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https://issues.apache.org/jira/browse/KAFKA-6953?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16495388#comment-16495388
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Flavio Stutz commented on KAFKA-6953:
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[~guozhang], you are right on your observations, but doing it by hand is very
laborious. We have to create custom REST endpoints, coordinate schedulers and
make deployments aside Kafka Streams Applications and, preferably, publish the
results back to a Kafka topic in order to achieve the desired processing result
(being the global aggregation here).
The idea here is KStreams to have a native way to handle this type of
requirement, because today users are getting confused and creating various
patterns to manage this sort of problem.
On the GlobalKTable discussions, I saw some users looking for this kind of
global aggregation feature, but GlobalKTable wasn't made for handling huge
distributed writes as it is "global" and has possible bottlenecks (am I wrong?).
> [Streams] Schedulable KTable as Graph source (for minimizing aggregation
> pressure)
> ----------------------------------------------------------------------------------
>
> Key: KAFKA-6953
> URL: https://issues.apache.org/jira/browse/KAFKA-6953
> Project: Kafka
> Issue Type: New Feature
> Components: streams
> Reporter: Flavio Stutz
> Priority: Major
>
> === PROBLEM ===
> We have faced the following scenario/problem in a lot of situations with
> KStreams:
> - Huge incoming data being processed by numerous application instances
> - Need to aggregate, or count the overall data as a single value
> (something like "count the total number of messages that has been processed
> among all distributed instances")
> - The challenge here is to manage this kind of situation without any
> bottlenecks. We don't need the overall aggregation of all instances states at
> each processed message, so it is possible to store the partial aggregations
> on local stores and, at time to time, query those states and aggregate them,
> avoiding bottlenecks.
> Some ways we KNOW it wouldn't work because of bottlenecks:
> - Sink all instances local counter/aggregation result to a Topic with a
> single partition so that we could have another Graph with a single instance
> that could aggregate all results
> - In this case, if I had 500 instances processing 1000/s each (with
> no bottlenecks), I would have a single partition topic with 500k messages/s
> for my single aggregating instance to process that much messages (IMPOSSIBLE
> bottleneck)
> === TRIALS ===
> These are some ways we managed to do this:
> - Expose a REST endpoint so that Prometheus could extract local metrics of
> each application instance's state stores and them calculate the total count
> on Prometheus using queries
> - we don't like this much because we believe KStreams was meant to
> INPUT and OUTPUT data using Kafka Topics for simplicity and power
> - Create a scheduled Punctuate at the end of the Graph so that we can
> query (using getAllMetadata) all other instances's state store counters, sum
> them all and them publish to another Kafka Topic from time to time.
> - For this to work we created a way so that only one application
> instance's Punctuate algorithm would perform the calculations (something like
> a master election through instance ids and metadata)
> === PROPOSAL ===
> Create a new DSL Source with the following characteristics:
> - Source parameters: "scheduled time" (using cron's like config), "state
> store name", bool "from all application instances"
> - Behavior: At the desired time, query all K,V tuples from the state store
> and source those messages to the Graph
> - If "from all application instances" is true, query the tuples
> from all application instances state stores and source them all, concatenated
> - This is a way to create a "timed aggregation barrier" to avoid
> bottlenecks. With this we could enhance the ability of KStreams to better
> handle the CAP Theorem characteristics, so that one could choose to have
> Consistency over Availability.
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