Details
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Sub-task
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Status: Closed
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Major
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Resolution: Fixed
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0.13.1
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None
Description
For queries with rollup and cube the number of rows calculation in GroupByStatsRule should be multiplied by number of elements in grouping set.
A side effect of this defect is that reducers will under estimate data size and end up with small number of tasks which negatively affects query runtime.
// since we do not know if hash-aggregation will be enabled or disabled // at runtime we will assume that map-side group by does not do any // reduction.hence no group by rule will be applied // map-side grouping set present. if grouping set is present then // multiply the number of rows by number of elements in grouping set if (gop.getConf().isGroupingSetsPresent()) { int multiplier = gop.getConf().getListGroupingSets().size(); // take into account the map-side parallelism as well, default is 1 multiplier *= mapSideParallelism; newNumRows = multiplier * stats.getNumRows(); long dataSize = multiplier * stats.getDataSize(); stats.setNumRows(newNumRows); stats.setDataSize(dataSize); for (ColStatistics cs : colStats) { if (cs != null) { long oldNumNulls = cs.getNumNulls(); long newNumNulls = multiplier * oldNumNulls; cs.setNumNulls(newNumNulls); } } } else { // map side no grouping set newNumRows = stats.getNumRows() * mapSideParallelism; updateStats(stats, newNumRows, true); }
Query
select *
from (select i_category
,i_class
,i_brand
,i_product_name
,d_year
,d_qoy
,d_moy
,s_store_id
,sumsales
,rank() over (partition by i_category order by sumsales desc) rk
from (select i_category
,i_class
,i_brand
,i_product_name
,d_year
,d_qoy
,d_moy
,s_store_id
,sum(coalesce(ss_sales_price*ss_quantity,0)) sumsales
from store_sales
,date_dim
,store
,item
where store_sales.ss_sold_date_sk=date_dim.d_date_sk
and store_sales.ss_item_sk=item.i_item_sk
and store_sales.ss_store_sk = store.s_store_sk
and d_month_seq between 1193 and 1193+11
and ss_sold_date between '1999-06-01' and '2000-05-31'
group by i_category, i_class, i_brand, i_product_name, d_year, d_qoy, d_moy,s_store_id with rollup)dw1) dw2
where rk <= 100
order by i_category
,i_class
,i_brand
,i_product_name
,d_year
,d_qoy
,d_moy
,s_store_id
,sumsales
,rk
limit 100
Plan generated , note the data size for Map 1
STAGE DEPENDENCIES:
Stage-1 is a root stage
Stage-0 depends on stages: Stage-1
STAGE PLANS:
Stage: Stage-1
Tez
Edges:
Map 1 <- Map 5 (BROADCAST_EDGE), Map 6 (BROADCAST_EDGE), Map 7 (BROADCAST_EDGE)
Reducer 2 <- Map 1 (SIMPLE_EDGE)
Reducer 3 <- Reducer 2 (SIMPLE_EDGE)
Reducer 4 <- Reducer 3 (SIMPLE_EDGE)
DagName: mmokhtar_20140903154848_7cf1519f-e95c-47ab-9f10-6d2130cd5734:1
Vertices:
Map 1
Map Operator Tree:
TableScan
alias: store_sales
filterExpr: (((ss_sold_date_sk is not null and ss_store_sk is not null) and ss_item_sk is not null) and ss_sold_date BETWEEN '1999-06-01' AND '2000-05-31') (type: boolean)
Statistics: Num rows: 110339135 Data size: 4817453454 Basic stats: COMPLETE Column stats: NONE
Filter Operator
predicate: ((ss_sold_date_sk is not null and ss_store_sk is not null) and ss_item_sk is not null) (type: boolean)
Statistics: Num rows: 13792392 Data size: 602181687 Basic stats: COMPLETE Column stats: NONE
Map Join Operator
condition map:
Inner Join 0 to 1
condition expressions:
0 {ss_sold_date_sk} {ss_item_sk} {ss_store_sk} {ss_quantity} {ss_sales_price} {ss_sold_date}
1 {d_date_sk} {d_month_seq} {d_year} {d_moy} {d_qoy}
keys:
0 ss_sold_date_sk (type: int)
1 d_date_sk (type: int)
outputColumnNames: _col0, _col2, _col7, _col10, _col13, _col23, _col27, _col30, _col33, _col35, _col37
input vertices:
1 Map 6
Statistics: Num rows: 15171632 Data size: 662399872 Basic stats: COMPLETE Column stats: NONE
Map Join Operator
condition map:
Inner Join 0 to 1
condition expressions:
0 {_col0} {_col2} {_col7} {_col10} {_col13} {_col23} {_col27} {_col30} {_col33} {_col35} {_col37}
