[jira] [Resolved] (CALCITE-3830) The ‘approximate’ field should be considered when computing the digest of AggregateCall

["Danny Chen (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/CALCITE-3830?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Danny Chen resolved CALCITE-3830.
---------------------------------
    Resolution: Fixed

Fixed in [cdd141d|https://github.com/apache/calcite/commit/cdd141df56e3c72315992c9a1477ff6d179b11d2], thanks for your PR [~icshuo] !

> The ‘approximate’ field should be considered when computing the digest of AggregateCall
> ---------------------------------------------------------------------------------------
>
>                 Key: CALCITE-3830
>                 URL: https://issues.apache.org/jira/browse/CALCITE-3830
>             Project: Calcite
>          Issue Type: Bug
>          Components: core
>    Affects Versions: 1.21.0
>            Reporter: Shuo Cheng
>            Assignee: Shuo Cheng
>            Priority: Major
>              Labels: pull-request-available
>             Fix For: 1.22.0
>
>          Time Spent: 1h 20m
>  Remaining Estimate: 0h
>
> In planner optimization, the digest  of Aggregate node contains digest of its AggregateCall, i.e. AggregateCall.toString, but currently the 'approximate' filed of AggregateCall is not considered in toString() method, which may leads to the situation two different relNodes are considered as identical in planner optimizing phase. 
> Here is an example:
> {code:java}
> // SQL
> select * from (
>   select a, count(distinct b) from T group by a
>   union all
>   select a, approx_count_distinct(b) from T group by a
> )
> // after applying a rule, the plan is
> LogicalSink(name=[_DataStreamTable_1], fields=[a, EXPR$1], __id__=[96])
> +- LogicalProject(a=[$0], EXPR$1=[$1], __id__=[94])
>    +- LogicalUnion(all=[true], __id__=[92])
>       :- LogicalAggregate(group=[{0}], EXPR$1=[COUNT(DISTINCT $1)], __id__=[89])
>       :  +- LogicalTableScan(table=[[default, _DataStreamTable_2]], __id__=[100])
>       +- LogicalAggregate(group=[{0}], EXPR$1=[COUNT(DISTINCT $1)], __id__=[89])
>          +- LogicalTableScan(table=[[default, _DataStreamTable_2]], __id__=[100])
> {code}
> As showing in the example, after optimizing, these two Aggregates are considered as identical (both with 89 as relNode ID).



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