[
https://issues.apache.org/jira/browse/HIVE-8261?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14160999#comment-14160999
]
Harish Butani commented on HIVE-8261:
-------------------------------------
[~vikram.dixit] can be add this to 0.14 branch
> CBO : Predicate pushdown is removed by Optiq
> ---------------------------------------------
>
> Key: HIVE-8261
> URL: https://issues.apache.org/jira/browse/HIVE-8261
> Project: Hive
> Issue Type: Bug
> Components: CBO
> Affects Versions: 0.14.0, 0.13.1
> Reporter: Mostafa Mokhtar
> Assignee: Harish Butani
> Fix For: 0.14.0
>
> Attachments: HIVE-8261.1.patch
>
>
> Plan for TPC-DS Q64 wasn't optimal upon looking at the logical plan I
> realized that predicate pushdown is not applied on date_dim d1.
> Interestingly before optiq we have the predicate pushed :
> {code}
> HiveFilterRel(condition=[<=($5, $1)])
> HiveJoinRel(condition=[=($3, $6)], joinType=[inner])
> HiveProjectRel(_o__col0=[$0], _o__col1=[$2], _o__col2=[$3],
> _o__col3=[$1])
> HiveFilterRel(condition=[=($0, 2000)])
> HiveAggregateRel(group=[{0, 1}], agg#0=[count()], agg#1=[sum($2)])
> HiveProjectRel($f0=[$4], $f1=[$5], $f2=[$2])
> HiveJoinRel(condition=[=($1, $8)], joinType=[inner])
> HiveJoinRel(condition=[=($1, $5)], joinType=[inner])
> HiveJoinRel(condition=[=($0, $3)], joinType=[inner])
> HiveProjectRel(ss_sold_date_sk=[$0], ss_item_sk=[$2],
> ss_wholesale_cost=[$11])
>
> HiveTableScanRel(table=[[tpcds_bin_partitioned_orc_200.store_sales]])
> HiveProjectRel(d_date_sk=[$0], d_year=[$6])
>
> HiveTableScanRel(table=[[tpcds_bin_partitioned_orc_200.date_dim]])
> HiveFilterRel(condition=[AND(in($2, 'maroon', 'burnished',
> 'dim', 'steel', 'navajo', 'chocolate'), between(false, $1, 35, +(35, 10)),
> between(false, $1, +(35, 1), +(35, 15)))])
> HiveProjectRel(i_item_sk=[$0], i_current_price=[$5],
> i_color=[$17])
>
> HiveTableScanRel(table=[[tpcds_bin_partitioned_orc_200.item]])
> HiveProjectRel(_o__col0=[$0])
> HiveAggregateRel(group=[{0}])
> HiveProjectRel($f0=[$0])
> HiveJoinRel(condition=[AND(=($0, $2), =($1, $3))],
> joinType=[inner])
> HiveProjectRel(cs_item_sk=[$15],
> cs_order_number=[$17])
>
> HiveTableScanRel(table=[[tpcds_bin_partitioned_orc_200.catalog_sales]])
> HiveProjectRel(cr_item_sk=[$2], cr_order_number=[$16])
>
> HiveTableScanRel(table=[[tpcds_bin_partitioned_orc_200.catalog_returns]])
> HiveProjectRel(_o__col0=[$0], _o__col1=[$2], _o__col3=[$1])
> HiveFilterRel(condition=[=($0, +(2000, 1))])
> HiveAggregateRel(group=[{0, 1}], agg#0=[count()])
> HiveProjectRel($f0=[$4], $f1=[$5], $f2=[$2])
> HiveJoinRel(condition=[=($1, $8)], joinType=[inner])
> HiveJoinRel(condition=[=($1, $5)], joinType=[inner])
> HiveJoinRel(condition=[=($0, $3)], joinType=[inner])
