD_EXCEPTION_HANDLER as well in the KIP?
> Personally I find "default" prefix a bit misleading since we do not allow
> users to override it per-node yet. But I'm okay either way as I can see we
> may extend it in the future and probably would like to not rename the
> config again. Also from the experience of `default partitioner` and
> `default timestamp extractor` we may also make sure that the passed in
> object can be either a string "class name" or a class object?
> 
> 
> Guozhang
> 
> 
> On Wed, Jun 7, 2017 at 2:16 PM, Jan Filipiak <Ja...@trivago.com>
> wrote:
> 
>> Hi Eno,
>> 
>> On 07.06.2017 22:49, Eno Thereska wrote:
>> 
>>> Comments inline:
>>> 
>>> On 5 Jun 2017, at 18:19, Jan Filipiak <Ja...@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?
>>> 
>> I don't think so. IMO you can not reasonably continue parsing when the
>> checksum of a message is not correct. If you are not sure you got the
>> correct length, how can you be sure to find the next record? I would always
>> straight fail in all cases. Its to hard for me to understand why one would
>> try to continue. I mentioned CRC's because thats the only bad pills I ever
>> saw so far. But I am happy that it just stopped and I could check what was
>> going on. This will also be invasive in the client code then.
>> 
>> If you ask me, I am always going to vote for "grind to halt" let the
>> developers see what happened and let them fix it. It helps building good
>> kafka experiences and better software and architectures. For me this is:
>> "force the user todo the right thing". https://youtu.be/aAb7hSCtvGw?t=374
>> eg. not letting unexpected input slip by.  Letting unexpected input slip by
>> is what bought us 15+years of war of all sorts of ingestion attacks. I
>> don't even dare to estimate how many missingrecords-search-teams going be
>> formed, maybe some hackerone for stream apps :D
>> 
>> Best Jan
>> 
>> 
>>> 
>>>>> 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 sound?
>>>>> 
>>>> 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.
>>> 
>>> Thanks,
>>> Eno
>>> 
>>> 
>>> 6. Will add an end-to-end example as Michael suggested.
>>>>> 
>>>>> Thanks
>>>>> Eno
>>>>> 
>>>>> 
>>>>> 
>>>>> On 4 Jun 2017, at 02:35, Matthias J. Sax <ma...@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
>>>>>>>>> kafka
>>>>>>>>> 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
>>>>>>>>>> giving
>>>>>>>>>> people options and there are times when you don't want to fail
>>>>>>>>>> fast.
>>>>>>>>>> 
>>>>>>>>>> 
>>>>>>>>>> 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
>>>>>>>>>>> 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.
>>>>>>>>>>>>>>>> 
>>>>>>>>>>>>>>>> 
>>>>>>>>>>>>>>>> 
>> 
> 
> 
> -- 
> -- Guozhang

Re: [DISCUSS]: KIP-161: streams record processing exception handlers

[Eno Thereska <en...@gmail.com>]

Thanks Guozhang,

I’ve updated the KIP and hopefully addressed all the comments so far. In the process also changed the name of the KIP to reflect its scope better: https://cwiki.apache.org/confluence/display/KAFKA/KIP-161%3A+streams+deserialization+exception+handlers <https://cwiki.apache.org/confluence/display/KAFKA/KIP-161:+streams+deserialization+exception+handlers>

Any other feedback appreciated, otherwise I’ll start the vote soon.

