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Posted to dev@hive.apache.org by "Damien Carol (JIRA)" <ji...@apache.org> on 2015/02/18 09:56:12 UTC
[jira] [Updated] (HIVE-9712) Row count and data size are set to
LONG.MAX when source table has 0 rows
[ https://issues.apache.org/jira/browse/HIVE-9712?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Damien Carol updated HIVE-9712:
-------------------------------
Summary: Row count and data size are set to LONG.MAX when source table has 0 rows (was: Hive : Row count and data size are set to LONG.MAX when source table has 0 rows)
> Row count and data size are set to LONG.MAX when source table has 0 rows
> ------------------------------------------------------------------------
>
> Key: HIVE-9712
> URL: https://issues.apache.org/jira/browse/HIVE-9712
> Project: Hive
> Issue Type: Bug
> Components: Physical Optimizer
> Affects Versions: 0.14.0
> Reporter: Mostafa Mokhtar
> Assignee: Prasanth Jayachandran
>
> TPC-DS Q66 generates and in-efficient plan because cardinality estimate of dimension table gets set to 9223372036854775807.
> {code}
> Map 10
> Map Operator Tree:
> TableScan
> alias: ship_mode
> filterExpr: ((sm_carrier) IN ('DIAMOND', 'AIRBORNE') and sm_ship_mode_sk is not null) (type: boolean)
> Statistics: Num rows: 0 Data size: 47 Basic stats: PARTIAL Column stats: COMPLETE
> Filter Operator
> predicate: ((sm_carrier) IN ('DIAMOND', 'AIRBORNE') and sm_ship_mode_sk is not null) (type: boolean)
> Statistics: Num rows: 9223372036854775807 Data size: 9223372036854775807 Basic stats: COMPLETE Column stats: COMPLETE
> Select Operator
> expressions: sm_ship_mode_sk (type: int)
> outputColumnNames: _col0
> Statistics: Num rows: 9223372036854775807 Data size: 9223372036854775807 Basic stats: COMPLETE Column stats: COMPLETE
> Reduce Output Operator
> key expressions: _col0 (type: int)
> sort order: +
> Map-reduce partition columns: _col0 (type: int)
> Statistics: Num rows: 9223372036854775807 Data size: 9223372036854775807 Basic stats: COMPLETE Column stats: COMPLETE
> Execution mode: vectorized
> {code}
> Full plan
> {code}
> explain
> select
> w_warehouse_name
> ,w_warehouse_sq_ft
> ,w_city
> ,w_county
> ,w_state
> ,w_country
> ,ship_carriers
> ,year
> ,sum(jan_sales) as jan_sales
> ,sum(feb_sales) as feb_sales
> ,sum(mar_sales) as mar_sales
> ,sum(apr_sales) as apr_sales
> ,sum(may_sales) as may_sales
> ,sum(jun_sales) as jun_sales
> ,sum(jul_sales) as jul_sales
> ,sum(aug_sales) as aug_sales
> ,sum(sep_sales) as sep_sales
> ,sum(oct_sales) as oct_sales
> ,sum(nov_sales) as nov_sales
> ,sum(dec_sales) as dec_sales
> ,sum(jan_sales/w_warehouse_sq_ft) as jan_sales_per_sq_foot
> ,sum(feb_sales/w_warehouse_sq_ft) as feb_sales_per_sq_foot
> ,sum(mar_sales/w_warehouse_sq_ft) as mar_sales_per_sq_foot
> ,sum(apr_sales/w_warehouse_sq_ft) as apr_sales_per_sq_foot
> ,sum(may_sales/w_warehouse_sq_ft) as may_sales_per_sq_foot
> ,sum(jun_sales/w_warehouse_sq_ft) as jun_sales_per_sq_foot
> ,sum(jul_sales/w_warehouse_sq_ft) as jul_sales_per_sq_foot
> ,sum(aug_sales/w_warehouse_sq_ft) as aug_sales_per_sq_foot
> ,sum(sep_sales/w_warehouse_sq_ft) as sep_sales_per_sq_foot
> ,sum(oct_sales/w_warehouse_sq_ft) as oct_sales_per_sq_foot
> ,sum(nov_sales/w_warehouse_sq_ft) as nov_sales_per_sq_foot
> ,sum(dec_sales/w_warehouse_sq_ft) as dec_sales_per_sq_foot
> ,sum(jan_net) as jan_net
> ,sum(feb_net) as feb_net
> ,sum(mar_net) as mar_net
> ,sum(apr_net) as apr_net
> ,sum(may_net) as may_net
> ,sum(jun_net) as jun_net
> ,sum(jul_net) as jul_net
> ,sum(aug_net) as aug_net
> ,sum(sep_net) as sep_net
> ,sum(oct_net) as oct_net
> ,sum(nov_net) as nov_net
> ,sum(dec_net) as dec_net
> from (
> select
> w_warehouse_name
> ,w_warehouse_sq_ft
> ,w_city
> ,w_county
> ,w_state
> ,w_country
> ,concat('DIAMOND', ',', 'AIRBORNE') as ship_carriers
> ,d_year as year
> ,sum(case when d_moy = 1
> then ws_sales_price* ws_quantity else 0 end) as jan_sales
> ,sum(case when d_moy = 2
> then ws_sales_price* ws_quantity else 0 end) as feb_sales
> ,sum(case when d_moy = 3
> then ws_sales_price* ws_quantity else 0 end) as mar_sales
> ,sum(case when d_moy = 4
> then ws_sales_price* ws_quantity else 0 end) as apr_sales
> ,sum(case when d_moy = 5
> then ws_sales_price* ws_quantity else 0 end) as may_sales
> ,sum(case when d_moy = 6
> then ws_sales_price* ws_quantity else 0 end) as jun_sales
> ,sum(case when d_moy = 7
> then ws_sales_price* ws_quantity else 0 end) as jul_sales
> ,sum(case when d_moy = 8
> then ws_sales_price* ws_quantity else 0 end) as aug_sales
> ,sum(case when d_moy = 9
> then ws_sales_price* ws_quantity else 0 end) as sep_sales
> ,sum(case when d_moy = 10
> then ws_sales_price* ws_quantity else 0 end) as oct_sales
> ,sum(case when d_moy = 11
> then ws_sales_price* ws_quantity else 0 end) as nov_sales
> ,sum(case when d_moy = 12
> then ws_sales_price* ws_quantity else 0 end) as dec_sales
> ,sum(case when d_moy = 1
> then ws_net_paid_inc_tax * ws_quantity else 0 end) as jan_net
> ,sum(case when d_moy = 2
> then ws_net_paid_inc_tax * ws_quantity else 0 end) as feb_net
> ,sum(case when d_moy = 3
> then ws_net_paid_inc_tax * ws_quantity else 0 end) as mar_net
> ,sum(case when d_moy = 4
> then ws_net_paid_inc_tax * ws_quantity else 0 end) as apr_net
> ,sum(case when d_moy = 5
> then ws_net_paid_inc_tax * ws_quantity else 0 end) as may_net
> ,sum(case when d_moy = 6
> then ws_net_paid_inc_tax * ws_quantity else 0 end) as jun_net
> ,sum(case when d_moy = 7
> then ws_net_paid_inc_tax * ws_quantity else 0 end) as jul_net
> ,sum(case when d_moy = 8
> then ws_net_paid_inc_tax * ws_quantity else 0 end) as aug_net
> ,sum(case when d_moy = 9
> then ws_net_paid_inc_tax * ws_quantity else 0 end) as sep_net
> ,sum(case when d_moy = 10
> then ws_net_paid_inc_tax * ws_quantity else 0 end) as oct_net
> ,sum(case when d_moy = 11
> then ws_net_paid_inc_tax * ws_quantity else 0 end) as nov_net
> ,sum(case when d_moy = 12
> then ws_net_paid_inc_tax * ws_quantity else 0 end) as dec_net
> from
> web_sales
> ,warehouse
> ,date_dim
> ,time_dim
> ,ship_mode
> where
> web_sales.ws_warehouse_sk = warehouse.w_warehouse_sk
> and web_sales.ws_sold_date_sk = date_dim.d_date_sk
