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From Mahendra Kariya <mahendra.kar...@go-jek.com>
Subject Re: Debugging Kafka Streams Windowing
Date Tue, 02 May 2017 10:27:43 GMT
Hi Matthias,

What we did was read the data from sink topic and print it to console. And
here's the raw data from that topic (the counts are randomized). As we can
see, the data is certainly missing for some time windows. For instance,
after 1493693760, the next timestamp for which the data is present
is 1493694300. That's around 9 minutes of data missing.

And this is just one instance. There are a lot of such instances in this
file.



On Sun, Apr 30, 2017 at 11:23 AM, Mahendra Kariya <
mahendra.kariya@go-jek.com> wrote:

> Thanks for the update Matthias! And sorry for the delayed response.
>
> The reason we use .aggregate() is because we want to count the number of
> unique values for a particular field in the message. So, we just add that
> particular field's value in the HashSet and then take the size of the
> HashSet.
>
> On our side, we are also investigating and it looks like there might be a
> bug somewhere in our codebase. If that's the case, then it's quite possible
> that there is no bug in Kafka Streams, except the metric one.
>
> We will revert after confirming.
>
>
>
>
> On Sun, Apr 30, 2017 at 10:39 AM, Matthias J. Sax <matthias@confluent.io>
> wrote:
>
>> Just a follow up (we identified a bug in the "skipped records" metric).
>> The reported value is not correct.
>>
>>
>> On 4/28/17 9:12 PM, Matthias J. Sax wrote:
>> > Ok. That makes sense.
>> >
>> > Question: why do you use .aggregate() instead of .count() ?
>> >
>> > Also, can you share the code of you AggregatorFunction()? Did you change
>> > any default setting of StreamsConfig?
>> >
>> > I have still no idea what could go wrong. Maybe you can run with log
>> > level TRACE? Maybe we can get some insight from those.
>> >
>> >
>> > -Matthias
>> >
>> > On 4/27/17 11:41 PM, Mahendra Kariya wrote:
>> >> Oh good point!
>> >>
>> >> The reason why there is only one row corresponding to each time window
>> is
>> >> because it only contains the latest value for the time window. So what
>> we
>> >> did was we just dumped the data present in the sink topic to a db
>> using an
>> >> upsert query. The primary key of the table was time window. The file
>> that I
>> >> attached is actually the data present in the DB. And we know that
>> there is
>> >> no bug in our db dump code because we have been using it for a long
>> time in
>> >> production without any issues.
>> >>
>> >> The reason the count is zero for some time windows is because I
>> subtracted
>> >> a random number the actual values and rounded it off to zero; for
>> privacy
>> >> reason. The actual data doesn't have any zero values. I should have
>> >> mentioned this earlier. My bad!
>> >>
>> >> The stream topology code looks something like this.
>> >>
>> >> stream
>> >>     .filter()
>> >>     .map((key, value) -> new KeyValue<>(transform(key), value)
>> >>     .groupByKey()
>> >>     .aggregate(HashSet::new, AggregatorFunction(),
>> >> TimeWindows.of(60000).until(3600000))
>> >>     .mapValues(HashSet::size)
>> >>     .toStream()
>> >>     .map((key, value) -> convertToProtobufObject(key, value))
>> >>     .to()
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >> On Fri, Apr 28, 2017 at 1:13 PM, Matthias J. Sax <
>> matthias@confluent.io>
>> >> wrote:
>> >>
>> >>> Thanks for the details (sorry that I forgot that you did share the
>> >>> output already).
>> >>>
>> >>> Might be a dumb question, but what is the count for missing windows
in
>> >>> your seconds implementation?
>> >>>
>> >>> If there is no data for a window, it should not emit a window with
>> count
>> >>> zero, but nothing.
>> >>>
>> >>> Thus, looking at your output, I am wondering how it could contain line
>> >>> like:
>> >>>
>> >>>> 2017-04-27T04:53:00 0
>> >>>
>> >>> I am also wondering why your output only contains a single value per
>> >>> window. As Streams outputs multiple updates per window while the count
>> >>> is increasing, you should actually see multiple records per window.
>> >>>
>> >>> Your code is like this:
>> >>>
>> >>> stream.filter().groupByKey().count(TimeWindow.of(60000)).to();
>> >>>
>> >>> Or do you have something more complex?
>> >>>
>> >>>
>> >>> -Matthias
>> >>>
>> >>>
>> >>> On 4/27/17 9:16 PM, Mahendra Kariya wrote:
>> >>>>> Can you somehow verify your output?
>> >>>>
>> >>>>
>> >>>> Do you mean the Kafka streams output? In the Kafka Streams output,
>> we do
>> >>>> see some missing values. I have attached the Kafka Streams output
>> (for a
>> >>>> few hours) in the very first email of this thread for reference.
>> >>>>
>> >>>> Let me also summarise what we have done so far.
>> >>>>
>> >>>> We took a dump of the raw data present in the source topic. We wrote
>> a
>> >>>> script to read this data and do the exact same aggregations that
we
>> do
>> >>>> using Kafka Streams. And then we compared the output from Kafka
>> Streams
>> >>> and
>> >>>> our script.
>> >>>>
>> >>>> The difference that we observed in the two outputs is that there
>> were a
>> >>> few
>> >>>> rows (corresponding to some time windows) missing in the Streams
>> output.
>> >>>> For the time windows for which the data was present, the aggregated
>> >>> numbers
>> >>>> matched exactly.
>> >>>>
>> >>>> This means, either all the records for a particular time window
are
>> being
>> >>>> skipped, or none. Now this is highly unlikely to happen. Maybe there
>> is a
>> >>>> bug somewhere in the rocksdb state stores? Just a speculation, not
>> sure
>> >>>> though. And there could even be a bug in the reported metric.
>> >>>>
>> >>>
>> >>>
>> >>
>> >
>>
>>
>

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