Description
The join order has Item joined last where it should be joined first
Query
select ca_zip, ca_county, sum(ws_sales_price)
from
web_sales
JOIN customer ON web_sales.ws_bill_customer_sk = customer.c_customer_sk
JOIN customer_address ON customer.c_current_addr_sk = customer_address.ca_address_sk
JOIN date_dim ON web_sales.ws_sold_date_sk = date_dim.d_date_sk
JOIN item ON web_sales.ws_item_sk = item.i_item_sk
where
( item.i_item_id in (select i_item_id
from item i2
where i2.i_item_sk in (2, 3, 5, 7, 11, 13, 17, 19, 23, 29)
)
)
and d_qoy = 2 and d_year = 2000
group by ca_zip, ca_county
order by ca_zip, ca_county
limit 100
Plan
2014-10-20 18:43:16,521 DEBUG [main]: parse.SemanticAnalyzer (SemanticAnalyzer.java:apply(12330)) - HiveSortRel(fetch=[100]): rowcount = 1.710158597922807E7, cumulative cost = {7.169080587598123E10 rows, 3.420317295845614E7 cpu, 0.0 io}, id = 579
HiveSortRel(sort0=[$0], sort1=[$1], dir0=[ASC], dir1=[ASC]): rowcount = 1.710158597922807E7, cumulative cost = {6.827294821015483E10 rows, 1.710158697922807E7 cpu, 0.0 io}, id = 577
HiveProjectRel(ca_zip=[$0], ca_county=[$1], _o__c2=[$2]): rowcount = 1.710158597922807E7, cumulative cost = {6.485509054432843E10 rows, 1.0 cpu, 0.0 io}, id = 575
HiveAggregateRel(group=[{0, 1}], agg#0=[sum($2)]): rowcount = 1.710158597922807E7, cumulative cost = {6.485509054432843E10 rows, 1.0 cpu, 0.0 io}, id = 573
HiveProjectRel($f0=[$2], $f1=[$1], $f2=[$0]): rowcount = 6.0197670310147226E7, cumulative cost = {6.485509054432843E10 rows, 1.0 cpu, 0.0 io}, id = 571
HiveProjectRel(ws_sales_price=[$2], ca_county=[$7], ca_zip=[$8]): rowcount = 6.0197670310147226E7, cumulative cost = {6.485509054432843E10 rows, 1.0 cpu, 0.0 io}, id = 569
HiveFilterRel(condition=[AND(=($11, 2), =($10, 2000))]): rowcount = 6.0197670310147226E7, cumulative cost = {6.485509054432843E10 rows, 1.0 cpu, 0.0 io}, id = 567
SemiJoinRel(condition=[=($13, $14)], joinType=[inner]): rowcount = 3.371069537368245E10, cumulative cost = {6.485509054432843E10 rows, 1.0 cpu, 0.0 io}, id = 565
HiveProjectRel(ws_item_sk=[$0], ws_bill_customer_sk=[$1], ws_sales_price=[$2], ws_sold_date_sk=[$3], c_customer_sk=[$9], c_current_addr_sk=[$10], ca_address_sk=[$11], ca_county=[$12], ca_zip=[$13], d_date_sk=[$6], d_year=[$7], d_qoy=[$8], i_item_sk=[$4], i_item_id=[$5]): rowcount = 3.371069537368245E10, cumulative cost = {6.485509054332843E10 rows, 0.0 cpu, 0.0 io}, id = 669
HiveJoinRel(condition=[=($1, $9)], joinType=[inner]): rowcount = 3.371069537368245E10, cumulative cost = {6.485509054332843E10 rows, 0.0 cpu, 0.0 io}, id = 667
HiveJoinRel(condition=[=($3, $6)], joinType=[inner]): rowcount = 2.1594638446E10, cumulative cost = {4.3189811941E10 rows, 0.0 cpu, 0.0 io}, id = 664
HiveJoinRel(condition=[=($0, $4)], joinType=[inner]): rowcount = 2.1594638446E10, cumulative cost = {2.1595100446E10 rows, 0.0 cpu, 0.0 io}, id = 601
