wangyum commented on pull request #28741:
URL: https://github.com/apache/spark/pull/28741#issuecomment-640136037
It seems this solution cannot be fully optimized.
PostgreSQL:
```
Aggregate (cost=77.33..77.34 rows=1 width=128)
-> Nested Loop (cost=0.00..77.31 rows=1 width=32)
Join Filter: (store_sales.ss_sold_date_sk = date_dim.d_date_sk)
-> Nested Loop (cost=0.00..67.18 rows=1 width=36)
Join Filter: ((store_sales.ss_addr_sk =
customer_address.ca_address_sk) AND ((((customer_address.ca_state)::text = ANY
('{TX,OH,TX}'::text[])) AND (store_sales.ss_net_profit >=
'100'::numeric) AND (store_sales.ss_net_profit <= '200'::numeric)) OR
(((customer_address.ca_state)::text = ANY ('{OR,NM,KY}'::text[])) AND
(store_sales.ss_net_profit >= '150'::numeric) AND (s
tore_sales.ss_net_profit <= '300'::numeric)) OR
(((customer_address.ca_state)::text = ANY ('{VA,TX,MS}'::text[])) AND
(store_sales.ss_net_profit >= '50'::numeric) AND (store_sales.ss_net_profi
t <= '250'::numeric))))
-> Nested Loop (cost=0.00..56.90 rows=1 width=54)
Join Filter: ((store_sales.ss_cdemo_sk =
customer_demographics.cd_demo_sk) AND
((((customer_demographics.cd_marital_status)::text = 'M'::text) AND
((customer_demographics.
cd_education_status)::text = 'Advanced Degree'::text) AND
(store_sales.ss_sales_price >= 100.00) AND (store_sales.ss_sales_price <=
150.00) AND (household_demographics.hd_dep_count = 3)) OR ((
(customer_demographics.cd_marital_status)::text = 'S'::text) AND
((customer_demographics.cd_education_status)::text = 'College'::text) AND
(store_sales.ss_sales_price >= 50.00) AND (store_sale
s.ss_sales_price <= 100.00) AND (household_demographics.hd_dep_count = 1))
OR (((customer_demographics.cd_marital_status)::text = 'W'::text) AND
((customer_demographics.cd_education_status)::t
ext = '2 yr Degree'::text) AND (store_sales.ss_sales_price >= 150.00) AND
(store_sales.ss_sales_price <= 200.00) AND (household_demographics.hd_dep_count
= 1))))
-> Nested Loop (cost=0.00..46.10 rows=1 width=76)
Join Filter: (store_sales.ss_hdemo_sk =
household_demographics.hd_demo_sk)
-> Nested Loop (cost=0.00..33.61 rows=1
width=76)
Join Filter: (store_sales.ss_store_sk =
store.s_store_sk)
-> Seq Scan on store_sales
(cost=0.00..23.60 rows=1 width=80)
Filter: ((((ss_sales_price >= 100.00)
AND (ss_sales_price <= 150.00)) OR ((ss_sales_price >= 50.00) AND
(ss_sales_price <= 100.00)) OR ((ss_sales_price >
= 150.00) AND (ss_sales_price <= 200.00))) AND (((ss_net_profit >=
'100'::numeric) AND (ss_net_profit <= '200'::numeric)) OR ((ss_net_profit >=
'150'::numeric) AND (ss_net_profit <= '300'::num
eric)) OR ((ss_net_profit >= '50'::numeric) AND (ss_net_profit <=
'250'::numeric))))
-> Seq Scan on store (cost=0.00..10.00
rows=1 width=4)
-> Seq Scan on household_demographics
(cost=0.00..12.45 rows=3 width=8)
Filter: ((hd_dep_count = 3) OR
(hd_dep_count = 1) OR (hd_dep_count = 1))
-> Seq Scan on customer_demographics
(cost=0.00..10.75 rows=1 width=1036)