Thanks
Eno

> On Jun 12, 2017, at 6:28 AM, Guozhang Wang <wa...@gmail.com> wrote:
> 
> Eno, Thanks for bringing this proposal up and sorry for getting late on
> this. Here are my two cents:
> 
> 1. First some meta comments regarding "fail fast" v.s. "making progress". I
> agree that in general we should better "enforce user to do the right thing"
> in system design, but we also need to keep in mind that Kafka is a
> multi-tenant system, i.e. from a Streams app's pov you probably would not
> control the whole streaming processing pipeline end-to-end. E.g. Your input
> data may not be controlled by yourself; it could be written by another app,
> or another team in your company, or even a different organization, and if
> an error happens maybe you cannot fix "to do the right thing" just by
> yourself in time. In such an environment I think it is important to leave
> the door open to let users be more resilient. So I find the current
> proposal which does leave the door open for either fail-fast or make
> progress quite reasonable.
> 
> 2. On the other hand, if the question is whether we should provide a
> built-in "send to bad queue" handler from the library, I think that might
> be an overkill: with some tweaks (see my detailed comments below) on the
> API we can allow users to implement such handlers pretty easily. In fact, I
> feel even "LogAndThresholdExceptionHandler" is not necessary as a built-in
> handler, as it would then require users to specify the threshold via
> configs, etc. I think letting people provide such "eco-libraries" may be
> better.
> 
> 3. Regarding the CRC error: today we validate CRC on both the broker end
> upon receiving produce requests and on consumer end upon receiving fetch
> responses; and if the CRC validation fails in the former case it would not
> be appended to the broker logs. So if we do see a CRC failure on the
> consumer side it has to be that either we have a flipped bit on the broker
> disks or over the wire. For the first case it is fatal while for the second
> it is retriable. Unfortunately we cannot tell which case it is when seeing
> CRC validation failures. But in either case, just skipping and making
> progress seems not a good choice here, and hence I would personally exclude
> these errors from the general serde errors to NOT leave the door open of
> making progress.
> 
> Currently such errors are thrown as KafkaException that wraps an
> InvalidRecordException, which may be too general and we could consider just
> throwing the InvalidRecordException directly. But that could be an
> orthogonal discussion if we agrees that CRC failures should not be
> considered in this KIP.
> 
> ----------------
> 
> Now some detailed comments:
> 
> 4. Could we consider adding the processor context in the handle() function
> as well? This context will be wrapping as the source node that is about to
> process the record. This could expose more info like which task / source
> node sees this error, which timestamp of the message, etc, and also can
> allow users to implement their handlers by exposing some metrics, by
> calling context.forward() to implement the "send to bad queue" behavior etc.
> 
> 5. Could you add the string name of
> StreamsConfig.DEFAULT_RECORD_EXCEPTION_HANDLER as well in the KIP?
> Personally I find "default" prefix a bit misleading since we do not allow
> users to override it per-node yet. But I'm okay either way as I can see we
> may extend it in the future and probably would like to not rename the
> config again. Also from the experience of `default partitioner` and
> `default timestamp extractor` we may also make sure that the passed in
> object can be either a string "class name" or a class object?
> 
> 
> Guozhang
> 
> 
> On Wed, Jun 7, 2017 at 2:16 PM, Jan Filipiak <Ja...@trivago.com>
> wrote:
> 
>> Hi Eno,
>> 
>> On 07.06.2017 22:49, Eno Thereska wrote:
>> 
>>> Comments inline:
>>> 
>>> On 5 Jun 2017, at 18:19, Jan Filipiak <Ja...@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?
>>> 
>> I don't think so. IMO you can not reasonably continue parsing when the
>> checksum of a message is not correct. If you are not sure you got the
>> correct length, how can you be sure to find the next record? I would always
>> straight fail in all cases. Its to hard for me to understand why one would
>> try to continue. I mentioned CRC's because thats the only bad pills I ever
>> saw so far. But I am happy that it just stopped and I could check what was
>> going on. This will also be invasive in the client code then.
>> 
>> If you ask me, I am always going to vote for "grind to halt" let the
>> developers see what happened and let them fix it. It helps building good
>> kafka experiences and better software and architectures. For me this is:
>> "force the user todo the right thing". https://youtu.be/aAb7hSCtvGw?t=374
>> eg. not letting unexpected input slip by.  Letting unexpected input slip by
>> is what bought us 15+years of war of all sorts of ingestion attacks. I
>> don't even dare to estimate how many missingrecords-search-teams going be
>> formed, maybe some hackerone for stream apps :D
>> 
>> Best Jan
>> 
>> 
>>> 
>>>>> 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 sound?
>>>>> 
>>>> 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.
>>> 
>>> Thanks,
>>> Eno
>>> 
>>> 
>>> 6. Will add an end-to-end example as Michael suggested.
>>>>> 
>>>>> Thanks
>>>>> Eno
>>>>> 
>>>>> 
>>>>> 
>>>>> On 4 Jun 2017, at 02:35, Matthias J. Sax <ma...@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
>>>>>>>>> kafka
>>>>>>>>> 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
>>>>>>>>>> giving
>>>>>>>>>> people options and there are times when you don't want to fail
>>>>>>>>>> fast.
>>>>>>>>>> 
>>>>>>>>>> 
>>>>>>>>>> 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
>>>>>>>>>>> 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.
>>>>>>>>>>>>>>>> 
>>>>>>>>>>>>>>>> 
>>>>>>>>>>>>>>>> 
>> 
> 
> 
> -- 
> -- Guozhang

