Mostafa Mokhtar created HIVE-7992:
-------------------------------------
Summary: StatsRulesProcFactory should gracefully handle overflows
Key: HIVE-7992
URL: https://issues.apache.org/jira/browse/HIVE-7992
Project: Hive
Issue Type: Bug
Components: Statistics
Affects Versions: 0.13.1
Reporter: Mostafa Mokhtar
Assignee: Prasanth J
Fix For: 0.14.0
This loop results in adding the parent twice incase of a 3 way join of
store_sales x date_dim x store
{code}
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());
}
{code}
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 {store=212,
store_sales=120464862, date_dim=120464862} 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 :
{code}
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)
{code}
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