> HiveProjectRel(ss_sold_date_sk=[$0], ss_item_sk=[$2],
> ss_wholesale_cost=[$11])
>
> HiveTableScanRel(table=[[tpcds_bin_partitioned_orc_200.store_sales]])
> HiveProjectRel(d_date_sk=[$0], d_year=[$6])
>
> HiveTableScanRel(table=[[tpcds_bin_partitioned_orc_200.date_dim]])
> HiveFilterRel(condition=[AND(in($2, 'maroon', 'burnished',
> 'dim', 'steel', 'navajo', 'chocolate'), between(false, $1, 35, +(35, 10)),
> between(false, $1, +(35, 1), +(35, 15)))])
> HiveProjectRel(i_item_sk=[$0], i_current_price=[$5],
> i_color=[$17])
>
> HiveTableScanRel(table=[[tpcds_bin_partitioned_orc_200.item]])
> HiveProjectRel(_o__col0=[$0])
> HiveAggregateRel(group=[{0}])
> HiveProjectRel($f0=[$0])
> HiveJoinRel(condition=[AND(=($0, $2), =($1, $3))],
> joinType=[inner])
> HiveProjectRel(cs_item_sk=[$15],
> cs_order_number=[$17])
>
> HiveTableScanRel(table=[[tpcds_bin_partitioned_orc_200.catalog_sales]])
> HiveProjectRel(cr_item_sk=[$2], cr_order_number=[$16])
>
> HiveTableScanRel(table=[[tpcds_bin_partitioned_orc_200.catalog_returns]])
> {code}
> While after Optiq the filter on date_dim gets pulled up the plan
> {code}
> HiveFilterRel(condition=[<=($5, $1)]): rowcount = 1.0, cumulative cost =
> {5.50188454E8 rows, 0.0 cpu, 0.0 io}, id = 6895
> HiveProjectRel(_o__col0=[$0], _o__col1=[$1], _o__col2=[$2],
> _o__col3=[$3], _o__col00=[$4], _o__col10=[$5], _o__col30=[$6]): rowcount =
> 1.0, cumulative cost = {5.50188454E8 rows, 0.0 cpu, 0.0 io}, id = 7046
> HiveJoinRel(condition=[=($3, $6)], joinType=[inner]): rowcount = 1.0,
> cumulative cost = {5.50188454E8 rows, 0.0 cpu, 0.0 io}, id = 7041
> HiveProjectRel(_o__col0=[$0], _o__col1=[$2], _o__col2=[$3],
> _o__col3=[$1]): rowcount = 1.0, cumulative cost = {5.50188452E8 rows, 0.0
> cpu, 0.0 io}, id = 6857
> HiveFilterRel(condition=[=($0, 2000)]): rowcount = 1.0, cumulative
> cost = {5.50188452E8 rows, 0.0 cpu, 0.0 io}, id = 6855
> HiveAggregateRel(group=[{0, 1}], agg#0=[count()],
> agg#1=[sum($2)]): rowcount = 1.0, cumulative cost = {5.50188452E8 rows, 0.0
> cpu, 0.0 io}, id = 6853
> HiveProjectRel($f0=[$4], $f1=[$5], $f2=[$2]): rowcount = 1.0,
> cumulative cost = {5.50188452E8 rows, 0.0 cpu, 0.0 io}, id = 6851
> HiveProjectRel(ss_sold_date_sk=[$3], ss_item_sk=[$4],
> ss_wholesale_cost=[$5], d_date_sk=[$0], d_year=[$1], i_item_sk=[$6],
> i_current_price=[$7], i_color=[$8], _o__col0=[$2]): rowcount = 1.0,
> cumulative cost = {5.50188452E8 rows, 0.0 cpu, 0.0 io}, id = 7039
> HiveJoinRel(condition=[=($3, $0)], joinType=[inner]):
> rowcount = 1.0, cumulative cost = {5.50188452E8 rows, 0.0 cpu, 0.0 io}, id =
> 7037
> HiveProjectRel(d_date_sk=[$0], d_year=[$6]): rowcount =
> 73049.0, cumulative cost = {0.0 rows, 0.0 cpu, 0.0 io}, id = 6861
