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https://issues.apache.org/jira/browse/KAFKA-6643?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Navinder Brar updated KAFKA-6643:
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Description:
In the current scenario, Kafka Streams has changelog Kafka topics(internal
topics having all the data for the store) which are used to build the state of
replicas. So, if we keep the number of standby replicas as 1, we still have
more availability for persistent state stores as changelog Kafka topics are
also replicated depending upon broker replication policy but that also means we
are using at least 4 times the space(1 master store, 1 replica store, 1
changelog, 1 changelog replica).
Now if we have an year's data in persistent stores(rocksdb), we don't want the
changelog topics to have an year's data as it will put an unnecessary burden on
brokers(in terms of space). If we have to scale our kafka streams
application(having 200-300 TB's of data) we have to scale the kafka brokers as
well. We want to reduce this dependency and find out ways to just use changelog
topic as a queue, having just 2 or 3 days of data and warm up the replicas from
scratch in some other way.
I have few proposals in that respect.
1. Use a new kafka topic related to each partition whi
was:
In the current scenario, Kafka Streams has changelog Kafka topics(internal
topics having all the data for the store) which are used to build the state of
replicas. So, if we keep the number of standby replicas as 1, we still have
more availability for persistent state stores as changelog Kafka topics are
also replicated depending upon broker replication policy but that also means we
are using at least 4 times the space(1 master store, 1 replica store, 1
changelog, 1 changelog replica).
Now if we have an year's data in persistent stores(rocksdb), we don't want the
changelog topics to have an year's data as it will put an unnecessary burden on
brokers(in terms of space). If we have to scale our kafka streams
application(having 200-300 TB's of data) we have to scale the kafka brokers as
well. We want to reduce this dependency and find out ways to just use changelog
topic as a queue, having just 2 or 3 days of data and warm up the replicas from
scratch in some other way.
> Warm up new replicas from scratch when changelog topic has retention time
> -------------------------------------------------------------------------
>
> Key: KAFKA-6643
> URL: https://issues.apache.org/jira/browse/KAFKA-6643
> Project: Kafka
> Issue Type: New Feature
> Components: streams
> Reporter: Navinder Brar
> Priority: Major
>
> In the current scenario, Kafka Streams has changelog Kafka topics(internal
> topics having all the data for the store) which are used to build the state
> of replicas. So, if we keep the number of standby replicas as 1, we still
> have more availability for persistent state stores as changelog Kafka topics
> are also replicated depending upon broker replication policy but that also
> means we are using at least 4 times the space(1 master store, 1 replica
> store, 1 changelog, 1 changelog replica).
> Now if we have an year's data in persistent stores(rocksdb), we don't want
> the changelog topics to have an year's data as it will put an unnecessary
> burden on brokers(in terms of space). If we have to scale our kafka streams
> application(having 200-300 TB's of data) we have to scale the kafka brokers
> as well. We want to reduce this dependency and find out ways to just use
> changelog topic as a queue, having just 2 or 3 days of data and warm up the
> replicas from scratch in some other way.
> I have few proposals in that respect.
> 1. Use a new kafka topic related to each partition whi
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