HiveProjectRel(ws_item_sk=[$2], ws_bill_customer_sk=[$3], ws_sales_price=[$20], ws_sold_date_sk=[$33]): rowcount = 2.1594638446E10, cumulative cost = {0.0 rows, 0.0 cpu, 0.0 io}, id = 497
HiveTableScanRel(table=[[tpcds_bin_partitioned_orc_30000.web_sales]]): rowcount = 2.1594638446E10, cumulative cost = {0}, id = 341
HiveProjectRel(i_item_sk=[$0], i_item_id=[$1]): rowcount = 462000.0, cumulative cost = {0.0 rows, 0.0 cpu, 0.0 io}, id = 555
HiveTableScanRel(table=[[tpcds_bin_partitioned_orc_30000.item]]): rowcount = 462000.0, cumulative cost = {0}, id = 340
HiveProjectRel(d_date_sk=[$0], d_year=[$6], d_qoy=[$10]): rowcount = 73049.0, cumulative cost = {0.0 rows, 0.0 cpu, 0.0 io}, id = 551
HiveTableScanRel(table=[[tpcds_bin_partitioned_orc_30000.date_dim]]): rowcount = 73049.0, cumulative cost = {0}, id = 342
HiveJoinRel(condition=[=($1, $2)], joinType=[inner]): rowcount = 7.064015632843196E7, cumulative cost = {1.2E8 rows, 0.0 cpu, 0.0 io}, id = 598
HiveProjectRel(c_customer_sk=[$0], c_current_addr_sk=[$4]): rowcount = 8.0E7, cumulative cost = {0.0 rows, 0.0 cpu, 0.0 io}, id = 500
HiveTableScanRel(table=[[tpcds_bin_partitioned_orc_30000.customer]]): rowcount = 8.0E7, cumulative cost = {0}, id = 343
HiveProjectRel(ca_address_sk=[$0], ca_county=[$7], ca_zip=[$9]): rowcount = 4.0E7, cumulative cost = {0.0 rows, 0.0 cpu, 0.0 io}, id = 547
HiveTableScanRel(table=[[tpcds_bin_partitioned_orc_30000.customer_address]]): rowcount = 4.0E7, cumulative cost = {0}, id = 339
HiveProjectRel(i_item_id=[$1]): rowcount = 1.05119214745814, cumulative cost = {0.0 rows, 0.0 cpu, 0.0 io}, id = 563
HiveProjectRel(i_item_sk=[$0], i_item_id=[$1]): rowcount = 1.05119214745814, cumulative cost = {0.0 rows, 0.0 cpu, 0.0 io}, id = 561
HiveFilterRel(condition=[in($0, 2, 3, 5, 7, 11, 13, 17, 19, 23, 29)]): rowcount = 1.05119214745814, cumulative cost = {0.0 rows, 0.0 cpu, 0.0 io}, id = 559
HiveTableScanRel(table=[[tpcds_bin_partitioned_orc_30000.item]]): rowcount = 462000.0, cumulative cost = {0}, id = 340
Then I rewrote the query trying to force CBO to generate the correct join order
with items as (select i_item_sk from
item where
( item.i_item_id in (select i_item_id
from item i2
where i2.i_item_sk in (2, 3, 5, 7, 11, 13, 17, 19, 23, 29)
)
)
)
select ca_zip, ca_county, sum(ws_sales_price)
from
web_sales
JOIN items ON web_sales.ws_item_sk = items.i_item_sk
JOIN customer ON web_sales.ws_bill_customer_sk = customer.c_customer_sk
JOIN customer_address ON customer.c_current_addr_sk = customer_address.ca_address_sk
JOIN date_dim ON web_sales.ws_sold_date_sk = date_dim.d_date_sk
where
d_qoy = 2 and d_year = 2000
group by ca_zip, ca_county
order by ca_zip, ca_county
limit 100
But the correct join order wasn't generated because CE for item x item + filter has a selectivity of 1.