Re: [DISCUSS]: KIP-161: streams record processing exception handlers

[Guozhang Wang <wa...@gmail.com>]

Eno, Thanks for bringing this proposal up and sorry for getting late on
this. Here are my two cents:

1. First some meta comments regarding "fail fast" v.s. "making progress". I
agree that in general we should better "enforce user to do the right thing"
in system design, but we also need to keep in mind that Kafka is a
multi-tenant system, i.e. from a Streams app's pov you probably would not
control the whole streaming processing pipeline end-to-end. E.g. Your input
data may not be controlled by yourself; it could be written by another app,
or another team in your company, or even a different organization, and if
an error happens maybe you cannot fix "to do the right thing" just by
yourself in time. In such an environment I think it is important to leave
the door open to let users be more resilient. So I find the current
proposal which does leave the door open for either fail-fast or make
progress quite reasonable.

2. On the other hand, if the question is whether we should provide a
built-in "send to bad queue" handler from the library, I think that might
be an overkill: with some tweaks (see my detailed comments below) on the
API we can allow users to implement such handlers pretty easily. In fact, I
feel even "LogAndThresholdExceptionHandler" is not necessary as a built-in
handler, as it would then require users to specify the threshold via
configs, etc. I think letting people provide such "eco-libraries" may be
better.

3. Regarding the CRC error: today we validate CRC on both the broker end
upon receiving produce requests and on consumer end upon receiving fetch
responses; and if the CRC validation fails in the former case it would not
be appended to the broker logs. So if we do see a CRC failure on the
consumer side it has to be that either we have a flipped bit on the broker
disks or over the wire. For the first case it is fatal while for the second
it is retriable. Unfortunately we cannot tell which case it is when seeing
CRC validation failures. But in either case, just skipping and making
progress seems not a good choice here, and hence I would personally exclude
these errors from the general serde errors to NOT leave the door open of
making progress.

Currently such errors are thrown as KafkaException that wraps an
InvalidRecordException, which may be too general and we could consider just
throwing the InvalidRecordException directly. But that could be an
orthogonal discussion if we agrees that CRC failures should not be
considered in this KIP.

----------------

Now some detailed comments:

4. Could we consider adding the processor context in the handle() function
as well? This context will be wrapping as the source node that is about to
process the record. This could expose more info like which task / source
node sees this error, which timestamp of the message, etc, and also can
allow users to implement their handlers by exposing some metrics, by
calling context.forward() to implement the "send to bad queue" behavior etc.

5. Could you add the string name of
StreamsConfig.DEFAULT_RECORD_EXCEPTION_HANDLER as well in the KIP?
Personally I find "default" prefix a bit misleading since we do not allow
users to override it per-node yet. But I'm okay either way as I can see we
may extend it in the future and probably would like to not rename the
config again. Also from the experience of `default partitioner` and
`default timestamp extractor` we may also make sure that the passed in
object can be either a string "class name" or a class object?


