Details
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Sub-task
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Status: Closed
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Minor
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Resolution: Fixed
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0.13.1
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None
Description
This loop results in adding the parent twice incase of a 3 way join of store_sales x date_dim x store
for (int pos = 0; pos < parents.size(); pos++) { ReduceSinkOperator parent = (ReduceSinkOperator) jop.getParentOperators().get(pos); Statistics parentStats = parent.getStatistics(); List<ExprNodeDesc> keyExprs = parent.getConf().getKeyCols(); // Parent RS may have column statistics from multiple parents. // Populate table alias to row count map, this will be used later to // scale down/up column statistics based on new row count // NOTE: JOIN with UNION as parent of RS will not have table alias // propagated properly. UNION operator does not propagate the table // alias of subqueries properly to expression nodes. Hence union20.q // will have wrong number of rows. Set<String> tableAliases = StatsUtils.getAllTableAlias(parent.getColumnExprMap()); for (String tabAlias : tableAliases) { rowCountParents.put(tabAlias, parentStats.getNumRows()); }
In the first join we have rowCountParents with
{store_sales=120464862, date_dim=36524} which is correct.
For the second join result rowCountParents ends up with
where it should be
{store=212, store_sales=120464862, date_dim=36524}.
The result of this is that computeNewRowCount ends up multiplying row count of store_sales x store_sales which makes the number of rows really high and eventually over flow.
Plan snippet :
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: COMPLETE
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: 107740258 Data size: 2124353556 Basic stats: COMPLETE Column stats: COMPLETE
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: 120464862 Data size: 26984129088 Basic stats: COMPLETE Column stats: COMPLETE
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: 17886616227069518 Data size: 5866810122478801920 Basic stats: COMPLETE Column stats: COMPLETE
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: -9223372036854775808 Data size: 0 Basic stats: NONE Column stats: COMPLETE
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: -9223372036854775808 Data size: 0 Basic stats: NONE Column stats: COMPLETE
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: -9223372036854775808 Data size: 0 Basic stats: NONE Column stats: COMPLETE
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: -9223372036854775808 Data size: 0 Basic stats: NONE Column stats: COMPLETE
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: -9223372036854775808 Data size: 0 Basic stats: NONE Column stats: COMPLETE
value expressions: _col9 (type: double)