>
> HiveTableScanRel(table=[[tpcds_bin_partitioned_orc_200.date_dim]]): rowcount
> = 73049.0, cumulative cost = {0}, id = 6537
> HiveJoinRel(condition=[=($2, $0)], joinType=[inner]):
> rowcount = 1.0, cumulative cost = {5.50115402E8 rows, 0.0 cpu, 0.0 io}, id =
> 7035
> HiveProjectRel(_o__col0=[$0]): rowcount = 38846.0,
> cumulative cost = {3.15348608E8 rows, 0.0 cpu, 0.0 io}, id = 6847
> HiveAggregateRel(group=[{0}]): rowcount = 38846.0,
> cumulative cost = {3.15348608E8 rows, 0.0 cpu, 0.0 io}, id = 6845
> HiveProjectRel($f0=[$0]): rowcount =
> 6.692553251460564E8, cumulative cost = {3.15348608E8 rows, 0.0 cpu, 0.0 io},
> id = 6843
> HiveProjectRel(cs_item_sk=[$0],
> cs_order_number=[$1], cr_item_sk=[$2], cr_order_number=[$3]): rowcount =
> 6.692553251460564E8, cumulative cost = {3.15348608E8 rows, 0.0 cpu, 0.0 io},
> id = 6945
> HiveJoinRel(condition=[AND(=($0, $2), =($1,
> $3))], joinType=[inner]): rowcount = 6.692553251460564E8, cumulative cost =
> {3.15348608E8 rows, 0.0 cpu, 0.0 io}, id = 6940
> HiveProjectRel(cs_item_sk=[$15],
> cs_order_number=[$17]): rowcount = 2.86549727E8, cumulative cost = {0.0 rows,
> 0.0 cpu, 0.0 io}, id = 6871
>
> HiveTableScanRel(table=[[tpcds_bin_partitioned_orc_200.catalog_sales]]):
> rowcount = 2.86549727E8, cumulative cost = {0}, id = 6531
> HiveProjectRel(cr_item_sk=[$2],
> cr_order_number=[$16]): rowcount = 2.8798881E7, cumulative cost = {0.0 rows,
> 0.0 cpu, 0.0 io}, id = 6873
>
> HiveTableScanRel(table=[[tpcds_bin_partitioned_orc_200.catalog_returns]]):
> rowcount = 2.8798881E7, cumulative cost = {0}, id = 6532
> HiveJoinRel(condition=[=($1, $3)], joinType=[inner]):
> rowcount = 1.0, cumulative cost = {5.50076555E8 rows, 0.0 cpu, 0.0 io}, id =
> 6996
> HiveProjectRel(ss_sold_date_sk=[$0], ss_item_sk=[$2],
> ss_wholesale_cost=[$11]): rowcount = 5.50076554E8, cumulative cost = {0.0
> rows, 0.0 cpu, 0.0 io}, id = 6859
>
> HiveTableScanRel(table=[[tpcds_bin_partitioned_orc_200.store_sales]]):
> rowcount = 5.50076554E8, cumulative cost = {0}, id = 6538
> HiveFilterRel(condition=[AND(in($2, 'maroon',
> 'burnished', 'dim', 'steel', 'navajo', 'chocolate'), between(false, $1, 35,
> +(35, 10)), between(false, $1, +(35, 1), +(35, 15)))]): rowcount = 1.0,
> cumulative cost = {0.0 rows, 0.0 cpu, 0.0 io}, id = 6833
> HiveProjectRel(i_item_sk=[$0],
> i_current_price=[$5], i_color=[$17]): rowcount = 48000.0, cumulative cost =
> {0.0 rows, 0.0 cpu, 0.0 io}, id = 6831
>
> HiveTableScanRel(table=[[tpcds_bin_partitioned_orc_200.item]]): rowcount =
> 48000.0, cumulative cost = {0}, id = 6539
> HiveProjectRel(_o__col0=[$0], _o__col1=[$2], _o__col3=[$1]): rowcount
> = 1.0, cumulative cost = {5.50188452E8 rows, 0.0 cpu, 0.0 io}, id = 6891