> and web_sales.ws_sold_time_sk = time_dim.t_time_sk
> and web_sales.ws_ship_mode_sk = ship_mode.sm_ship_mode_sk
> and d_year = 2002
> and t_time between 49530 and 49530+28800
> and sm_carrier in ('DIAMOND','AIRBORNE')
> group by
> w_warehouse_name
> ,w_warehouse_sq_ft
> ,w_city
> ,w_county
> ,w_state
> ,w_country
> ,d_year
> union all
> select
> w_warehouse_name
> ,w_warehouse_sq_ft
> ,w_city
> ,w_county
> ,w_state
> ,w_country
> ,concat('DIAMOND', ',', 'AIRBORNE') as ship_carriers
> ,d_year as year
> ,sum(case when d_moy = 1
> then cs_ext_sales_price* cs_quantity else 0 end) as jan_sales
> ,sum(case when d_moy = 2
> then cs_ext_sales_price* cs_quantity else 0 end) as feb_sales
> ,sum(case when d_moy = 3
> then cs_ext_sales_price* cs_quantity else 0 end) as mar_sales
> ,sum(case when d_moy = 4
> then cs_ext_sales_price* cs_quantity else 0 end) as apr_sales
> ,sum(case when d_moy = 5
> then cs_ext_sales_price* cs_quantity else 0 end) as may_sales
> ,sum(case when d_moy = 6
> then cs_ext_sales_price* cs_quantity else 0 end) as jun_sales
> ,sum(case when d_moy = 7
> then cs_ext_sales_price* cs_quantity else 0 end) as jul_sales
> ,sum(case when d_moy = 8
> then cs_ext_sales_price* cs_quantity else 0 end) as aug_sales
> ,sum(case when d_moy = 9
> then cs_ext_sales_price* cs_quantity else 0 end) as sep_sales
> ,sum(case when d_moy = 10
> then cs_ext_sales_price* cs_quantity else 0 end) as oct_sales
> ,sum(case when d_moy = 11
> then cs_ext_sales_price* cs_quantity else 0 end) as nov_sales
> ,sum(case when d_moy = 12
> then cs_ext_sales_price* cs_quantity else 0 end) as dec_sales
> ,sum(case when d_moy = 1
> then cs_net_paid_inc_ship_tax * cs_quantity else 0 end) as jan_net
> ,sum(case when d_moy = 2
> then cs_net_paid_inc_ship_tax * cs_quantity else 0 end) as feb_net
> ,sum(case when d_moy = 3
> then cs_net_paid_inc_ship_tax * cs_quantity else 0 end) as mar_net
> ,sum(case when d_moy = 4
> then cs_net_paid_inc_ship_tax * cs_quantity else 0 end) as apr_net
> ,sum(case when d_moy = 5
> then cs_net_paid_inc_ship_tax * cs_quantity else 0 end) as may_net
> ,sum(case when d_moy = 6
> then cs_net_paid_inc_ship_tax * cs_quantity else 0 end) as jun_net
> ,sum(case when d_moy = 7
> then cs_net_paid_inc_ship_tax * cs_quantity else 0 end) as jul_net
> ,sum(case when d_moy = 8
> then cs_net_paid_inc_ship_tax * cs_quantity else 0 end) as aug_net
> ,sum(case when d_moy = 9
> then cs_net_paid_inc_ship_tax * cs_quantity else 0 end) as sep_net
> ,sum(case when d_moy = 10
> then cs_net_paid_inc_ship_tax * cs_quantity else 0 end) as oct_net
> ,sum(case when d_moy = 11
> then cs_net_paid_inc_ship_tax * cs_quantity else 0 end) as nov_net
> ,sum(case when d_moy = 12
> then cs_net_paid_inc_ship_tax * cs_quantity else 0 end) as dec_net
> from
> catalog_sales
> ,warehouse
> ,date_dim
> ,time_dim
> ,ship_mode
> where
> catalog_sales.cs_warehouse_sk = warehouse.w_warehouse_sk
> and catalog_sales.cs_sold_date_sk = date_dim.d_date_sk
> and catalog_sales.cs_sold_time_sk = time_dim.t_time_sk
> and catalog_sales.cs_ship_mode_sk = ship_mode.sm_ship_mode_sk
> and d_year = 2002
> and t_time between 49530 AND 49530+28800
> and sm_carrier in ('DIAMOND','AIRBORNE')
> group by
> w_warehouse_name
> ,w_warehouse_sq_ft
> ,w_city
> ,w_county
> ,w_state
> ,w_country
> ,d_year
> ) x
> group by
> w_warehouse_name
> ,w_warehouse_sq_ft
> ,w_city
> ,w_county
> ,w_state
> ,w_country
> ,ship_carriers
> ,year
> order by w_warehouse_name
> limit 100
> OK
> STAGE DEPENDENCIES:
> Stage-1 is a root stage
> Stage-0 depends on stages: Stage-1
> STAGE PLANS:
> Stage: Stage-1
> Tez
> Edges:
> Map 12 <- Map 15 (BROADCAST_EDGE), Map 16 (BROADCAST_EDGE)
> Map 2 <- Map 8 (BROADCAST_EDGE), Map 9 (BROADCAST_EDGE)
> Reducer 13 <- Map 11 (BROADCAST_EDGE), Map 12 (SIMPLE_EDGE), Map 17 (SIMPLE_EDGE)
> Reducer 14 <- Reducer 13 (SIMPLE_EDGE), Union 5 (CONTAINS)
> Reducer 3 <- Map 1 (BROADCAST_EDGE), Map 10 (SIMPLE_EDGE), Map 2 (SIMPLE_EDGE)
> Reducer 4 <- Reducer 3 (SIMPLE_EDGE), Union 5 (CONTAINS)
> Reducer 6 <- Union 5 (SIMPLE_EDGE)
> Reducer 7 <- Reducer 6 (SIMPLE_EDGE)
> DagName: mmokhtar_20150211222424_0df571ed-82d9-426e-9eb9-52f95f022fa1:1
> Vertices:
> Map 1
> Map Operator Tree:
> TableScan
> alias: date_dim
> filterExpr: ((d_year = 2002) and d_date_sk is not null) (type: boolean)
> Statistics: Num rows: 73049 Data size: 81741831 Basic stats: COMPLETE Column stats: COMPLETE
> Filter Operator
> predicate: ((d_year = 2002) and d_date_sk is not null) (type: boolean)
> Statistics: Num rows: 652 Data size: 7824 Basic stats: COMPLETE Column stats: COMPLETE
> Select Operator
> expressions: d_date_sk (type: int), d_moy (type: int)
> outputColumnNames: _col0, _col2
> Statistics: Num rows: 652 Data size: 5216 Basic stats: COMPLETE Column stats: COMPLETE
> Reduce Output Operator
> key expressions: _col0 (type: int)
> sort order: +
> Map-reduce partition columns: _col0 (type: int)
> Statistics: Num rows: 652 Data size: 5216 Basic stats: COMPLETE Column stats: COMPLETE
> value expressions: _col2 (type: int)
> Execution mode: vectorized
> Map 10
> Map Operator Tree:
> TableScan
> alias: ship_mode
> filterExpr: ((sm_carrier) IN ('DIAMOND', 'AIRBORNE') and sm_ship_mode_sk is not null) (type: boolean)
> Statistics: Num rows: 0 Data size: 47 Basic stats: PARTIAL Column stats: COMPLETE
> Filter Operator
> predicate: ((sm_carrier) IN ('DIAMOND', 'AIRBORNE') and sm_ship_mode_sk is not null) (type: boolean)
> Statistics: Num rows: 9223372036854775807 Data size: 9223372036854775807 Basic stats: COMPLETE Column stats: COMPLETE
> Select Operator
> expressions: sm_ship_mode_sk (type: int)
> outputColumnNames: _col0
> Statistics: Num rows: 9223372036854775807 Data size: 9223372036854775807 Basic stats: COMPLETE Column stats: COMPLETE
> Reduce Output Operator
> key expressions: _col0 (type: int)
> sort order: +
> Map-reduce partition columns: _col0 (type: int)
> Statistics: Num rows: 9223372036854775807 Data size: 9223372036854775807 Basic stats: COMPLETE Column stats: COMPLETE
> Execution mode: vectorized
> Map 11
> Map Operator Tree:
> TableScan
> alias: date_dim
> filterExpr: ((d_year = 2002) and d_date_sk is not null) (type: boolean)