Guozhang


On Wed, Jun 7, 2017 at 2:16 PM, Jan Filipiak <Ja...@trivago.com>
wrote:

> Hi Eno,
>
> On 07.06.2017 22:49, Eno Thereska wrote:
>
>> Comments inline:
>>
>> On 5 Jun 2017, at 18:19, Jan Filipiak <Ja...@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?
>>
> I don't think so. IMO you can not reasonably continue parsing when the
> checksum of a message is not correct. If you are not sure you got the
> correct length, how can you be sure to find the next record? I would always
> straight fail in all cases. Its to hard for me to understand why one would
> try to continue. I mentioned CRC's because thats the only bad pills I ever
> saw so far. But I am happy that it just stopped and I could check what was
> going on. This will also be invasive in the client code then.
>
> If you ask me, I am always going to vote for "grind to halt" let the
> developers see what happened and let them fix it. It helps building good
> kafka experiences and better software and architectures. For me this is:
> "force the user todo the right thing". https://youtu.be/aAb7hSCtvGw?t=374
> eg. not letting unexpected input slip by.  Letting unexpected input slip by
> is what bought us 15+years of war of all sorts of ingestion attacks. I
> don't even dare to estimate how many missingrecords-search-teams going be
> formed, maybe some hackerone for stream apps :D
>
> Best Jan
>
>
>>
>>>> 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 sound?
>>>>
>>> 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.
>>
>> Thanks,
>> Eno
>>
>>
>> 6. Will add an end-to-end example as Michael suggested.
>>>>
>>>> Thanks
>>>> Eno
>>>>
>>>>
>>>>
>>>> On 4 Jun 2017, at 02:35, Matthias J. Sax <ma...@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
>>>>>>>> kafka
>>>>>>>> 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
>>>>>>>>> giving
>>>>>>>>> people options and there are times when you don't want to fail
>>>>>>>>> fast.
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> 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
>>>>>>>>>> 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.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>


-- 
-- Guozhang

Re: [DISCUSS]: KIP-161: streams record processing exception handlers

[Jan Filipiak <Ja...@trivago.com>]

Hi Eno,

On 07.06.2017 22:49, Eno Thereska wrote:
> Comments inline:
>
>> On 5 Jun 2017, at 18:19, Jan Filipiak <Ja...@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?
I don't think so. IMO you can not reasonably continue parsing when the 
checksum of a message is not correct. If you are not sure you got the 
correct length, how can you be sure to find the next record? I would 
always straight fail in all cases. Its to hard for me to understand why 
one would try to continue. I mentioned CRC's because thats the only bad 
pills I ever saw so far. But I am happy that it just stopped and I could 
check what was going on. This will also be invasive in the client code then.

If you ask me, I am always going to vote for "grind to halt" let the 
developers see what happened and let them fix it. It helps building good 
kafka experiences and better software and architectures. For me this is: 
"force the user todo the right thing". 
https://youtu.be/aAb7hSCtvGw?t=374 eg. not letting unexpected input slip 
by.  Letting unexpected input slip by is what bought us 15+years of war 
of all sorts of ingestion attacks. I don't even dare to estimate how 
many missingrecords-search-teams going be formed, maybe some hackerone 
for stream apps :D

Best Jan
>
>>>
>>> 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 sound?
>> 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.
>
> Thanks,
> Eno
>
>
>>> 6. Will add an end-to-end example as Michael suggested.
>>>
>>> Thanks
>>> Eno
>>>
>>>
>>>
>>>> On 4 Jun 2017, at 02:35, Matthias J. Sax <ma...@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 <Ja...@trivago.com>
>>>>>> wrote:
>>>>>>
>>>>>>> IMHO your doing it wrong then. + building to much support into the kafka
>>>>>>> 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 giving
>>>>>>>> people options and there are times when you don't want to fail fast.
>>>>>>>>
>>>>>>>>
>>>>>>>> On Fri, 2 Jun 2017 at 11:00 Jan Filipiak <Ja...@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 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 <ma...@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.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>

Re: [DISCUSS]: KIP-161: streams record processing exception handlers

[Jan Filipiak <Ja...@trivago.com>]

Hi Eno,

On 07.06.2017 22:49, Eno Thereska wrote:
> Comments inline:
>
>> On 5 Jun 2017, at 18:19, Jan Filipiak <Ja...@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?
I don't think so. IMO you can not reasonably continue parsing when the 
checksum of a message is not correct. If you are not sure you got the 
correct length, how can you be sure to find the next record? I would 
always straight fail in all cases. Its to hard for me to understand why 
one would try to continue. I mentioned CRC's because thats the only bad 
pills I ever saw so far. But I am happy that it just stopped and I could 
check what was going on. This will also be invasive in the client code then.