> HiveFilterRel(condition=[=($0, +(2000, 1))]): rowcount = 1.0,
> cumulative cost = {5.50188452E8 rows, 0.0 cpu, 0.0 io}, id = 6889
> HiveAggregateRel(group=[{0, 1}], agg#0=[count()]): rowcount =
> 1.0, cumulative cost = {5.50188452E8 rows, 0.0 cpu, 0.0 io}, id = 6887
> HiveProjectRel($f0=[$4], $f1=[$5], $f2=[$2]): rowcount = 1.0,
> cumulative cost = {5.50188452E8 rows, 0.0 cpu, 0.0 io}, id = 6885
> HiveProjectRel(ss_sold_date_sk=[$3], ss_item_sk=[$4],
> ss_wholesale_cost=[$5], d_date_sk=[$0], d_year=[$1], i_item_sk=[$6],
> i_current_price=[$7], i_color=[$8], _o__col0=[$2]): rowcount = 1.0,
> cumulative cost = {5.50188452E8 rows, 0.0 cpu, 0.0 io}, id = 6992
> HiveJoinRel(condition=[=($3, $0)], joinType=[inner]):
> rowcount = 1.0, cumulative cost = {5.50188452E8 rows, 0.0 cpu, 0.0 io}, id =
> 6990
> HiveProjectRel(d_date_sk=[$0], d_year=[$6]): rowcount =
> 73049.0, cumulative cost = {0.0 rows, 0.0 cpu, 0.0 io}, id = 6861
>
> HiveTableScanRel(table=[[tpcds_bin_partitioned_orc_200.date_dim]]): rowcount
> = 73049.0, cumulative cost = {0}, id = 6537
> HiveJoinRel(condition=[=($2, $0)], joinType=[inner]):
> rowcount = 1.0, cumulative cost = {5.50115402E8 rows, 0.0 cpu, 0.0 io}, id =
> 6988
> HiveProjectRel(_o__col0=[$0]): rowcount = 38846.0,
> cumulative cost = {3.15348608E8 rows, 0.0 cpu, 0.0 io}, id = 6881
> HiveAggregateRel(group=[{0}]): rowcount = 38846.0,
> cumulative cost = {3.15348608E8 rows, 0.0 cpu, 0.0 io}, id = 6879
> HiveProjectRel($f0=[$0]): rowcount =
> 6.692553251460564E8, cumulative cost = {3.15348608E8 rows, 0.0 cpu, 0.0 io},
> id = 6877
> HiveProjectRel(cs_item_sk=[$0],
> cs_order_number=[$1], cr_item_sk=[$2], cr_order_number=[$3]): rowcount =
> 6.692553251460564E8, cumulative cost = {3.15348608E8 rows, 0.0 cpu, 0.0 io},
> id = 6938
> HiveJoinRel(condition=[AND(=($0, $2), =($1,
> $3))], joinType=[inner]): rowcount = 6.692553251460564E8, cumulative cost =
> {3.15348608E8 rows, 0.0 cpu, 0.0 io}, id = 6933
> HiveProjectRel(cs_item_sk=[$15],
> cs_order_number=[$17]): rowcount = 2.86549727E8, cumulative cost = {0.0 rows,
> 0.0 cpu, 0.0 io}, id = 6871
>
> HiveTableScanRel(table=[[tpcds_bin_partitioned_orc_200.catalog_sales]]):
> rowcount = 2.86549727E8, cumulative cost = {0}, id = 6531
> HiveProjectRel(cr_item_sk=[$2],
> cr_order_number=[$16]): rowcount = 2.8798881E7, cumulative cost = {0.0 rows,
> 0.0 cpu, 0.0 io}, id = 6873
>
> HiveTableScanRel(table=[[tpcds_bin_partitioned_orc_200.catalog_returns]]):
> rowcount = 2.8798881E7, cumulative cost = {0}, id = 6532
> HiveJoinRel(condition=[=($1, $3)], joinType=[inner]):
> rowcount = 1.0, cumulative cost = {5.50076555E8 rows, 0.0 cpu, 0.0 io}, id =
> 6949
> HiveProjectRel(ss_sold_date_sk=[$0], ss_item_sk=[$2],