> Statistics: Num rows: 73049 Data size: 81741831 Basic stats: COMPLETE Column stats: COMPLETE
> Filter Operator
> predicate: ((d_year = 2002) and d_date_sk is not null) (type: boolean)
> Statistics: Num rows: 652 Data size: 7824 Basic stats: COMPLETE Column stats: COMPLETE
> Select Operator
> expressions: d_date_sk (type: int), d_moy (type: int)
> outputColumnNames: _col0, _col2
> Statistics: Num rows: 652 Data size: 5216 Basic stats: COMPLETE Column stats: COMPLETE
> Reduce Output Operator
> key expressions: _col0 (type: int)
> sort order: +
> Map-reduce partition columns: _col0 (type: int)
> Statistics: Num rows: 652 Data size: 5216 Basic stats: COMPLETE Column stats: COMPLETE
> value expressions: _col2 (type: int)
> Execution mode: vectorized
> Map 12
> Map Operator Tree:
> TableScan
> alias: catalog_sales
> filterExpr: (((cs_warehouse_sk is not null and cs_sold_time_sk is not null) and cs_ship_mode_sk is not null) and cs_sold_date_sk is not null) (type: boolean)
> Statistics: Num rows: 286549727 Data size: 65825832570 Basic stats: COMPLETE Column stats: COMPLETE
> Filter Operator
> predicate: (((cs_warehouse_sk is not null and cs_sold_time_sk is not null) and cs_ship_mode_sk is not null) and cs_sold_date_sk is not null) (type: boolean)
> Statistics: Num rows: 284394646 Data size: 7948760032 Basic stats: COMPLETE Column stats: COMPLETE
> Select Operator
> expressions: cs_sold_date_sk (type: int), cs_sold_time_sk (type: int), cs_ship_mode_sk (type: int), cs_warehouse_sk (type: int), cs_quantity (type: int), cs_ext_sales_price (type: float), cs_net_paid_inc_ship_tax (type: float)
> outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6
> Statistics: Num rows: 284394646 Data size: 7948760032 Basic stats: COMPLETE Column stats: COMPLETE
> Map Join Operator
> condition map:
> Inner Join 0 to 1
> keys:
> 0 _col3 (type: int)
> 1 _col0 (type: int)
> outputColumnNames: _col0, _col1, _col2, _col4, _col5, _col6, _col8, _col9, _col10, _col11, _col12, _col13
> input vertices:
> 1 Map 15
> Statistics: Num rows: 284394656 Data size: 142766117312 Basic stats: COMPLETE Column stats: COMPLETE
> Map Join Operator
> condition map:
> Inner Join 0 to 1
> keys:
> 0 _col1 (type: int)
> 1 _col0 (type: int)
> outputColumnNames: _col0, _col2, _col4, _col5, _col6, _col8, _col9, _col10, _col11, _col12, _col13
> input vertices:
> 1 Map 16
> Statistics: Num rows: 142197328 Data size: 70814269344 Basic stats: COMPLETE Column stats: COMPLETE
> Reduce Output Operator
> key expressions: _col2 (type: int)
> sort order: +
> Map-reduce partition columns: _col2 (type: int)
> Statistics: Num rows: 142197328 Data size: 70814269344 Basic stats: COMPLETE Column stats: COMPLETE
> value expressions: _col0 (type: int), _col4 (type: int), _col5 (type: float), _col6 (type: float), _col8 (type: string), _col9 (type: int), _col10 (type: string), _col11 (type: string), _col12 (type: string), _col13 (type: string)
> Execution mode: vectorized
> Map 15
> Map Operator Tree:
> TableScan
> alias: warehouse
> filterExpr: w_warehouse_sk is not null (type: boolean)
> Statistics: Num rows: 6 Data size: 6166 Basic stats: COMPLETE Column stats: COMPLETE
> Filter Operator
> predicate: w_warehouse_sk is not null (type: boolean)
> Statistics: Num rows: 6 Data size: 2888 Basic stats: COMPLETE Column stats: COMPLETE
> Select Operator
> expressions: w_warehouse_sk (type: int), w_warehouse_name (type: string), w_warehouse_sq_ft (type: int), w_city (type: string), w_county (type: string), w_state (type: string), w_country (type: string)
> outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6
> Statistics: Num rows: 6 Data size: 2888 Basic stats: COMPLETE Column stats: COMPLETE
> Reduce Output Operator
> key expressions: _col0 (type: int)
> sort order: +
> Map-reduce partition columns: _col0 (type: int)
> Statistics: Num rows: 6 Data size: 2888 Basic stats: COMPLETE Column stats: COMPLETE
> value expressions: _col1 (type: string), _col2 (type: int), _col3 (type: string), _col4 (type: string), _col5 (type: string), _col6 (type: string)
> Execution mode: vectorized
> Map 16
> Map Operator Tree:
> TableScan
> alias: time_dim
> filterExpr: (t_time BETWEEN 49530 AND 78330 and t_time_sk is not null) (type: boolean)
> Statistics: Num rows: 86400 Data size: 40694400 Basic stats: COMPLETE Column stats: COMPLETE
> Filter Operator
> predicate: (t_time BETWEEN 49530 AND 78330 and t_time_sk is not null) (type: boolean)
> Statistics: Num rows: 43200 Data size: 345600 Basic stats: COMPLETE Column stats: COMPLETE
> Select Operator
> expressions: t_time_sk (type: int)
> outputColumnNames: _col0
> Statistics: Num rows: 43200 Data size: 172800 Basic stats: COMPLETE Column stats: COMPLETE
> Reduce Output Operator
> key expressions: _col0 (type: int)
> sort order: +
> Map-reduce partition columns: _col0 (type: int)
> Statistics: Num rows: 43200 Data size: 172800 Basic stats: COMPLETE Column stats: COMPLETE
> Execution mode: vectorized
> Map 17
> Map Operator Tree:
> TableScan
> alias: ship_mode
> filterExpr: ((sm_carrier) IN ('DIAMOND', 'AIRBORNE') and sm_ship_mode_sk is not null) (type: boolean)
> Statistics: Num rows: 0 Data size: 47 Basic stats: PARTIAL Column stats: COMPLETE
> Filter Operator
> predicate: ((sm_carrier) IN ('DIAMOND', 'AIRBORNE') and sm_ship_mode_sk is not null) (type: boolean)
> Statistics: Num rows: 9223372036854775807 Data size: 9223372036854775807 Basic stats: COMPLETE Column stats: COMPLETE
> Select Operator
> expressions: sm_ship_mode_sk (type: int)
> outputColumnNames: _col0
> Statistics: Num rows: 9223372036854775807 Data size: 9223372036854775807 Basic stats: COMPLETE Column stats: COMPLETE
> Reduce Output Operator
> key expressions: _col0 (type: int)
> sort order: +
> Map-reduce partition columns: _col0 (type: int)
> Statistics: Num rows: 9223372036854775807 Data size: 9223372036854775807 Basic stats: COMPLETE Column stats: COMPLETE
> Execution mode: vectorized
> Map 2
> Map Operator Tree:
> TableScan
> alias: web_sales
> filterExpr: (((ws_warehouse_sk is not null and ws_sold_time_sk is not null) and ws_ship_mode_sk is not null) and ws_sold_date_sk is not null) (type: boolean)
> Statistics: Num rows: 143966864 Data size: 33110363004 Basic stats: COMPLETE Column stats: COMPLETE
