Major Description
Today the downstream sub-topology's parallelism (aka the number of tasks) are purely dependent on the upstream sub-topology's parallelism, which ultimately depends on the source topic's num. partitions. However this does not work perfectly with dynamic scaling scenarios.
Imagine if your have a simple aggregation application, it would have two sub-topologies cut by the repartition topic, the first sub-topology would be very light as it reads from input topics and write to repartition topic based on the agg-key; the second sub-topology would do the actual work with the agg state store, etc, hence is heavy computational. Right now the first and second topology will always have the same number of tasks as the repartition topic num.partitions is defined to be the same as the source topic num.partitions, so to scale up we have to increase the number of input topic partitions.
One way to improve on that, is to use a default large number for repartition topics and also allow users to override it (either through DSL code, or through config). Doing this different sub-topologies would have different number of tasks, i.e. parallelism units. In addition, users may also use this config to "hint" the DSL translator to NOT create the repartition topics (i.e. to not break up the sub-topologies) when she has the knowledge of the data format.
Attachments
Issue Links
- is fixed by
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KAFKA-8611 Add KStream#repartition operation
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- Resolved
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1.
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Avoid repartitioning when key change doesn't change partitions |
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Open | Unassigned |