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https://issues.apache.org/jira/browse/CALCITE-3594?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16996655#comment-16996655
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Rui Wang commented on CALCITE-3594:
-----------------------------------

I have drafted a PR to show the changes that in order to support this groupby 
key hints. 

I found that there are one tricky place to change when working on the draft. So 
basically this time hints are belonged to group by keys, then the clauses. So 
the required data structure is List<Pair<key, List<hint>>> for Aggregate. The 
list of pair means there are a list keys. Each pair is for one key, and the 
each key could have a list of hints.

I think my current design is less mature so I would be happy to refine it.

> 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
>              Labels: pull-request-available
>          Time Spent: 10m
>  Remaining Estimate: 0h
>
> 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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