Just to clarify, intermediate topics should in most cases not be compacted
for exactly the reasons if your application depends on all intermediate
data. For the final topic, it makes sense. If you also consume intermediate
topics for web application, one solution is to split it into two topics
(like topic-raw for Flink and topic-compacted for applications) and live
with some amplification.
On Thu, Feb 25, 2021 at 12:11 AM Rex Fenley <r...@remind101.com> wrote:
> All of our Flink jobs are (currently) used for web applications at the end
> of the day. We see a lot of latency spikes from record amplification and we
> were at first hoping we could pass intermediate results through Kafka and
> compact them to lower the record amplification, but then it hit me that
> this might be an issue.
>
> Thanks for the detailed explanation, though it seems like we'll need to
> look for a different solution or only compact on records we know will never
> mutate.
>
> On Wed, Feb 24, 2021 at 6:38 AM Arvid Heise <ar...@apache.org> wrote:
>
>> Jan's response is correct, but I'd like to emphasize the impact on a
>> Flink application.
>>
>> If the compaction happens before the data arrives in Flink, the
>> intermediate updates are lost and just the final result appears.
>> Also if you restart your Flink application and reprocess older data, it
>> will naturally only see the compacted data save for the active segment.
>>
>> So how to make it deterministic? Simply drop topic compaction. If it's
>> coming from CDC and you want to process and produce changelog streams over
>> several applications, you probably don't want to use log compactions
>> anyways.
>>
>> Log compaction only makes sense in the snapshot topic that displays the
>> current state (KTable), where you don't think in CDC updates anymore but
>> just final records, like
>> (user_id: 1, state: "california")
>> (user_id: 1, state: "ohio")
>>
>> Usually, if you use CDC in your company, each application is responsible
>> for building its own current model by tapping in the relevant changes. Log
>> compacted topics would then only appear at the end of processing, when you
>> hand it over towards non-analytical applications, such as Web Apps.
>>
>> On Wed, Feb 24, 2021 at 10:01 AM Jan Lukavský <je...@seznam.cz> wrote:
>>
>>> Hi Rex,
>>>
>>> If I understand correctly, you are concerned about behavior of Kafka
>>> source in the case of compacted topic, right? If that is the case, then
>>> this is not directly related to Flink, Flink will expose the behavior
>>> defined by Kafka. You can read about it for instance here [1]. TL;TD - your
>>> pipeline is guaranteed to see every record written to topic (every single
>>> update, be it later "overwritten" or not) if it processes the record with
>>> latency at most 'delete.retention.ms'. This is configurable per topic -
>>> default 24 hours. If you want to reprocess the data later, your consumer
>>> might see only resulting compacted ("retracted") stream, and not every
>>> record actually written to the topic.
>>>
>>> Jan
>>>
>>> [1]
>>> https://medium.com/swlh/introduction-to-topic-log-compaction-in-apache-kafka-3e4d4afd2262
>>> On 2/24/21 3:14 AM, Rex Fenley wrote:
>>>
>>> Apologies, forgot to finish. If the Kafka source performs its own
>>> retractions of old data on key (user_id) for every append it receives, it
>>> should resolve this discrepancy I think.
>>>
>>> Again, is this true? Anything else I'm missing?
>>>
>>> Thanks!
>>>
>>>
>>> On Tue, Feb 23, 2021 at 6:12 PM Rex Fenley <r...@remind101.com> wrote:
>>>
>>>> Hi,
>>>>
>>>> I'm concerned about the impacts of Kafka's compactions when sending
>>>> data between running flink jobs.
>>>>
>>>> For example, one job produces retract stream records in sequence of
>>>> (false, (user_id: 1, state: "california") -- retract
>>>> (true, (user_id: 1, state: "ohio")) -- append
>>>> Which is consumed by Kafka and keyed by user_id, this could end up
>>>> compacting to just
>>>> (true, (user_id: 1, state: "ohio")) -- append
>>>> If some other downstream Flink job has a filter on state ==
>>>> "california" and reads from the Kafka stream, I assume it will miss the
>>>> retract message altogether and produce incorrect results.
>>>>
>>>> Is this true? How do we prevent this from happening? We need to use
>>>> compaction since all our jobs are based on CDC and we can't just drop data
>>>> after x number of days.
>>>>
>>>> Thanks
>>>>
>>>> --
>>>>
>>>> Rex Fenley | Software Engineer - Mobile and Backend
>>>>
>>>>
>>>> Remind.com <https://www.remind.com/> | BLOG <http://blog.remind.com/>
>>>> | FOLLOW US <https://twitter.com/remindhq> | LIKE US
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>>>>
>>>
>>>
>>> --
>>>
>>> Rex Fenley | Software Engineer - Mobile and Backend
>>>
>>>
>>> Remind.com <https://www.remind.com/> | BLOG <http://blog.remind.com/>
>>> | FOLLOW US <https://twitter.com/remindhq> | LIKE US
>>> <https://www.facebook.com/remindhq>
>>>
>>>
>
> --
>
> Rex Fenley | Software Engineer - Mobile and Backend
>
>
> Remind.com <https://www.remind.com/> | BLOG <http://blog.remind.com/> |
> FOLLOW US <https://twitter.com/remindhq> | LIKE US
> <https://www.facebook.com/remindhq>
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