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https://issues.apache.org/jira/browse/CALCITE-3594?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16994167#comment-16994167
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Rui Wang commented on CALCITE-3594:
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[~danny0405] Thanks for suggestion. Let me check current implementation to see
how to adopt your suggestions. Will report back once I come up with something.
> Support hot Groupby keys hint
> -----------------------------
>
> Key: CALCITE-3594
> URL: https://issues.apache.org/jira/browse/CALCITE-3594
> Project: Calcite
> Issue Type: Sub-task
> Reporter: Rui Wang
> Assignee: Rui Wang
> Priority: Major
>
> It will be useful for Apache Beam if we support the following SqlHint:
> SELECT * FROM t
> GROUP BY t.key_column /* + hot_key(key1=fanout_factor, ...) */)
> The hot key strategy works on aggregation and it provides a list of hot keys
> with fanout factor for a column. The fanout factor says how many partition
> should be created for that specific key, such that we can have a per
> partition aggregate and then have a final aggregate. One example to explain
> it:
> SELECT * FROM t
> GROUP BY t.key_column /* + hot_key("value1"=2) */)
> // for the key_column, there is a "value1" which appear so many times (so
> it's hot), please consider split it into two partition and process separately.
> Such problem is common for big data processing, where hot key creates slowest
> machine which either slow down the whole pipeline or make retries. In such
> case, one common resolution is to split data to multiple partition and
> aggregate per partition, and then have a final combine.
> Usually execution engine won't know what is the hot key(s). SqlHint provides
> a good way to tell the engine which key is useful to deal with it.
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