1 {s_store_sk} {s_store_id}
keys:
0 _col7 (type: int)
1 s_store_sk (type: int)
outputColumnNames: _col0, _col2, _col7, _col10, _col13, _col23, _col27, _col30, _col33, _col35, _col37, _col58, _col59
input vertices:
1 Map 5
Statistics: Num rows: 16688796 Data size: 728639872 Basic stats: COMPLETE Column stats: NONE
Map Join Operator
condition map:
Inner Join 0 to 1
condition expressions:
0 {_col0} {_col2} {_col7} {_col10} {_col13} {_col23} {_col27} {_col30} {_col33} {_col35} {_col37} {_col58} {_col59}
1 {i_item_sk} {i_brand} {i_class} {i_category} {i_product_name}
keys:
0 _col2 (type: int)
1 i_item_sk (type: int)
outputColumnNames: _col0, _col2, _col7, _col10, _col13, _col23, _col27, _col30, _col33, _col35, _col37, _col58, _col59, _col90, _col98, _col100, _col102, _col111
input vertices:
1 Map 7
Statistics: Num rows: 18357676 Data size: 801503872 Basic stats: COMPLETE Column stats: NONE
Filter Operator
predicate: (((((_col0 = _col27) and (_col2 = _col90)) and (_col7 = _col58)) and _col30 BETWEEN 1193 AND 1204) and _col23 BETWEEN '1999-06-01' AND '2000-05-31') (type: boolean)
Statistics: Num rows: 573677 Data size: 25046979 Basic stats: COMPLETE Column stats: NONE
Select Operator
expressions: _col102 (type: string), _col100 (type: string), _col98 (type: string), _col111 (type: string), _col33 (type: int), _col37 (type: int), _col35 (type: int), _col59 (type: string), _col13 (type: float), _col10 (type: int)
outputColumnNames: _col102, _col100, _col98, _col111, _col33, _col37, _col35, _col59, _col13, _col10
Statistics: Num rows: 573677 Data size: 25046979 Basic stats: COMPLETE Column stats: NONE
Group By Operator
aggregations: sum(COALESCE((_col13 * _col10),0))
keys: _col102 (type: string), _col100 (type: string), _col98 (type: string), _col111 (type: string), _col33 (type: int), _col37 (type: int), _col35 (type: int), _col59 (type: string), '0' (type: string)
mode: hash
outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9
Statistics: Num rows: 573677 Data size: 25046979 Basic stats: COMPLETE Column stats: NONE
Reduce Output Operator
key expressions: _col0 (type: string), _col1 (type: string), _col2 (type: string), _col3 (type: string), _col4 (type: int), _col5 (type: int), _col6 (type: int), _col7 (type: string), _col8 (type: string)
sort order: +++++++++
Map-reduce partition columns: _col0 (type: string), _col1 (type: string), _col2 (type: string), _col3 (type: string), _col4 (type: int), _col5 (type: int), _col6 (type: int), _col7 (type: string), _col8 (type: string)
Statistics: Num rows: 573677 Data size: 25046979 Basic stats: COMPLETE Column stats: NONE
value expressions: _col9 (type: double)
Map 5
Map Operator Tree:
TableScan
alias: store
filterExpr: s_store_sk is not null (type: boolean)
Statistics: Num rows: 212 Data size: 405680 Basic stats: COMPLETE Column stats: COMPLETE
Filter Operator
predicate: s_store_sk is not null (type: boolean)
Statistics: Num rows: 212 Data size: 22048 Basic stats: COMPLETE Column stats: COMPLETE
Reduce Output Operator
key expressions: s_store_sk (type: int)
sort order: +
Map-reduce partition columns: s_store_sk (type: int)
Statistics: Num rows: 212 Data size: 22048 Basic stats: COMPLETE Column stats: COMPLETE
value expressions: s_store_id (type: string)
Execution mode: vectorized
Map 6
Map Operator Tree:
TableScan
alias: date_dim
filterExpr: (d_date_sk is not null and d_month_seq BETWEEN 1193 AND 1204) (type: boolean)
Statistics: Num rows: 73049 Data size: 81741831 Basic stats: COMPLETE Column stats: COMPLETE
Filter Operator
predicate: (d_date_sk is not null and d_month_seq BETWEEN 1193 AND 1204) (type: boolean)
Statistics: Num rows: 36524 Data size: 730480 Basic stats: COMPLETE Column stats: COMPLETE
Reduce Output Operator
key expressions: d_date_sk (type: int)
sort order: +
Map-reduce partition columns: d_date_sk (type: int)
Statistics: Num rows: 36524 Data size: 730480 Basic stats: COMPLETE Column stats: COMPLETE
value expressions: d_month_seq (type: int), d_year (type: int), d_moy (type: int), d_qoy (type: int)
Execution mode: vectorized
Map 7
Map Operator Tree:
TableScan
alias: item
filterExpr: i_item_sk is not null (type: boolean)
