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https://issues.apache.org/jira/browse/HIVE-7990?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Prasanth J updated HIVE-7990:
-----------------------------
Labels: (was: performance)
> With fetch column stats disabled number of elements in grouping set is not
> taken into account
> ---------------------------------------------------------------------------------------------
>
> Key: HIVE-7990
> URL: https://issues.apache.org/jira/browse/HIVE-7990
> Project: Hive
> Issue Type: Sub-task
> Components: Statistics
> Affects Versions: 0.13.1
> Reporter: Mostafa Mokhtar
> Assignee: Prasanth J
>
> 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.
>
> {code}
> // 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);
> }
>
> {code}
> Query
> {code}
> 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
> {code}
> Plan generated , note the data size for Map 1
> {code}
> 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
> {code}
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