> ss_wholesale_cost=[$11]): rowcount = 5.50076554E8, cumulative cost = {0.0
> rows, 0.0 cpu, 0.0 io}, id = 6859
>
> HiveTableScanRel(table=[[tpcds_bin_partitioned_orc_200.store_sales]]):
> rowcount = 5.50076554E8, cumulative cost = {0}, id = 6538
> HiveFilterRel(condition=[AND(in($2, 'maroon',
> 'burnished', 'dim', 'steel', 'navajo', 'chocolate'), between(false, $1, 35,
> +(35, 10)), between(false, $1, +(35, 1), +(35, 15)))]): rowcount = 1.0,
> cumulative cost = {0.0 rows, 0.0 cpu, 0.0 io}, id = 6867
> HiveProjectRel(i_item_sk=[$0],
> i_current_price=[$5], i_color=[$17]): rowcount = 48000.0, cumulative cost =
> {0.0 rows, 0.0 cpu, 0.0 io}, id = 6865
>
> HiveTableScanRel(table=[[tpcds_bin_partitioned_orc_200.item]]): rowcount =
> 48000.0, cumulative cost = {0}, id = 6539
> {code}
> I simplified the query a little bit while still maintaining the query
> structure
> The query :
> Note that the final join between cs1 and cs2 has a predicates "cs1.syear =
> 2000 and cs2.syear = 2000 + 1"
> {code}
> select cs1.syear ,cs1.cnt
> ,cs1.s1 ,cs2.syear ,cs2.cnt
> from
> (select d1.d_year as syear ,count(*) as cnt,sum(ss_wholesale_cost) as s1
> ,i_item_sk as item_sk
> FROM store_sales
> JOIN date_dim d1 ON store_sales.ss_sold_date_sk = d1.d_date_sk
> JOIN item ON store_sales.ss_item_sk = item.i_item_sk
> JOIN
> (select cs_item_sk
> from catalog_sales JOIN catalog_returns
> ON catalog_sales.cs_item_sk = catalog_returns.cr_item_sk
> and catalog_sales.cs_order_number = catalog_returns.cr_order_number
> group by cs_item_sk) cs_ui
> ON store_sales.ss_item_sk = cs_ui.cs_item_sk
> WHERE
> i_color in ('maroon','burnished','dim','steel','navajo','chocolate')
> and
> i_current_price between 35 and 35 + 10 and
> i_current_price between 35 + 1 and 35 + 15
> group by d1.d_year,i_item_sk
> ) cs1
> JOIN
> (select d1.d_year as syear ,count(*) as cnt,sum(ss_wholesale_cost) as s1 ,
> i_item_sk as item_sk
> FROM store_sales
> JOIN date_dim d1 ON store_sales.ss_sold_date_sk = d1.d_date_sk
> JOIN item ON store_sales.ss_item_sk = item.i_item_sk
> JOIN
> (select cs_item_sk
> from catalog_sales JOIN catalog_returns
> ON catalog_sales.cs_item_sk = catalog_returns.cr_item_sk
> and catalog_sales.cs_order_number = catalog_returns.cr_order_number
> group by cs_item_sk) cs_ui
> ON store_sales.ss_item_sk = cs_ui.cs_item_sk
> WHERE
> i_color in ('maroon','burnished','dim','steel','navajo','chocolate')
> and
> i_current_price between 35 and 35 + 10 and
> i_current_price between 35 + 1 and 35 + 15
> group by d1.d_year,i_item_sk
> ) cs2
> ON cs1.item_sk=cs2.item_sk
> where
> cs1.syear = 2000 and
> cs2.syear = 2000 + 1 and
> cs2.cnt <= cs1.cnt;
> {code}
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