Statistics: Num rows: 48000 Data size: 68732712 Basic stats: COMPLETE Column stats: COMPLETE
Filter Operator
predicate: i_item_sk is not null (type: boolean)
Statistics: Num rows: 48000 Data size: 18672000 Basic stats: COMPLETE Column stats: COMPLETE
Reduce Output Operator
key expressions: i_item_sk (type: int)
sort order: +
Map-reduce partition columns: i_item_sk (type: int)
Statistics: Num rows: 48000 Data size: 18672000 Basic stats: COMPLETE Column stats: COMPLETE
value expressions: i_brand (type: string), i_class (type: string), i_category (type: string), i_product_name (type: string)
Execution mode: vectorized
Reducer 2
Reduce Operator Tree:
Group By Operator
aggregations: sum(VALUE._col0)
keys: KEY._col0 (type: string), KEY._col1 (type: string), KEY._col2 (type: string), KEY._col3 (type: string), KEY._col4 (type: int), KEY._col5 (type: int), KEY._col6 (type: int), KEY._col7 (type: string), KEY._col8 (type: string)
mode: mergepartial
outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9
Statistics: Num rows: 286838 Data size: 12523467 Basic stats: COMPLETE Column stats: NONE
Select Operator
expressions: _col0 (type: string), _col1 (type: string), _col2 (type: string), _col3 (type: string), _col4 (type: int), _col5 (type: int), _col6 (type: int), _col7 (type: string), _col9 (type: double)
outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8
Statistics: Num rows: 286838 Data size: 12523467 Basic stats: COMPLETE Column stats: NONE
Reduce Output Operator
key expressions: _col0 (type: string), _col8 (type: double)
sort order: +-
Map-reduce partition columns: _col0 (type: string)
Statistics: Num rows: 286838 Data size: 12523467 Basic stats: COMPLETE Column stats: NONE
TopN Hash Memory Usage: 0.04
value expressions: _col0 (type: string), _col1 (type: string), _col2 (type: string), _col3 (type: string), _col4 (type: int), _col5 (type: int), _col6 (type: int), _col7 (type: string), _col8 (type: double)
Reducer 3
Reduce Operator Tree:
Extract
Statistics: Num rows: 286838 Data size: 12523467 Basic stats: COMPLETE Column stats: NONE
PTF Operator
Statistics: Num rows: 286838 Data size: 12523467 Basic stats: COMPLETE Column stats: NONE
Filter Operator
predicate: (_wcol0 <= 100) (type: boolean)
Statistics: Num rows: 95612 Data size: 4174459 Basic stats: COMPLETE Column stats: NONE
Select Operator
expressions: _col0 (type: string), _col1 (type: string), _col2 (type: string), _col3 (type: string), _col4 (type: int), _col5 (type: int), _col6 (type: int), _col7 (type: string), _col8 (type: double), _wcol0 (type: int)
outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9
Statistics: Num rows: 95612 Data size: 4174459 Basic stats: COMPLETE Column stats: NONE
Reduce Output Operator
key expressions: _col0 (type: string), _col1 (type: string), _col2 (type: string), _col3 (type: string), _col4 (type: int), _col5 (type: int), _col6 (type: int), _col7 (type: string), _col8 (type: double), _col9 (type: int)
sort order: ++++++++++
Statistics: Num rows: 95612 Data size: 4174459 Basic stats: COMPLETE Column stats: NONE
TopN Hash Memory Usage: 0.04
Reducer 4
Reduce Operator Tree:
Select Operator
expressions: KEY.reducesinkkey0 (type: string), KEY.reducesinkkey1 (type: string), KEY.reducesinkkey2 (type: string), KEY.reducesinkkey3 (type: string), KEY.reducesinkkey4 (type: int), KEY.reducesinkkey5 (type: int), KEY.reducesinkkey6 (type: int), KEY.reducesinkkey7 (type: string), KEY.reducesinkkey8 (type: double), KEY.reducesinkkey9 (type: int)
outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9
Statistics: Num rows: 95612 Data size: 4174459 Basic stats: COMPLETE Column stats: NONE
Limit
Number of rows: 100
Statistics: Num rows: 100 Data size: 4300 Basic stats: COMPLETE Column stats: NONE
File Output Operator
compressed: false
Statistics: Num rows: 100 Data size: 4300 Basic stats: COMPLETE Column stats: NONE
table:
input format: org.apache.hadoop.mapred.TextInputFormat
output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat
serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe
Execution mode: vectorized
Stage: Stage-0
Fetch Operator
limit: 100
Processor Tree:
ListSink
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