2014-10-20 18:46:27,120 DEBUG [main]: parse.SemanticAnalyzer (SemanticAnalyzer.java:apply(12330)) - HiveSortRel(fetch=[100]): rowcount = 1.6595391288544238E7, cumulative cost = {2.8364280421639153E10 rows, 3.3190782577088475E7 cpu, 0.0 io}, id = 1291
HiveSortRel(sort0=[$0], sort1=[$1], dir0=[ASC], dir1=[ASC]): rowcount = 1.6595391288544238E7, cumulative cost = {2.505357243157397E10 rows, 1.6595391288544238E7 cpu, 0.0 io}, id = 1289
HiveProjectRel(ca_zip=[$0], ca_county=[$1], _o__c2=[$2]): rowcount = 1.6595391288544238E7, cumulative cost = {2.174286444150879E10 rows, 0.0 cpu, 0.0 io}, id = 1287
HiveAggregateRel(group=[{0, 1}], agg#0=[sum($2)]): rowcount = 1.6595391288544238E7, cumulative cost = {2.174286444150879E10 rows, 0.0 cpu, 0.0 io}, id = 1285
HiveProjectRel($f0=[$9], $f1=[$8], $f2=[$2]): rowcount = 6.019767031014723E7, cumulative cost = {2.174286444150879E10 rows, 0.0 cpu, 0.0 io}, id = 1283
HiveProjectRel(ws_item_sk=[$5], ws_bill_customer_sk=[$6], ws_sales_price=[$7], ws_sold_date_sk=[$8], i_item_sk=[$12], c_customer_sk=[$0], c_current_addr_sk=[$1], ca_address_sk=[$2], ca_county=[$3], ca_zip=[$4], d_date_sk=[$9], d_year=[$10], d_qoy=[$11]): rowcount = 6.019767031014723E7, cumulative cost = {2.174286444150879E10 rows, 0.0 cpu, 0.0 io}, id = 1380
HiveJoinRel(condition=[=($6, $0)], joinType=[inner]): rowcount = 6.019767031014723E7, cumulative cost = {2.174286444150879E10 rows, 0.0 cpu, 0.0 io}, id = 1378
HiveJoinRel(condition=[=($1, $2)], joinType=[inner]): rowcount = 7.064015632843196E7, cumulative cost = {1.2E8 rows, 0.0 cpu, 0.0 io}, id = 1309
HiveProjectRel(c_customer_sk=[$0], c_current_addr_sk=[$4]): rowcount = 8.0E7, cumulative cost = {0.0 rows, 0.0 cpu, 0.0 io}, id = 1269
HiveTableScanRel(table=[[tpcds_bin_partitioned_orc_30000.customer]]): rowcount = 8.0E7, cumulative cost = {0}, id = 1035
HiveProjectRel(ca_address_sk=[$0], ca_county=[$7], ca_zip=[$9]): rowcount = 4.0E7, cumulative cost = {0.0 rows, 0.0 cpu, 0.0 io}, id = 1273
HiveTableScanRel(table=[[tpcds_bin_partitioned_orc_30000.customer_address]]): rowcount = 4.0E7, cumulative cost = {0}, id = 1032
HiveJoinRel(condition=[=($0, $7)], joinType=[inner]): rowcount = 3.856185436785714E7, cumulative cost = {2.16336624308125E10 rows, 0.0 cpu, 0.0 io}, id = 1376
HiveJoinRel(condition=[=($3, $4)], joinType=[inner]): rowcount = 3.856185436785714E7, cumulative cost = {2.159463857644464E10 rows, 0.0 cpu, 0.0 io}, id = 1316
HiveProjectRel(ws_item_sk=[$2], ws_bill_customer_sk=[$3], ws_sales_price=[$20], ws_sold_date_sk=[$33]): rowcount = 2.1594638446E10, cumulative cost = {0.0 rows, 0.0 cpu, 0.0 io}, id = 1205
HiveTableScanRel(table=[[tpcds_bin_partitioned_orc_30000.web_sales]]): rowcount = 2.1594638446E10, cumulative cost = {0}, id = 1033
HiveProjectRel(d_date_sk=[$0], d_year=[$6], d_qoy=[$10]): rowcount = 130.44464285714287, cumulative cost = {0.0 rows, 0.0 cpu, 0.0 io}, id = 1279
HiveFilterRel(condition=[AND(=($10, 2), =($6, 2000))]): rowcount = 130.44464285714287, cumulative cost = {0.0 rows, 0.0 cpu, 0.0 io}, id = 1277
HiveTableScanRel(table=[[tpcds_bin_partitioned_orc_30000.date_dim]]): rowcount = 73049.0, cumulative cost = {0}, id = 1034
HiveProjectRel(i_item_sk=[$0]): rowcount = 462000.0, cumulative cost = {1.0 rows, 1.0 cpu, 0.0 io}, id = 1265
