[
https://issues.apache.org/jira/browse/HIVE-8261?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Mostafa Mokhtar updated HIVE-8261:
----------------------------------
Description:
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}
was:
Currently CBO uses NDV not join selectivity in computeInnerJoinSelectivity
which results in in-accurate estimate number of rows.
I looked at the plan for TPC-DS Q17 after the latest set of changes and I am
concerned that the estimate of rows for the join of store_sales and
store_returns is so low, as you can see the estimate is 8461 rows for joining
1.2795706667449066E8 with 1.2922108035889767E7.
{code}
HiveJoinRel(condition=[AND(=($130, $3), =($129, $15))], joinType=[inner]):
rowcount = 1079.1345153548855, cumulative cost = {8.271845957931738E10 rows,
0.0 cpu, 0.0 io}, id = 517
HiveJoinRel(condition=[=($0, $38)], joinType=[inner]):
rowcount = 6.669190301841249E7, cumulative cost = {4.300510912631623E10 rows,
0.0 cpu, 0.0 io}, id = 402
HiveTableScanRel(table=[[catalog_sales]]): rowcount =
4.3005109025E10, cumulative cost = {0}, id = 2
HiveFilterRel(condition=[in($15, '2000Q1', '2000Q2',
'2000Q3')]): rowcount = 101.31622746185853, cumulative cost = {0.0 rows, 0.0
cpu, 0.0 io}, id = 181
HiveTableScanRel(table=[[d3]]): rowcount = 73049.0,
cumulative cost = {0}, id = 3
HiveJoinRel(condition=[AND(AND(=($3, $61), =($2, $60)), =($9,
$67))], joinType=[inner]): rowcount = 8461.27236667537, cumulative cost =
{8.26517592150266E10 rows, 0.0 cpu, 0.0 io}, id = 515
HiveJoinRel(condition=[=($27, $0)], joinType=[inner]):
rowcount = 1.2795706667449066E8, cumulative cost = {8.251088004031622E10 rows,
0.0 cpu, 0.0 io}, id = 417
HiveTableScanRel(table=[[store_sales]]): rowcount =
8.2510879939E10, cumulative cost = {0}, id = 5
HiveFilterRel(condition=[=($15, '2000Q1')]): rowcount =
101.31622746185853, cumulative cost = {0.0 rows, 0.0 cpu, 0.0 io}, id = 173
HiveTableScanRel(table=[[d1]]): rowcount = 73049.0,
cumulative cost = {0}, id = 0
HiveJoinRel(condition=[=($0, $24)], joinType=[inner]):
rowcount = 1.2922108035889767E7, cumulative cost = {8.332595810316228E9 rows,
0.0 cpu, 0.0 io}, id = 424
HiveTableScanRel(table=[[store_returns]]): rowcount =
8.332595709E9, cumulative cost = {0}, id = 7
HiveFilterRel(condition=[in($15, '2000Q1', '2000Q2',
'2000Q3')]): rowcount = 101.31622746185853, cumulative cost = {0.0 rows, 0.0
cpu, 0.0 io}, id = 177
HiveTableScanRel(table=[[d2]]): rowcount = 73049.0,
cumulative cost = {0}, id = 1
{code}
> CBO : Predicate pushdown is lost in 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: Gunther Hagleitner
> Fix For: 0.14.0
>
>
> 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}
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