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https://issues.apache.org/jira/browse/BEAM-8121?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16936750#comment-16936750
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Alexey Romanenko commented on BEAM-8121:
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[~TauJan] Thank you for running these tests, I think it's very helpful.
{quote}
So my conclusions were that slow pipeline transformation steps decreases
general throughput. So I assume its fusion issue?
{quote}
Yes, I think so too (as I guessed in my first comment). On the other hand, the
problem with reshuffle could be serialisation/deserialisation stage (I guess
CPU 100% in your case is because of this), which also can take significant
amount of time. So, in general, use or not use a fusion/reshuffle - it should
be a question of finding a trade-off.
> Messages are not distributed per machines when consuming from Kafka topic
> with 1 partition
> ------------------------------------------------------------------------------------------
>
> Key: BEAM-8121
> URL: https://issues.apache.org/jira/browse/BEAM-8121
> Project: Beam
> Issue Type: Bug
> Components: io-java-kafka
> Affects Versions: 2.14.0
> Reporter: TJ
> Priority: Major
> Attachments: datalake-dataflow-cleaned.zip
>
>
> Messages are consumed from Kafka using KafkaIO. Each kafka topic contains
> only 1 partition. (That means that messages can be consumed only by one
> Consumer per 1 consumer group)
> When backlog of topic grows and system scales from 1 to X machines, all the
> messages seems to be executed on the same machine on which they are read.
> Due to that message throughput doesn't increase comparing X machines to 1
> machine. If one machine was reading 2K messages per s, X machines will be
> reading the same amount.
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