If you ask me, I am always going to vote for "grind to halt" let the 
developers see what happened and let them fix it. It helps building good 
kafka experiences and better software and architectures. For me this is: 
"force the user todo the right thing". 
https://youtu.be/aAb7hSCtvGw?t=374 eg. not letting unexpected input slip 
by.  Letting unexpected input slip by is what bought us 15+years of war 
of all sorts of ingestion attacks. I don't even dare to estimate how 
many missingrecords-search-teams going be formed, maybe some hackerone 
for stream apps :D

Best Jan
>
>>>
>>> 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 sound?
>> 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.
>
> Thanks,
> Eno
>
>
>>> 6. Will add an end-to-end example as Michael suggested.
>>>
>>> Thanks
>>> Eno
>>>
>>>
>>>
>>>> On 4 Jun 2017, at 02:35, Matthias J. Sax <ma...@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 <Ja...@trivago.com>
>>>>>> wrote:
>>>>>>
>>>>>>> IMHO your doing it wrong then. + building to much support into the kafka
>>>>>>> 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 giving
>>>>>>>> people options and there are times when you don't want to fail fast.
>>>>>>>>
>>>>>>>>
>>>>>>>> On Fri, 2 Jun 2017 at 11:00 Jan Filipiak <Ja...@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 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 <ma...@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.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>

Re: [DISCUSS]: KIP-161: streams record processing exception handlers

[Eno Thereska <en...@gmail.com>]

Comments inline:

> On 5 Jun 2017, at 18:19, Jan Filipiak <Ja...@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 sound?
> 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.

Thanks,
Eno


>> 6. Will add an end-to-end example as Michael suggested.
>> 
>> Thanks
>> Eno
>> 
>> 
>> 
>>> On 4 Jun 2017, at 02:35, Matthias J. Sax <ma...@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 <Ja...@trivago.com>
>>>>> wrote:
>>>>> 
>>>>>> IMHO your doing it wrong then. + building to much support into the kafka
>>>>>> 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 giving
>>>>>>> people options and there are times when you don't want to fail fast.
>>>>>>> 
>>>>>>> 
>>>>>>> On Fri, 2 Jun 2017 at 11:00 Jan Filipiak <Ja...@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 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 <ma...@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.
>>>>>>>>>>>>> 
>>>>>>>>>>>>> 
> 

Re: [DISCUSS]: KIP-161: streams record processing exception handlers

["Matthias J. Sax" <ma...@confluent.io>]

@Jan: EOS will be turned off by default in 0.11. I assume, we might
enable it by default in a later release but the will be always a config
to disable it.


