Kafka Streams - 是否可以减少多个聚合创建的内部主题的数量

Question

I have a Kafka Streams app that groups incoming messages by several values. For example:

Example message:

{ "gender": "female", "location": "canada", "age-group": "25-30" }

Topology:

table
    .groupBy((key, value) -> groupByGender) // example key: female
    .count("gender-counts");

table
    .groupBy((key, value) -> groupByLocation) // example key: canada
    .count("location-counts");

table
    .groupBy((key, value) -> groupByAgeGroup) // example key: 25-30
    .count("age-group-counts");

This results in lots of topics:

my-consumer-gender-counts-changelog
my-consumer-gender-counts-repartition
my-consumer-location-counts-changelog
my-consumer-location-counts-repartition
my-consumer-age-group-counts-changelog
my-consumer-age-group-counts-repartition

It would be nice if we could send multiple aggregations to a single state store, and include the group by value as part of the key. For example:

table
    .groupBy((key, value) -> groupByGender) // example key: female_gender
    .count("counts");

table
    .groupBy((key, value) -> groupByLocation) // example key: canada_location
    .count("counts");

table
    .groupBy((key, value) -> groupByAgeGroup) // example key: 25-30_age_group
    .count("counts");

This would result in far fewer topics:

counts-changelog
counts-repartition

This currently doesn't appear to be possible (using the DSL anyways), since using the groupBy operator creates an internal topic for repartitioning, so if we have multiple sub-topologies that groupBy different things, then Kafka Streams will attempt to register the same repartitioning topic from multiple sources. This results in the following error:

org.apache.kafka.streams.errors.TopologyBuilderException: Invalid topology building: Topic counts-repartition has already been registered by another source.
        at org.apache.kafka.streams.processor.TopologyBuilder.validateTopicNotAlreadyRegistered(TopologyBuilder.java:518)

If groupBy could return more than one record (eg like flatMap does), then we could return a collection of records (one record for each grouping), but this too doesn't seem to be possible using the DSL.

My question is, given a single record that can be grouped by multiple values (eg { "gender": "female", "location": "canada", "age-group": "25-30" } ), should the creation of multiple topics (2 for each grouping) ever be of concern (eg what we we had 100 different groupings)? Are there other strategies that might be a better fit when a single record could be grouped by several values? Is what I'm proposing (sinking multiple aggregations to a single changelog topic) a bad idea (even when the number of unique keys is very low)?

Answer 1

If you want to group by different attributes, you cannot avoid multiple repartitioning topics. Assume you have two grouping attributes g1 and g2 and three records with the following values:

r1 = g1:A, g2:1
r2 = g1:A, g2:2
r3 = g1:B, g2:2

Thus, to correctly aggregate the records based on g1 , records r1 and r2 must be grouped together. Assume your repartitioning topic has 2 partitions p1 and p2 , the record would get redistributes like

p1: r1, r2
p2: r3,

On the other hand, if you aggregate on r2 , records r2 and r3 must be grouped together:

p1: r1
p2: r2,r3

Note, that r2 must go to different partitions for both cases, and thus, it's not possible to use a single topic, but you need one topic per grouping. (This is not Kafka specific -- any other framework would need to replicate and redistribute the date multiple times, too).

Theoretically it's possible to reduce the number of topic if you add more semantical information (like super-key, sub-key, or 1-to-1-key mapping). But that's not supported by Kafka Streams (and AFAIK, no other comparable system).