HiveFilterRel(condition=[=(1, 1)]): rowcount = 462000.0, cumulative cost = {1.0 rows, 1.0 cpu, 0.0 io}, id = 1263
SemiJoinRel(condition=[=($1, $2)], joinType=[inner]): rowcount = 462000.0, cumulative cost = {1.0 rows, 1.0 cpu, 0.0 io}, id = 1261
HiveProjectRel(i_item_sk=[$0], i_item_id=[$1]): rowcount = 462000.0, cumulative cost = {0.0 rows, 0.0 cpu, 0.0 io}, id = 1253
HiveTableScanRel(table=[[tpcds_bin_partitioned_orc_30000.item]]): rowcount = 462000.0, cumulative cost = {0}, id = 1024
HiveProjectRel(i_item_id=[$1]): rowcount = 1.05119214745814, cumulative cost = {0.0 rows, 0.0 cpu, 0.0 io}, id = 1259
HiveProjectRel(i_item_sk=[$0], i_item_id=[$1]): rowcount = 1.05119214745814, cumulative cost = {0.0 rows, 0.0 cpu, 0.0 io}, id = 1257
HiveFilterRel(condition=[in($0, 2, 3, 5, 7, 11, 13, 17, 19, 23, 29)]): rowcount = 1.05119214745814, cumulative cost = {0.0 rows, 0.0 cpu, 0.0 io}, id = 1255
HiveTableScanRel(table=[[tpcds_bin_partitioned_orc_30000.item]]): rowcount = 462000.0, cumulative cost = {0}, id = 1024
This query generates the correct join order
with items as (select i_item_sk from
item where
item.i_item_id in (select i_item_id
from item i2
where i2.i_item_sk in (2, 3, 5, 7, 11, 13, 17, 19, 23, 29)
)
),
ws as (
select ws_bill_customer_sk,ws_sales_price,ws_sold_date_sk
from web_sales
JOIN items ON web_sales.ws_item_sk = items.i_item_sk
)
select ca_zip, ca_county, sum(ws_sales_price)
from ws
JOIN customer ON ws.ws_bill_customer_sk = customer.c_customer_sk
JOIN customer_address ON customer.c_current_addr_sk = customer_address.ca_address_sk
JOIN date_dim ON ws.ws_sold_date_sk = date_dim.d_date_sk
where d_qoy = 2 and d_year = 2000
group by ca_zip, ca_county
order by ca_zip, ca_county
limit 100
Plan
2014-10-20 19:13:15,989 DEBUG [main]: parse.SemanticAnalyzer (SemanticAnalyzer.java:apply(12330)) - HiveSortRel(fetch=[100]): rowcount = 1.6595391288544238E7, cumulative cost = {4.99203570142713E10 rows, 3.3190783577088475E7 cpu, 0.0 io}, id = 4367
HiveSortRel(sort0=[$0], sort1=[$1], dir0=[ASC], dir1=[ASC]): rowcount = 1.6595391288544238E7, cumulative cost = {4.6609649024206116E10 rows, 1.6595392288544238E7 cpu, 0.0 io}, id = 4365
HiveProjectRel(ca_zip=[$0], ca_county=[$1], _o__c2=[$2]): rowcount = 1.6595391288544238E7, cumulative cost = {4.329894103414093E10 rows, 1.0 cpu, 0.0 io}, id = 4363
HiveAggregateRel(group=[{0, 1}], agg#0=[sum($2)]): rowcount = 1.6595391288544238E7, cumulative cost = {4.329894103414093E10 rows, 1.0 cpu, 0.0 io}, id = 4361
HiveProjectRel($f0=[$7], $f1=[$6], $f2=[$1]): rowcount = 6.019767031014723E7, cumulative cost = {4.329894103414093E10 rows, 1.0 cpu, 0.0 io}, id = 4359
HiveProjectRel(ws_bill_customer_sk=[$5], ws_sales_price=[$6], ws_sold_date_sk=[$7], c_customer_sk=[$0], c_current_addr_sk=[$1], ca_address_sk=[$2], ca_county=[$3], ca_zip=[$4], d_date_sk=[$8], d_year=[$9], d_qoy=[$10]): rowcount = 6.019767031014723E7, cumulative cost = {4.329894103414093E10 rows, 1.0 cpu, 0.0 io}, id = 4426
HiveJoinRel(condition=[=($5, $0)], joinType=[inner]): rowcount = 6.019767031014723E7, cumulative cost = {4.329894103414093E10 rows, 1.0 cpu, 0.0 io}, id = 4424
HiveJoinRel(condition=[=($1, $2)], joinType=[inner]): rowcount = 7.064015632843196E7, cumulative cost = {1.2E8 rows, 0.0 cpu, 0.0 io}, id = 4392