> Filter Operator
> predicate: (((ws_warehouse_sk is not null and ws_sold_time_sk is not null) and ws_ship_mode_sk is not null) and ws_sold_date_sk is not null) (type: boolean)
> Statistics: Num rows: 143912967 Data size: 4029131264 Basic stats: COMPLETE Column stats: COMPLETE
> Select Operator
> expressions: ws_sold_date_sk (type: int), ws_sold_time_sk (type: int), ws_ship_mode_sk (type: int), ws_warehouse_sk (type: int), ws_quantity (type: int), ws_sales_price (type: float), ws_net_paid_inc_tax (type: float)
> outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6
> Statistics: Num rows: 143912967 Data size: 4029131264 Basic stats: COMPLETE Column stats: COMPLETE
> Map Join Operator
> condition map:
> Inner Join 0 to 1
> keys:
> 0 _col3 (type: int)
> 1 _col0 (type: int)
> outputColumnNames: _col0, _col1, _col2, _col4, _col5, _col6, _col8, _col9, _col10, _col11, _col12, _col13
> input vertices:
> 1 Map 8
> Statistics: Num rows: 143912960 Data size: 72244305920 Basic stats: COMPLETE Column stats: COMPLETE
> Map Join Operator
> condition map:
> Inner Join 0 to 1
> keys:
> 0 _col1 (type: int)
> 1 _col0 (type: int)
> outputColumnNames: _col0, _col2, _col4, _col5, _col6, _col8, _col9, _col10, _col11, _col12, _col13
> input vertices:
> 1 Map 9
> Statistics: Num rows: 71956480 Data size: 35834327040 Basic stats: COMPLETE Column stats: COMPLETE
> Reduce Output Operator
> key expressions: _col2 (type: int)
> sort order: +
> Map-reduce partition columns: _col2 (type: int)
> Statistics: Num rows: 71956480 Data size: 35834327040 Basic stats: COMPLETE Column stats: COMPLETE
> value expressions: _col0 (type: int), _col4 (type: int), _col5 (type: float), _col6 (type: float), _col8 (type: string), _col9 (type: int), _col10 (type: string), _col11 (type: string), _col12 (type: string), _col13 (type: string)
> Execution mode: vectorized
> Map 8
> Map Operator Tree:
> TableScan
> alias: warehouse
> filterExpr: w_warehouse_sk is not null (type: boolean)
> Statistics: Num rows: 6 Data size: 6166 Basic stats: COMPLETE Column stats: COMPLETE
> Filter Operator
> predicate: w_warehouse_sk is not null (type: boolean)
> Statistics: Num rows: 6 Data size: 2888 Basic stats: COMPLETE Column stats: COMPLETE
> Select Operator
> expressions: w_warehouse_sk (type: int), w_warehouse_name (type: string), w_warehouse_sq_ft (type: int), w_city (type: string), w_county (type: string), w_state (type: string), w_country (type: string)
> outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6
> Statistics: Num rows: 6 Data size: 2888 Basic stats: COMPLETE Column stats: COMPLETE
> Reduce Output Operator
> key expressions: _col0 (type: int)
> sort order: +
> Map-reduce partition columns: _col0 (type: int)
> Statistics: Num rows: 6 Data size: 2888 Basic stats: COMPLETE Column stats: COMPLETE
> value expressions: _col1 (type: string), _col2 (type: int), _col3 (type: string), _col4 (type: string), _col5 (type: string), _col6 (type: string)
> Execution mode: vectorized
> Map 9
> Map Operator Tree:
> TableScan
> alias: time_dim
> filterExpr: (t_time BETWEEN 49530 AND 78330 and t_time_sk is not null) (type: boolean)
> Statistics: Num rows: 86400 Data size: 40694400 Basic stats: COMPLETE Column stats: COMPLETE
> Filter Operator
> predicate: (t_time BETWEEN 49530 AND 78330 and t_time_sk is not null) (type: boolean)
> Statistics: Num rows: 43200 Data size: 345600 Basic stats: COMPLETE Column stats: COMPLETE
> Select Operator
> expressions: t_time_sk (type: int)
> outputColumnNames: _col0
> Statistics: Num rows: 43200 Data size: 172800 Basic stats: COMPLETE Column stats: COMPLETE
> Reduce Output Operator
> key expressions: _col0 (type: int)
> sort order: +
> Map-reduce partition columns: _col0 (type: int)
> Statistics: Num rows: 43200 Data size: 172800 Basic stats: COMPLETE Column stats: COMPLETE
> Execution mode: vectorized
> Reducer 13
> Reduce Operator Tree:
> Merge Join Operator
> condition map:
> Inner Join 0 to 1
> keys:
> 0 _col2 (type: int)
> 1 _col0 (type: int)
> outputColumnNames: _col0, _col4, _col5, _col6, _col8, _col9, _col10, _col11, _col12, _col13
> Statistics: Num rows: 9223372036854775807 Data size: 9223372036854775807 Basic stats: COMPLETE Column stats: COMPLETE
> Select Operator
> expressions: _col0 (type: int), _col10 (type: string), _col11 (type: string), _col12 (type: string), _col13 (type: string), _col4 (type: int), _col5 (type: float), _col6 (type: float), _col8 (type: string), _col9 (type: int)
> outputColumnNames: _col0, _col10, _col11, _col12, _col13, _col4, _col5, _col6, _col8, _col9
> Statistics: Num rows: 9223372036854775807 Data size: 9223372036854775807 Basic stats: COMPLETE Column stats: COMPLETE
> Map Join Operator
> condition map:
> Inner Join 0 to 1
> keys:
> 0 _col0 (type: int)
> 1 _col0 (type: int)
> outputColumnNames: _col2, _col7, _col8, _col9, _col11, _col12, _col13, _col14, _col15, _col16
> input vertices:
> 0 Map 11
> Statistics: Num rows: 82323356149350400 Data size: 9223372036854775807 Basic stats: COMPLETE Column stats: COMPLETE
> Select Operator
> expressions: _col11 (type: string), _col12 (type: int), _col13 (type: string), _col14 (type: string), _col15 (type: string), _col16 (type: string), 2002 (type: int), CASE WHEN ((_col2 = 1)) THEN ((_col8 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 2)) THEN ((_col8 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 3)) THEN ((_col8 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 4)) THEN ((_col8 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 5)) THEN ((_col8 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 6)) THEN ((_col8 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 7)) THEN ((_col8 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 8)) THEN ((_col8 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 9)) THEN ((_col8 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 10)) THEN ((_col8 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 11)) THEN ((_col8 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 12)) THEN ((_col8 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 1)) THEN ((_col9 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 2)) THEN ((_col9 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 3)) THEN ((_col9 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 4)) THEN ((_col9 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 5)) THEN ((_col9 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 6)) THEN ((_col9 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 7)) THEN ((_col9 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 8)) THEN ((_col9 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 9)) THEN ((_col9 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 10)) THEN ((_col9 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 11)) THEN ((_col9 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 12)) THEN ((_col9 * UDFToFloat(_col7))) ELSE (0) END (type: float)
> outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9, _col10, _col11, _col12, _col13, _col14, _col15, _col16, _col17, _col18, _col19, _col20, _col21, _col22, _col23, _col24, _col25, _col26, _col27, _col28, _col29, _col30
> Statistics: Num rows: 82323356149350400 Data size: 9223372036854775807 Basic stats: COMPLETE Column stats: COMPLETE
> Group By Operator
> aggregations: sum(_col7), sum(_col8), sum(_col9), sum(_col10), sum(_col11), sum(_col12), sum(_col13), sum(_col14), sum(_col15), sum(_col16), sum(_col17), sum(_col18), sum(_col19), sum(_col20), sum(_col21), sum(_col22), sum(_col23), sum(_col24), sum(_col25), sum(_col26), sum(_col27), sum(_col28), sum(_col29), sum(_col30)
> keys: _col0 (type: string), _col1 (type: int), _col2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type: string), _col6 (type: int)
> mode: hash
> outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9, _col10, _col11, _col12, _col13, _col14, _col15, _col16, _col17, _col18, _col19, _col20, _col21, _col22, _col23, _col24, _col25, _col26, _col27, _col28, _col29, _col30
> Statistics: Num rows: 2147483647 Data size: 1447403978078 Basic stats: COMPLETE Column stats: COMPLETE
> Reduce Output Operator
> key expressions: _col0 (type: string), _col1 (type: int), _col2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type: string), _col6 (type: int)
> sort order: +++++++
> Map-reduce partition columns: _col0 (type: string), _col1 (type: int), _col2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type: string), _col6 (type: int)
> Statistics: Num rows: 2147483647 Data size: 1447403978078 Basic stats: COMPLETE Column stats: COMPLETE
> value expressions: _col7 (type: double), _col8 (type: double), _col9 (type: double), _col10 (type: double), _col11 (type: double), _col12 (type: double), _col13 (type: double), _col14 (type: double), _col15 (type: double), _col16 (type: double), _col17 (type: double), _col18 (type: double), _col19 (type: double), _col20 (type: double), _col21 (type: double), _col22 (type: double), _col23 (type: double), _col24 (type: double), _col25 (type: double), _col26 (type: double), _col27 (type: double), _col28 (type: double), _col29 (type: double), _col30 (type: double)
> Reducer 14
> Reduce Operator Tree:
> Group By Operator
> aggregations: sum(VALUE._col0), sum(VALUE._col1), sum(VALUE._col2), sum(VALUE._col3), sum(VALUE._col4), sum(VALUE._col5), sum(VALUE._col6), sum(VALUE._col7), sum(VALUE._col8), sum(VALUE._col9), sum(VALUE._col10), sum(VALUE._col11), sum(VALUE._col12), sum(VALUE._col13), sum(VALUE._col14), sum(VALUE._col15), sum(VALUE._col16), sum(VALUE._col17), sum(VALUE._col18), sum(VALUE._col19), sum(VALUE._col20), sum(VALUE._col21), sum(VALUE._col22), sum(VALUE._col23)
> keys: KEY._col0 (type: string), KEY._col1 (type: int), KEY._col2 (type: string), KEY._col3 (type: string), KEY._col4 (type: string), KEY._col5 (type: string), KEY._col6 (type: int)
> mode: mergepartial
> outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9, _col10, _col11, _col12, _col13, _col14, _col15, _col16, _col17, _col18, _col19, _col20, _col21, _col22, _col23, _col24, _col25, _col26, _col27, _col28, _col29, _col30
> Select Operator
> expressions: _col0 (type: string), _col1 (type: int), _col2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type: string), 'DIAMOND,AIRBORNE' (type: string), _col6 (type: int), _col7 (type: double), _col8 (type: double), _col9 (type: double), _col10 (type: double), _col11 (type: double), _col12 (type: double), _col13 (type: double), _col14 (type: double), _col15 (type: double), _col16 (type: double), _col17 (type: double), _col18 (type: double), _col19 (type: double), _col20 (type: double), _col21 (type: double), _col22 (type: double), _col23 (type: double), _col24 (type: double), _col25 (type: double), _col26 (type: double), _col27 (type: double), _col28 (type: double), _col29 (type: double), _col30 (type: double)
> outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9, _col10, _col11, _col12, _col13, _col14, _col15, _col16, _col17, _col18, _col19, _col20, _col21, _col22, _col23, _col24, _col25, _col26, _col27, _col28, _col29, _col30, _col31
> Select Operator
> expressions: _col0 (type: string), _col1 (type: int), _col2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type: string), _col6 (type: string), _col7 (type: int), _col8 (type: double), _col9 (type: double), _col10 (type: double), _col11 (type: double), _col12 (type: double), _col13 (type: double), _col14 (type: double), _col15 (type: double), _col16 (type: double), _col17 (type: double), _col18 (type: double), _col19 (type: double), (_col8 / UDFToDouble(_col1)) (type: double), (_col9 / UDFToDouble(_col1)) (type: double), (_col10 / UDFToDouble(_col1)) (type: double), (_col11 / UDFToDouble(_col1)) (type: double), (_col12 / UDFToDouble(_col1)) (type: double), (_col13 / UDFToDouble(_col1)) (type: double), (_col14 / UDFToDouble(_col1)) (type: double), (_col15 / UDFToDouble(_col1)) (type: double), (_col16 / UDFToDouble(_col1)) (type: double), (_col17 / UDFToDouble(_col1)) (type: double), (_col18 / UDFToDouble(_col1)) (type: double), (_col19 / UDFToDouble(_col1)) (type: double), _col20 (type: double), _col21 (type: double), _col22 (type: double), _col23 (type: double), _col24 (type: double), _col25 (type: double), _col26 (type: double), _col27 (type: double), _col28 (type: double), _col29 (type: double), _col30 (type: double), _col31 (type: double)
> outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9, _col10, _col11, _col12, _col13, _col14, _col15, _col16, _col17, _col18, _col19, _col20, _col21, _col22, _col23, _col24, _col25, _col26, _col27, _col28, _col29, _col30, _col31, _col32, _col33, _col34, _col35, _col36, _col37, _col38, _col39, _col40, _col41, _col42, _col43
> Group By Operator
> aggregations: sum(_col8), sum(_col9), sum(_col10), sum(_col11), sum(_col12), sum(_col13), sum(_col14), sum(_col15), sum(_col16), sum(_col17), sum(_col18), sum(_col19), sum(_col20), sum(_col21), sum(_col22), sum(_col23), sum(_col24), sum(_col25), sum(_col26), sum(_col27), sum(_col28), sum(_col29), sum(_col30), sum(_col31), sum(_col32), sum(_col33), sum(_col34), sum(_col35), sum(_col36), sum(_col37), sum(_col38), sum(_col39), sum(_col40), sum(_col41), sum(_col42), sum(_col43)
> keys: _col0 (type: string), _col1 (type: int), _col2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type: string), _col6 (type: string), _col7 (type: int)
> mode: hash
> outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9, _col10, _col11, _col12, _col13, _col14, _col15, _col16, _col17, _col18, _col19, _col20, _col21, _col22, _col23, _col24, _col25, _col26, _col27, _col28, _col29, _col30, _col31, _col32, _col33, _col34, _col35, _col36, _col37, _col38, _col39, _col40, _col41, _col42, _col43
> Reduce Output Operator
> key expressions: _col0 (type: string), _col1 (type: int), _col2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type: string), _col6 (type: string), _col7 (type: int)
> sort order: ++++++++
> Map-reduce partition columns: _col0 (type: string), _col1 (type: int), _col2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type: string), _col6 (type: string), _col7 (type: int)
> value expressions: _col8 (type: double), _col9 (type: double), _col10 (type: double), _col11 (type: double), _col12 (type: double), _col13 (type: double), _col14 (type: double), _col15 (type: double), _col16 (type: double), _col17 (type: double), _col18 (type: double), _col19 (type: double), _col20 (type: double), _col21 (type: double), _col22 (type: double), _col23 (type: double), _col24 (type: double), _col25 (type: double), _col26 (type: double), _col27 (type: double), _col28 (type: double), _col29 (type: double), _col30 (type: double), _col31 (type: double), _col32 (type: double), _col33 (type: double), _col34 (type: double), _col35 (type: double), _col36 (type: double), _col37 (type: double), _col38 (type: double), _col39 (type: double), _col40 (type: double), _col41 (type: double), _col42 (type: double), _col43 (type: double)
> Reducer 3
> Reduce Operator Tree:
> Merge Join Operator
> condition map:
> Inner Join 0 to 1
> keys:
> 0 _col2 (type: int)
> 1 _col0 (type: int)
> outputColumnNames: _col0, _col4, _col5, _col6, _col8, _col9, _col10, _col11, _col12, _col13
> Statistics: Num rows: 9223372036854775807 Data size: 9223372036854775807 Basic stats: COMPLETE Column stats: COMPLETE
> Select Operator
> expressions: _col0 (type: int), _col10 (type: string), _col11 (type: string), _col12 (type: string), _col13 (type: string), _col4 (type: int), _col5 (type: float), _col6 (type: float), _col8 (type: string), _col9 (type: int)
> outputColumnNames: _col0, _col10, _col11, _col12, _col13, _col4, _col5, _col6, _col8, _col9
> Statistics: Num rows: 9223372036854775807 Data size: 9223372036854775807 Basic stats: COMPLETE Column stats: COMPLETE
> Map Join Operator
> condition map:
> Inner Join 0 to 1
> keys:
> 0 _col0 (type: int)
> 1 _col0 (type: int)
> outputColumnNames: _col2, _col7, _col8, _col9, _col11, _col12, _col13, _col14, _col15, _col16
> input vertices:
> 0 Map 1
> Statistics: Num rows: 82323356149350400 Data size: 9223372036854775807 Basic stats: COMPLETE Column stats: COMPLETE
> Select Operator
> expressions: _col11 (type: string), _col12 (type: int), _col13 (type: string), _col14 (type: string), _col15 (type: string), _col16 (type: string), 2002 (type: int), CASE WHEN ((_col2 = 1)) THEN ((_col8 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 2)) THEN ((_col8 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 3)) THEN ((_col8 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 4)) THEN ((_col8 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 5)) THEN ((_col8 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 6)) THEN ((_col8 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 7)) THEN ((_col8 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 8)) THEN ((_col8 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 9)) THEN ((_col8 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 10)) THEN ((_col8 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 11)) THEN ((_col8 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 12)) THEN ((_col8 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 1)) THEN ((_col9 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 2)) THEN ((_col9 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 3)) THEN ((_col9 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 4)) THEN ((_col9 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 5)) THEN ((_col9 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 6)) THEN ((_col9 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 7)) THEN ((_col9 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 8)) THEN ((_col9 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 9)) THEN ((_col9 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 10)) THEN ((_col9 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 11)) THEN ((_col9 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 12)) THEN ((_col9 * UDFToFloat(_col7))) ELSE (0) END (type: float)
> outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9, _col10, _col11, _col12, _col13, _col14, _col15, _col16, _col17, _col18, _col19, _col20, _col21, _col22, _col23, _col24, _col25, _col26, _col27, _col28, _col29, _col30
> Statistics: Num rows: 82323356149350400 Data size: 9223372036854775807 Basic stats: COMPLETE Column stats: COMPLETE
> Group By Operator