-Matthias

On 6/5/17 10:19 AM, Jan Filipiak 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.
>> 2. Damian has convinced me that the KIP should just be for
>> deserialisation from the network, not from local state store DBs. For
>> the latter we'll follow the current way of failing since the DB is
>> likely corrupt.
>>
>> 3. Dead letter queue option. There was never any intention here to do
>> anything super clever like attempt to re-inject the failed records
>> from the dead letter queue back into the system. Reasoning about when
>> that'd be useful in light of all sorts of semantic breakings would be
>> hard (arguably impossible). The idea was to just have a place to have
>> all these dead records to help with subsequent debugging. We could
>> also just log a whole bunch of info for a poison pill record and not
>> have a dead letter queue at all. Perhaps that's a better, simpler,
>> starting point.
> +1
>>
>> 4. Agree with Jay on style, a DefaultHandler with some config options.
>> Will add options to KIP. Also as part of this let's remove the
>> threshold logger since it gets complex and arguably the ROI is low.
>>
>> 5. Jay's JSON example, where serialisation passes but the JSON message
>> doesn't have the expected fields, is an interesting one. It's a bit
>> complicated to handle this in the middle of processing. For example,
>> some operators in the DAG might actually find the needed JSON fields
>> and make progress, but other operators, for the same record, might not
>> find their fields and will throw an exception.
>>
>> 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 sound?
> 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)
>>
>> 6. Will add an end-to-end example as Michael suggested.
>>
>> Thanks
>> Eno
>>
>>
>>
>>> On 4 Jun 2017, at 02:35, Matthias J. Sax <ma...@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
>>>>> <Ja...@trivago.com>
>>>>> wrote:
>>>>>
>>>>>> IMHO your doing it wrong then. + building to much support into the
>>>>>> kafka
>>>>>> 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
>>>>>>> giving
>>>>>>> people options and there are times when you don't want to fail fast.
>>>>>>>
>>>>>>>
>>>>>>> On Fri, 2 Jun 2017 at 11:00 Jan Filipiak <Ja...@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
>>>>>>>> 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
>>>>>>>>> <ma...@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.
>>>>>>>>>>>>>
>>>>>>>>>>>>>
> 

Re: [DISCUSS]: KIP-161: streams record processing exception handlers

[Eno Thereska <en...@gmail.com>]

Comments inline:

> On 5 Jun 2017, at 18:19, Jan Filipiak <Ja...@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 sound?
> 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.

Thanks,
Eno


>> 6. Will add an end-to-end example as Michael suggested.
>> 
>> Thanks
>> Eno
>> 
>> 
>> 
>>> On 4 Jun 2017, at 02:35, Matthias J. Sax <ma...@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 <Ja...@trivago.com>
>>>>> wrote:
>>>>> 
>>>>>> IMHO your doing it wrong then. + building to much support into the kafka
>>>>>> 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 giving
>>>>>>> people options and there are times when you don't want to fail fast.
>>>>>>> 
>>>>>>> 
>>>>>>> On Fri, 2 Jun 2017 at 11:00 Jan Filipiak <Ja...@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 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 <ma...@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.
>>>>>>>>>>>>> 
>>>>>>>>>>>>> 
> 

Re: [DISCUSS]: KIP-161: streams record processing exception handlers

[Jan Filipiak <Ja...@trivago.com>]

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.
> 2. Damian has convinced me that the KIP should just be for deserialisation from the network, not from local state store DBs. For the latter we'll follow the current way of failing since the DB is likely corrupt.
>
> 3. Dead letter queue option. There was never any intention here to do anything super clever like attempt to re-inject the failed records from the dead letter queue back into the system. Reasoning about when that'd be useful in light of all sorts of semantic breakings would be hard (arguably impossible). The idea was to just have a place to have all these dead records to help with subsequent debugging. We could also just log a whole bunch of info for a poison pill record and not have a dead letter queue at all. Perhaps that's a better, simpler, starting point.
+1
>
> 4. Agree with Jay on style, a DefaultHandler with some config options. Will add options to KIP. Also as part of this let's remove the threshold logger since it gets complex and arguably the ROI is low.
>
> 5. Jay's JSON example, where serialisation passes but the JSON message doesn't have the expected fields, is an interesting one. It's a bit complicated to handle this in the middle of processing. For example, some operators in the DAG might actually find the needed JSON fields and make progress, but other operators, for the same record, might not find their fields and will throw an exception.
>
> 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 sound?
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)
>
> 6. Will add an end-to-end example as Michael suggested.
>
> Thanks
> Eno
>
>
>
>> On 4 Jun 2017, at 02:35, Matthias J. Sax <ma...@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 <Ja...@trivago.com>
>>>> wrote:
>>>>
>>>>> IMHO your doing it wrong then. + building to much support into the kafka
>>>>> 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 giving
>>>>>> people options and there are times when you don't want to fail fast.
>>>>>>
>>>>>>
>>>>>> On Fri, 2 Jun 2017 at 11:00 Jan Filipiak <Ja...@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 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 <ma...@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.
>>>>>>>>>>>>
>>>>>>>>>>>>

Re: [DISCUSS]: KIP-161: streams record processing exception handlers

[Eno Thereska <en...@gmail.com>]

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.

