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https://issues.apache.org/jira/browse/HIVE-20660?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16648297#comment-16648297
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Ashutosh Chauhan commented on HIVE-20660:
-----------------------------------------
+1
> Group by statistics estimation could be improved by bounding the total number
> of rows to source table
> -----------------------------------------------------------------------------------------------------
>
> Key: HIVE-20660
> URL: https://issues.apache.org/jira/browse/HIVE-20660
> Project: Hive
> Issue Type: Improvement
> Components: Statistics
> Affects Versions: 4.0.0
> Reporter: Vineet Garg
> Assignee: Vineet Garg
> Priority: Major
> Attachments: HIVE-20660.1.patch, HIVE-20660.2.patch,
> HIVE-20660.3.patch, HIVE-20660.4.patch, HIVE-20660.5.patch, HIVE-20660.6.patch
>
>
> Currently the stats for group by is estimated by taking product of NDVs of
> all the keys and bounding it by the number of rows of its input. This bound
> could be improved by using the source table instead of immediate input, the
> insight in this case is that cardinality/ndvs of a table can not go beyond
> the original (outer joins will only add NULLs thereby increasing the
> cardinality by 1).
> Note that the assumption here is that group by keys all belong to the same
> source table/input.
> This will improve the estimation in situations where group by is executed
> after joins wherein Hive could end up estimating the number of rows.
> *Reproducer*
> {code:sql}
> set hive.stats.fetch.column.stats=true;
> create table t1(i int, j int);
> alter table t1 update statistics set('numRows'='10000',
> 'rawDataSize'='18000');
> alter table t1 update statistics for column i
> set('numDVs'='2500','numNulls'='50','highValue'='1000','lowValue'='0');
> alter table t1 update statistics for column j
> set('numDVs'='500','numNulls'='30','highValue'='100','lowValue'='50');
> create table t2(i2 int, j2 int);
> alter table t2 update statistics set('numRows'='100000000',
> 'rawDataSize'='10000');
> alter table t2 update statistics for column i2
> set('numDVs'='10000000','numNulls'='0','highValue'='8000','lowValue'='0');
> alter table t2 update statistics for column j2
> set('numDVs'='10','numNulls'='0','highValue'='800','lowValue'='-1');
> explain select count (1) from t1,t2
> where t1.j=t2.i2
> group by t1.i, t1.j;
> {code}
> {code:sql}
> Reducer 2
> Reduce Operator Tree:
> Merge Join Operator
> condition map:
> Inner Join 0 to 1
> keys:
> 0 _col1 (type: int)
> 1 _col0 (type: int)
> outputColumnNames: _col0, _col1
> Statistics: Num rows: 99700 Data size: 797288 Basic stats:
> COMPLETE Column stats: COMPLETE
> Group By Operator
> aggregations: count()
> keys: _col0 (type: int), _col1 (type: int)
> mode: hash
> outputColumnNames: _col0, _col1, _col2
> Statistics: Num rows: 49850 Data size: 797448 Basic stats:
> COMPLETE Column stats: COMPLETE <==========
> Reduce Output Operator
> key expressions: _col0 (type: int), _col1 (type: int)
> sort order: ++
> Map-reduce partition columns: _col0 (type: int), _col1
> (type: int)
> Statistics: Num rows: 49850 Data size: 797448 Basic
> stats: COMPLETE Column stats: COMPLETE
> value expressions: _col2 (type: bigint)
> .....................
> {code}
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