HiveProjectRel(c_customer_sk=[$0], c_current_addr_sk=[$4]): rowcount = 8.0E7, cumulative cost = {0.0 rows, 0.0 cpu, 0.0 io}, id = 4345
HiveTableScanRel(table=[[tpcds_bin_partitioned_orc_30000.customer]]): rowcount = 8.0E7, cumulative cost = {0}, id = 4101
HiveProjectRel(ca_address_sk=[$0], ca_county=[$7], ca_zip=[$9]): rowcount = 4.0E7, cumulative cost = {0.0 rows, 0.0 cpu, 0.0 io}, id = 4349
HiveTableScanRel(table=[[tpcds_bin_partitioned_orc_30000.customer_address]]): rowcount = 4.0E7, cumulative cost = {0}, id = 4099
HiveJoinRel(condition=[=($2, $3)], joinType=[inner]): rowcount = 3.856185436785714E7, cumulative cost = {4.318973902344464E10 rows, 1.0 cpu, 0.0 io}, id = 4395
HiveProjectRel(ws_bill_customer_sk=[$1], ws_sales_price=[$2], ws_sold_date_sk=[$3]): rowcount = 2.1594638446E10, cumulative cost = {2.1595100447E10 rows, 1.0 cpu, 0.0 io}, id = 4343
HiveProjectRel(ws_item_sk=[$0], ws_bill_customer_sk=[$1], ws_sales_price=[$2], ws_sold_date_sk=[$3], i_item_sk=[$4]): rowcount = 2.1594638446E10, cumulative cost = {2.1595100447E10 rows, 1.0 cpu, 0.0 io}, id = 4388
HiveJoinRel(condition=[=($0, $4)], joinType=[inner]): rowcount = 2.1594638446E10, cumulative cost = {2.1595100447E10 rows, 1.0 cpu, 0.0 io}, id = 4383
HiveProjectRel(ws_item_sk=[$2], ws_bill_customer_sk=[$3], ws_sales_price=[$20], ws_sold_date_sk=[$33]): rowcount = 2.1594638446E10, cumulative cost = {0.0 rows, 0.0 cpu, 0.0 io}, id = 4277
HiveTableScanRel(table=[[tpcds_bin_partitioned_orc_30000.web_sales]]): rowcount = 2.1594638446E10, cumulative cost = {0}, id = 4096
HiveProjectRel(i_item_sk=[$0]): rowcount = 462000.0, cumulative cost = {1.0 rows, 1.0 cpu, 0.0 io}, id = 4339
HiveFilterRel(condition=[=(1, 1)]): rowcount = 462000.0, cumulative cost = {1.0 rows, 1.0 cpu, 0.0 io}, id = 4337
SemiJoinRel(condition=[=($1, $2)], joinType=[inner]): rowcount = 462000.0, cumulative cost = {1.0 rows, 1.0 cpu, 0.0 io}, id = 4335
HiveProjectRel(i_item_sk=[$0], i_item_id=[$1]): rowcount = 462000.0, cumulative cost = {0.0 rows, 0.0 cpu, 0.0 io}, id = 4327
HiveTableScanRel(table=[[tpcds_bin_partitioned_orc_30000.item]]): rowcount = 462000.0, cumulative cost = {0}, id = 4088
HiveProjectRel(i_item_id=[$1]): rowcount = 1.05119214745814, cumulative cost = {0.0 rows, 0.0 cpu, 0.0 io}, id = 4333
HiveProjectRel(i_item_sk=[$0], i_item_id=[$1]): rowcount = 1.05119214745814, cumulative cost = {0.0 rows, 0.0 cpu, 0.0 io}, id = 4331
HiveFilterRel(condition=[in($0, 2, 3, 5, 7, 11, 13, 17, 19, 23, 29)]): rowcount = 1.05119214745814, cumulative cost = {0.0 rows, 0.0 cpu, 0.0 io}, id = 4329
HiveTableScanRel(table=[[tpcds_bin_partitioned_orc_30000.item]]): rowcount = 462000.0, cumulative cost = {0}, id = 4088
HiveProjectRel(d_date_sk=[$0], d_year=[$6], d_qoy=[$10]): rowcount = 130.44464285714287, cumulative cost = {0.0 rows, 0.0 cpu, 0.0 io}, id = 4355
HiveFilterRel(condition=[AND(=($10, 2), =($6, 2000))]): rowcount = 130.44464285714287, cumulative cost = {0.0 rows, 0.0 cpu, 0.0 io}, id = 4353
HiveTableScanRel(table=[[tpcds_bin_partitioned_orc_30000.date_dim]]): rowcount = 73049.0, cumulative cost = {0}, id = 4100
Attachments
Attachments
Issue Links
- is duplicated by
-
HIVE-8653 CBO: Push Semi Join through, Project/Filter/Join
-
- Closed
-