2. Damian has convinced me that the KIP should just be for deserialisation from the network, not from local state store DBs. For the latter we'll follow the current way of failing since the DB is likely corrupt.

3. Dead letter queue option. There was never any intention here to do anything super clever like attempt to re-inject the failed records from the dead letter queue back into the system. Reasoning about when that'd be useful in light of all sorts of semantic breakings would be hard (arguably impossible). The idea was to just have a place to have all these dead records to help with subsequent debugging. We could also just log a whole bunch of info for a poison pill record and not have a dead letter queue at all. Perhaps that's a better, simpler, starting point. 

4. Agree with Jay on style, a DefaultHandler with some config options. Will add options to KIP. Also as part of this let's remove the threshold logger since it gets complex and arguably the ROI is low. 

5. Jay's JSON example, where serialisation passes but the JSON message doesn't have the expected fields, is an interesting one. It's a bit complicated to handle this in the middle of processing. For example, some operators in the DAG might actually find the needed JSON fields and make progress, but other operators, for the same record, might not find their fields and will throw an exception.

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 sound?

6. Will add an end-to-end example as Michael suggested.

Thanks
Eno



> On 4 Jun 2017, at 02:35, Matthias J. Sax <ma...@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 <Ja...@trivago.com>
>>> wrote:
>>> 
>>>> IMHO your doing it wrong then. + building to much support into the kafka
>>>> 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 giving
>>>>> people options and there are times when you don't want to fail fast.
>>>>> 
>>>>> 
>>>>> On Fri, 2 Jun 2017 at 11:00 Jan Filipiak <Ja...@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 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 <ma...@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.
>>>>>>>>>>> 
>>>>>>>>>>> 
>> 
> 

Re: [DISCUSS]: KIP-161: streams record processing exception handlers

[Eno Thereska <en...@gmail.com>]

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.

2. Damian has convinced me that the KIP should just be for deserialisation from the network, not from local state store DBs. For the latter we'll follow the current way of failing since the DB is likely corrupt.

3. Dead letter queue option. There was never any intention here to do anything super clever like attempt to re-inject the failed records from the dead letter queue back into the system. Reasoning about when that'd be useful in light of all sorts of semantic breakings would be hard (arguably impossible). The idea was to just have a place to have all these dead records to help with subsequent debugging. We could also just log a whole bunch of info for a poison pill record and not have a dead letter queue at all. Perhaps that's a better, simpler, starting point. 

4. Agree with Jay on style, a DefaultHandler with some config options. Will add options to KIP. Also as part of this let's remove the threshold logger since it gets complex and arguably the ROI is low. 

5. Jay's JSON example, where serialisation passes but the JSON message doesn't have the expected fields, is an interesting one. It's a bit complicated to handle this in the middle of processing. For example, some operators in the DAG might actually find the needed JSON fields and make progress, but other operators, for the same record, might not find their fields and will throw an exception.

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 sound?

6. Will add an end-to-end example as Michael suggested.

Thanks
Eno



> On 4 Jun 2017, at 02:35, Matthias J. Sax <ma...@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 <Ja...@trivago.com>
>>> wrote:
>>> 
>>>> IMHO your doing it wrong then. + building to much support into the kafka
>>>> 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 giving
>>>>> people options and there are times when you don't want to fail fast.
>>>>> 
>>>>> 
>>>>> On Fri, 2 Jun 2017 at 11:00 Jan Filipiak <Ja...@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 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 <ma...@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.
>>>>>>>>>>> 
>>>>>>>>>>> 
>> 
> 

Re: [DISCUSS]: KIP-161: streams record processing exception handlers

["Matthias J. Sax" <ma...@confluent.io>]

