Vineet Garg created HIVE-20660:
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Summary: 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
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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