> aggregations: sum(_col7), sum(_col8), sum(_col9), sum(_col10), sum(_col11), sum(_col12), sum(_col13), sum(_col14), sum(_col15), sum(_col16), sum(_col17), sum(_col18), sum(_col19), sum(_col20), sum(_col21), sum(_col22), sum(_col23), sum(_col24), sum(_col25), sum(_col26), sum(_col27), sum(_col28), sum(_col29), sum(_col30)
> keys: _col0 (type: string), _col1 (type: int), _col2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type: string), _col6 (type: int)
> mode: hash
> outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9, _col10, _col11, _col12, _col13, _col14, _col15, _col16, _col17, _col18, _col19, _col20, _col21, _col22, _col23, _col24, _col25, _col26, _col27, _col28, _col29, _col30
> Statistics: Num rows: 2147483647 Data size: 1447403978078 Basic stats: COMPLETE Column stats: COMPLETE
> Reduce Output Operator
> key expressions: _col0 (type: string), _col1 (type: int), _col2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type: string), _col6 (type: int)
> sort order: +++++++
> Map-reduce partition columns: _col0 (type: string), _col1 (type: int), _col2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type: string), _col6 (type: int)
> Statistics: Num rows: 2147483647 Data size: 1447403978078 Basic stats: COMPLETE Column stats: COMPLETE
> value expressions: _col7 (type: double), _col8 (type: double), _col9 (type: double), _col10 (type: double), _col11 (type: double), _col12 (type: double), _col13 (type: double), _col14 (type: double), _col15 (type: double), _col16 (type: double), _col17 (type: double), _col18 (type: double), _col19 (type: double), _col20 (type: double), _col21 (type: double), _col22 (type: double), _col23 (type: double), _col24 (type: double), _col25 (type: double), _col26 (type: double), _col27 (type: double), _col28 (type: double), _col29 (type: double), _col30 (type: double)
> Reducer 4
> Reduce Operator Tree:
> Group By Operator
> aggregations: sum(VALUE._col0), sum(VALUE._col1), sum(VALUE._col2), sum(VALUE._col3), sum(VALUE._col4), sum(VALUE._col5), sum(VALUE._col6), sum(VALUE._col7), sum(VALUE._col8), sum(VALUE._col9), sum(VALUE._col10), sum(VALUE._col11), sum(VALUE._col12), sum(VALUE._col13), sum(VALUE._col14), sum(VALUE._col15), sum(VALUE._col16), sum(VALUE._col17), sum(VALUE._col18), sum(VALUE._col19), sum(VALUE._col20), sum(VALUE._col21), sum(VALUE._col22), sum(VALUE._col23)
> keys: KEY._col0 (type: string), KEY._col1 (type: int), KEY._col2 (type: string), KEY._col3 (type: string), KEY._col4 (type: string), KEY._col5 (type: string), KEY._col6 (type: int)
> mode: mergepartial
> outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9, _col10, _col11, _col12, _col13, _col14, _col15, _col16, _col17, _col18, _col19, _col20, _col21, _col22, _col23, _col24, _col25, _col26, _col27, _col28, _col29, _col30
> Select Operator
> expressions: _col0 (type: string), _col1 (type: int), _col2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type: string), 'DIAMOND,AIRBORNE' (type: string), _col6 (type: int), _col7 (type: double), _col8 (type: double), _col9 (type: double), _col10 (type: double), _col11 (type: double), _col12 (type: double), _col13 (type: double), _col14 (type: double), _col15 (type: double), _col16 (type: double), _col17 (type: double), _col18 (type: double), _col19 (type: double), _col20 (type: double), _col21 (type: double), _col22 (type: double), _col23 (type: double), _col24 (type: double), _col25 (type: double), _col26 (type: double), _col27 (type: double), _col28 (type: double), _col29 (type: double), _col30 (type: double)
> outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9, _col10, _col11, _col12, _col13, _col14, _col15, _col16, _col17, _col18, _col19, _col20, _col21, _col22, _col23, _col24, _col25, _col26, _col27, _col28, _col29, _col30, _col31
> Select Operator
> expressions: _col0 (type: string), _col1 (type: int), _col2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type: string), _col6 (type: string), _col7 (type: int), _col8 (type: double), _col9 (type: double), _col10 (type: double), _col11 (type: double), _col12 (type: double), _col13 (type: double), _col14 (type: double), _col15 (type: double), _col16 (type: double), _col17 (type: double), _col18 (type: double), _col19 (type: double), (_col8 / UDFToDouble(_col1)) (type: double), (_col9 / UDFToDouble(_col1)) (type: double), (_col10 / UDFToDouble(_col1)) (type: double), (_col11 / UDFToDouble(_col1)) (type: double), (_col12 / UDFToDouble(_col1)) (type: double), (_col13 / UDFToDouble(_col1)) (type: double), (_col14 / UDFToDouble(_col1)) (type: double), (_col15 / UDFToDouble(_col1)) (type: double), (_col16 / UDFToDouble(_col1)) (type: double), (_col17 / UDFToDouble(_col1)) (type: double), (_col18 / UDFToDouble(_col1)) (type: double), (_col19 / UDFToDouble(_col1)) (type: double), _col20 (type: double), _col21 (type: double), _col22 (type: double), _col23 (type: double), _col24 (type: double), _col25 (type: double), _col26 (type: double), _col27 (type: double), _col28 (type: double), _col29 (type: double), _col30 (type: double), _col31 (type: double)
> outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9, _col10, _col11, _col12, _col13, _col14, _col15, _col16, _col17, _col18, _col19, _col20, _col21, _col22, _col23, _col24, _col25, _col26, _col27, _col28, _col29, _col30, _col31, _col32, _col33, _col34, _col35, _col36, _col37, _col38, _col39, _col40, _col41, _col42, _col43
> Group By Operator
> aggregations: sum(_col8), sum(_col9), sum(_col10), sum(_col11), sum(_col12), sum(_col13), sum(_col14), sum(_col15), sum(_col16), sum(_col17), sum(_col18), sum(_col19), sum(_col20), sum(_col21), sum(_col22), sum(_col23), sum(_col24), sum(_col25), sum(_col26), sum(_col27), sum(_col28), sum(_col29), sum(_col30), sum(_col31), sum(_col32), sum(_col33), sum(_col34), sum(_col35), sum(_col36), sum(_col37), sum(_col38), sum(_col39), sum(_col40), sum(_col41), sum(_col42), sum(_col43)
> keys: _col0 (type: string), _col1 (type: int), _col2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type: string), _col6 (type: string), _col7 (type: int)
> mode: hash
> outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9, _col10, _col11, _col12, _col13, _col14, _col15, _col16, _col17, _col18, _col19, _col20, _col21, _col22, _col23, _col24, _col25, _col26, _col27, _col28, _col29, _col30, _col31, _col32, _col33, _col34, _col35, _col36, _col37, _col38, _col39, _col40, _col41, _col42, _col43
