Mostafa Mokhtar created HIVE-9712:
-------------------------------------
Summary: Hive : Row count and data size are set to LONG.MAX when
filter is applied on an estimate of 0
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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