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https://issues.apache.org/jira/browse/FLINK-10348?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16617054#comment-16617054
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Jiayi Liao commented on FLINK-10348:
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[~elevy]
1. From my perspective, we use fetch request to request data, the
parameters(fetch size/max wait..) are set by the our flink programs, so it's
our job to decide how to request these data.
2. About the multiple input operators, it won't help if the partitions are more
than the parallelism, because the operator consumes the earliest data and
oldest data at the same time and it's hard to generate the watermark.
> Solve data skew when consuming data from kafka
> ----------------------------------------------
>
> Key: FLINK-10348
> URL: https://issues.apache.org/jira/browse/FLINK-10348
> Project: Flink
> Issue Type: New Feature
> Components: Kafka Connector
> Affects Versions: 1.6.0
> Reporter: Jiayi Liao
> Assignee: Jiayi Liao
> Priority: Major
>
> By using KafkaConsumer, our strategy is to send fetch request to brokers with
> a fixed fetch size. Assume x topic has n partition and there exists data skew
> between partitions, now we need to consume data from x topic with earliest
> offset, and we can get max fetch size data in every fetch request. The
> problem is that when an task consumes data from both "big" partitions and
> "small" partitions, the data in "big" partitions may be late elements because
> "small" partitions are consumed faster.
> *Solution: *
> I think we can leverage two parameters to control this.
> 1. data.skew.check // whether to check data skew
> 2. data.skew.check.interval // the interval between checks
> Every data.skew.check.interval, we will check the latest offset of every
> specific partition, and calculate (latest offset - current offset), then get
> partitions which need to slow down and redefine their fetch size.
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