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https://issues.apache.org/jira/browse/KAFKA-3543?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Guozhang Wang updated KAFKA-3543:
---------------------------------
Description:
Right now it seems that if you want to apply an arbitrary stateful
transformation to a stream, you either have to use a TransformerSupplier or
ProcessorSupplier sent to transform() or process(). The custom processor will
allow you to emit multiple new values, but the process() method currently
terminates that branch of the topology so you can't apply additional data flow.
transform() lets you continue the data flow, but forces you to emit a single
value for every input value.
(It actually doesn't quite force you to do this, since you can hold onto the
ProcessorContext and emit multiple, but that's probably not the ideal way to do
it :))
It seems desirable to somehow allow a transformation that emits multiple values
per input value. I'm not sure of the best way to factor this inside of the
current TransformerSupplier/Transformer architecture in a way that is clean and
efficient -- currently I'm doing the workaround above of just calling forward()
myself on the context and actually emitting dummy values which are filtered out
downstream.
-------------
It is worth considering adding a new flatTransofrm function as
{code}
<K1, V1> KStream<K1, V1> transform(TransformerSupplier<K, V,
Iterable<KeyValue<K1, V1>>> transformerSupplier, String... stateStoreNames)
{code}
which is essentially the same as
{code} transform().flatMap() {code}
was:
Right now it seems that if you want to apply an arbitrary stateful
transformation to a stream, you either have to use a TransformerSupplier or
ProcessorSupplier sent to transform() or process(). The custom processor will
allow you to emit multiple new values, but the process() method currently
terminates that branch of the topology so you can't apply additional data flow.
transform() lets you continue the data flow, but forces you to emit a single
value for every input value.
(It actually doesn't quite force you to do this, since you can hold onto the
ProcessorContext and emit multiple, but that's probably not the ideal way to do
it :))
It seems desirable to somehow allow a transformation that emits multiple values
per input value. I'm not sure of the best way to factor this inside of the
current TransformerSupplier/Transformer architecture in a way that is clean and
efficient -- currently I'm doing the workaround above of just calling forward()
myself on the context and actually emitting dummy values which are filtered out
downstream.
-------------
It is worth considering adding a new flatTransofrm
> Allow a variant of transform() which can emit multiple values
> -------------------------------------------------------------
>
> Key: KAFKA-3543
> URL: https://issues.apache.org/jira/browse/KAFKA-3543
> Project: Kafka
> Issue Type: Improvement
> Components: streams
> Affects Versions: 0.10.0.0
> Reporter: Greg Fodor
> Assignee: Guozhang Wang
> Labels: api
> Fix For: 0.10.1.0
>
>
> Right now it seems that if you want to apply an arbitrary stateful
> transformation to a stream, you either have to use a TransformerSupplier or
> ProcessorSupplier sent to transform() or process(). The custom processor will
> allow you to emit multiple new values, but the process() method currently
> terminates that branch of the topology so you can't apply additional data
> flow. transform() lets you continue the data flow, but forces you to emit a
> single value for every input value.
> (It actually doesn't quite force you to do this, since you can hold onto the
> ProcessorContext and emit multiple, but that's probably not the ideal way to
> do it :))
> It seems desirable to somehow allow a transformation that emits multiple
> values per input value. I'm not sure of the best way to factor this inside of
> the current TransformerSupplier/Transformer architecture in a way that is
> clean and efficient -- currently I'm doing the workaround above of just
> calling forward() myself on the context and actually emitting dummy values
> which are filtered out downstream.
> -------------
> It is worth considering adding a new flatTransofrm function as
> {code}
> <K1, V1> KStream<K1, V1> transform(TransformerSupplier<K, V,
> Iterable<KeyValue<K1, V1>>> transformerSupplier, String... stateStoreNames)
> {code}
> which is essentially the same as
> {code} transform().flatMap() {code}
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