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 <Ja...@trivago.com>
>> wrote:
>>
>>> IMHO your doing it wrong then. + building to much support into the kafka
>>> 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 giving
>>>> people options and there are times when you don't want to fail fast.
>>>>
>>>>
>>>> On Fri, 2 Jun 2017 at 11:00 Jan Filipiak <Ja...@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 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 <ma...@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.
>>>>>>>>>>
>>>>>>>>>>
> 

Re: [DISCUSS]: KIP-161: streams record processing exception handlers

[Jan Filipiak <Ja...@trivago.com>]

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 <Ja...@trivago.com>
> wrote:
>
>> IMHO your doing it wrong then. + building to much support into the kafka
>> 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 giving
>>> people options and there are times when you don't want to fail fast.
>>>
>>>
>>> On Fri, 2 Jun 2017 at 11:00 Jan Filipiak <Ja...@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 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 <ma...@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.
>>>>>>>>>
>>>>>>>>>

Re: [DISCUSS]: KIP-161: streams record processing exception handlers

[Jan Filipiak <Ja...@trivago.com>]

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 <Ja...@trivago.com>
> wrote:
>
>> IMHO your doing it wrong then. + building to much support into the kafka
>> 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 giving
>>> people options and there are times when you don't want to fail fast.
>>>
>>>
>>> On Fri, 2 Jun 2017 at 11:00 Jan Filipiak <Ja...@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 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 <ma...@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.
>>>>>>>>>
>>>>>>>>>

Re: [DISCUSS]: KIP-161: streams record processing exception handlers

[Jay Kreps <ja...@confluent.io>]

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 <Ja...@trivago.com>
wrote:

> IMHO your doing it wrong then. + building to much support into the kafka
> 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 giving
>> people options and there are times when you don't want to fail fast.
>>
>>
>> On Fri, 2 Jun 2017 at 11:00 Jan Filipiak <Ja...@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 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 <ma...@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.
>>>>>>>>
>>>>>>>>
>>>
>

Re: [DISCUSS]: KIP-161: streams record processing exception handlers

[Jan Filipiak <Ja...@trivago.com>]

IMHO your doing it wrong then. + building to much support into the kafka 
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 giving
> people options and there are times when you don't want to fail fast.
>
>
> On Fri, 2 Jun 2017 at 11:00 Jan Filipiak <Ja...@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 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 <ma...@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.
>>>>>>>
>>

Re: [DISCUSS]: KIP-161: streams record processing exception handlers

[Damian Guy <da...@gmail.com>]

Jan, you have a choice to Fail fast if you want. This is about giving
people options and there are times when you don't want to fail fast.


On Fri, 2 Jun 2017 at 11:00 Jan Filipiak <Ja...@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 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 <ma...@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.
> >>>>>
> >>>>
> >>
>
>

Re: [DISCUSS]: KIP-161: streams record processing exception handlers

[Damian Guy <da...@gmail.com>]

Jan, you have a choice to Fail fast if you want. This is about giving
people options and there are times when you don't want to fail fast.


On Fri, 2 Jun 2017 at 11:00 Jan Filipiak <Ja...@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 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 <ma...@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.
> >>>>>
> >>>>
> >>
>
>

Re: [DISCUSS]: KIP-161: streams record processing exception handlers

[Jan Filipiak <Ja...@trivago.com>]

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 <ma...@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 <Ja...@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.
>>>>>
>>>>
>>

Re: [DISCUSS]: KIP-161: streams record processing exception handlers

[Jan Filipiak <Ja...@trivago.com>]

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 <ma...@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 <Ja...@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.
>>>>>
>>>>
>>

Re: [DISCUSS]: KIP-161: streams record processing exception handlers

[Damian Guy <da...@gmail.com>]

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 <ma...@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 <Ja...@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.
> >>>
> >>
> >>
> >
>
>

Re: [DISCUSS]: KIP-161: streams record processing exception handlers

[Damian Guy <da...@gmail.com>]

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 <ma...@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 <Ja...@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.
> >>>
> >>
> >>
> >
>
>