> Reduce Output Operator
> key expressions: _col0 (type: string), _col1 (type: int), _col2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type: string), _col6 (type: string), _col7 (type: int)
> sort order: ++++++++
> Map-reduce partition columns: _col0 (type: string), _col1 (type: int), _col2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type: string), _col6 (type: string), _col7 (type: int)
> value expressions: _col8 (type: double), _col9 (type: double), _col10 (type: double), _col11 (type: double), _col12 (type: double), _col13 (type: double), _col14 (type: double), _col15 (type: double), _col16 (type: double), _col17 (type: double), _col18 (type: double), _col19 (type: double), _col20 (type: double), _col21 (type: double), _col22 (type: double), _col23 (type: double), _col24 (type: double), _col25 (type: double), _col26 (type: double), _col27 (type: double), _col28 (type: double), _col29 (type: double), _col30 (type: double), _col31 (type: double), _col32 (type: double), _col33 (type: double), _col34 (type: double), _col35 (type: double), _col36 (type: double), _col37 (type: double), _col38 (type: double), _col39 (type: double), _col40 (type: double), _col41 (type: double), _col42 (type: double), _col43 (type: double)
> Reducer 6
> Reduce Operator Tree:
> Group By Operator
> aggregations: sum(VALUE._col0), sum(VALUE._col1), sum(VALUE._col2), sum(VALUE._col3), sum(VALUE._col4), sum(VALUE._col5), sum(VALUE._col6), sum(VALUE._col7), sum(VALUE._col8), sum(VALUE._col9), sum(VALUE._col10), sum(VALUE._col11), sum(VALUE._col12), sum(VALUE._col13), sum(VALUE._col14), sum(VALUE._col15), sum(VALUE._col16), sum(VALUE._col17), sum(VALUE._col18), sum(VALUE._col19), sum(VALUE._col20), sum(VALUE._col21), sum(VALUE._col22), sum(VALUE._col23), sum(VALUE._col24), sum(VALUE._col25), sum(VALUE._col26), sum(VALUE._col27), sum(VALUE._col28), sum(VALUE._col29), sum(VALUE._col30), sum(VALUE._col31), sum(VALUE._col32), sum(VALUE._col33), sum(VALUE._col34), sum(VALUE._col35)
> keys: KEY._col0 (type: string), KEY._col1 (type: int), KEY._col2 (type: string), KEY._col3 (type: string), KEY._col4 (type: string), KEY._col5 (type: string), KEY._col6 (type: string), KEY._col7 (type: int)
> mode: mergepartial
> outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9, _col10, _col11, _col12, _col13, _col14, _col15, _col16, _col17, _col18, _col19, _col20, _col21, _col22, _col23, _col24, _col25, _col26, _col27, _col28, _col29, _col30, _col31, _col32, _col33, _col34, _col35, _col36, _col37, _col38, _col39, _col40, _col41, _col42, _col43
> Statistics: Num rows: 1 Data size: 288 Basic stats: COMPLETE Column stats: COMPLETE
> Reduce Output Operator
> key expressions: _col0 (type: string)
> sort order: +
> Statistics: Num rows: 1 Data size: 288 Basic stats: COMPLETE Column stats: COMPLETE
> TopN Hash Memory Usage: 0.04
> value expressions: _col1 (type: int), _col2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type: string), _col6 (type: string), _col7 (type: int), _col8 (type: double), _col9 (type: double), _col10 (type: double), _col11 (type: double), _col12 (type: double), _col13 (type: double), _col14 (type: double), _col15 (type: double), _col16 (type: double), _col17 (type: double), _col18 (type: double), _col19 (type: double), _col20 (type: double), _col21 (type: double), _col22 (type: double), _col23 (type: double), _col24 (type: double), _col25 (type: double), _col26 (type: double), _col27 (type: double), _col28 (type: double), _col29 (type: double), _col30 (type: double), _col31 (type: double), _col32 (type: double), _col33 (type: double), _col34 (type: double), _col35 (type: double), _col36 (type: double), _col37 (type: double), _col38 (type: double), _col39 (type: double), _col40 (type: double), _col41 (type: double), _col42 (type: double), _col43 (type: double)
> Reducer 7
> Reduce Operator Tree:
> Select Operator
> expressions: KEY.reducesinkkey0 (type: string), VALUE._col0 (type: int), VALUE._col1 (type: string), VALUE._col2 (type: string), VALUE._col3 (type: string), VALUE._col4 (type: string), VALUE._col5 (type: string), VALUE._col6 (type: int), VALUE._col7 (type: double), VALUE._col8 (type: double), VALUE._col9 (type: double), VALUE._col10 (type: double), VALUE._col11 (type: double), VALUE._col12 (type: double), VALUE._col13 (type: double), VALUE._col14 (type: double), VALUE._col15 (type: double), VALUE._col16 (type: double), VALUE._col17 (type: double), VALUE._col18 (type: double), VALUE._col19 (type: double), VALUE._col20 (type: double), VALUE._col21 (type: double), VALUE._col22 (type: double), VALUE._col23 (type: double), VALUE._col24 (type: double), VALUE._col25 (type: double), VALUE._col26 (type: double), VALUE._col27 (type: double), VALUE._col28 (type: double), VALUE._col29 (type: double), VALUE._col30 (type: double), VALUE._col31 (type: double), VALUE._col32 (type: double), VALUE._col33 (type: double), VALUE._col34 (type: double), VALUE._col35 (type: double), VALUE._col36 (type: double), VALUE._col37 (type: double), VALUE._col38 (type: double), VALUE._col39 (type: double), VALUE._col40 (type: double), VALUE._col41 (type: double), VALUE._col42 (type: double)
> outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9, _col10, _col11, _col12, _col13, _col14, _col15, _col16, _col17, _col18, _col19, _col20, _col21, _col22, _col23, _col24, _col25, _col26, _col27, _col28, _col29, _col30, _col31, _col32, _col33, _col34, _col35, _col36, _col37, _col38, _col39, _col40, _col41, _col42, _col43
> Statistics: Num rows: 1 Data size: 288 Basic stats: COMPLETE Column stats: COMPLETE
> Limit
> Number of rows: 100
> Statistics: Num rows: 1 Data size: 288 Basic stats: COMPLETE Column stats: COMPLETE
> File Output Operator
> compressed: false
> Statistics: Num rows: 1 Data size: 288 Basic stats: COMPLETE Column stats: COMPLETE
> 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
> Union 5
> Vertex: Union 5
> Stage: Stage-0
> Fetch Operator
> limit: 100
> Processor Tree:
> ListSink
> {code}
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