Filter: ((((cd_marital_status)::text = 'M'::text)
AND ((cd_education_status)::text = 'Advanced Degree'::text)) OR
(((cd_marital_status)::text = 'S'::text) AND ((cd_e
ducation_status)::text = 'College'::text)) OR (((cd_marital_status)::text =
'W'::text) AND ((cd_education_status)::text = '2 yr Degree'::text)))
-> Seq Scan on customer_address (cost=0.00..10.24 rows=1
width=520)
Filter: (((ca_country)::text = 'United States'::text)
AND (((ca_state)::text = ANY ('{TX,OH,TX}'::text[])) OR ((ca_state)::text = ANY
('{OR,NM,KY}'::text[])) OR ((ca_state
)::text = ANY ('{VA,TX,MS}'::text[]))))
-> Seq Scan on date_dim (cost=0.00..10.12 rows=1 width=4)
Filter: (d_year = 2001)
(22 rows)
```
After this PR(`set spark.sql.constraintPropagation.enabled=false` to ignore
infer `IsNotNull`):
```
*(7) HashAggregate(keys=[], functions=[avg(cast(ss_quantity#10 as bigint)),
avg(UnscaledValue(ss_ext_sales_price#15)),
avg(UnscaledValue(ss_ext_wholesale_cost#16)),
sum(UnscaledValue(ss_ext_wholesale_cost#16))])
+- Exchange SinglePartition, true, [id=#252]
+- *(6) HashAggregate(keys=[], functions=[partial_avg(cast(ss_quantity#10
as bigint)), partial_avg(UnscaledValue(ss_ext_sales_price#15)),
partial_avg(UnscaledValue(ss_ext_wholesale_cost#16)),
partial_sum(UnscaledValue(ss_ext_wholesale_cost#16))])
+- *(6) Project [ss_quantity#10, ss_ext_sales_price#15,
ss_ext_wholesale_cost#16]
+- *(6) BroadcastHashJoin [ss_hdemo_sk#5], [hd_demo_sk#61], Inner,
BuildRight, (((((((cd_marital_status#54 = M) AND (cd_education_status#55 =
Advanced Degree)) AND (ss_sales_price#13 >= 100.00)) AND (ss_sales_price#13 <=
150.00)) AND (hd_dep_count#64 = 3)) OR (((((cd_marital_status#54 = S) AND
(cd_education_status#55 = College)) AND (ss_sales_price#13 >= 50.00)) AND
(ss_sales_price#13 <= 100.00)) AND (hd_dep_count#64 = 1))) OR
(((((cd_marital_status#54 = W) AND (cd_education_status#55 = 2 yr Degree)) AND
(ss_sales_price#13 >= 150.00)) AND (ss_sales_price#13 <= 200.00)) AND
(hd_dep_count#64 = 1)))
:- *(6) Project [ss_hdemo_sk#5, ss_quantity#10,
ss_sales_price#13, ss_ext_sales_price#15, ss_ext_wholesale_cost#16,
cd_marital_status#54, cd_education_status#55]
: +- *(6) BroadcastHashJoin [ss_cdemo_sk#4], [cd_demo_sk#52],
Inner, BuildRight, ((((((cd_marital_status#54 = M) AND (cd_education_status#55
= Advanced Degree)) AND (ss_sales_price#13 >= 100.00)) AND (ss_sales_price#13
<= 150.00)) OR ((((cd_marital_status#54 = S) AND (cd_education_status#55 =
College)) AND (ss_sales_price#13 >= 50.00)) AND (ss_sales_price#13 <= 100.00)))
OR ((((cd_marital_status#54 = W) AND (cd_education_status#55 = 2 yr Degree))
AND (ss_sales_price#13 >= 150.00)) AND (ss_sales_price#13 <= 200.00)))
: :- *(6) Project [ss_cdemo_sk#4, ss_hdemo_sk#5,
ss_quantity#10, ss_sales_price#13, ss_ext_sales_price#15,
ss_ext_wholesale_cost#16]
: : +- *(6) BroadcastHashJoin [ss_sold_date_sk#0],
[d_date_sk#79], Inner, BuildRight
: : :- *(6) Project [ss_sold_date_sk#0, ss_cdemo_sk#4,
ss_hdemo_sk#5, ss_quantity#10, ss_sales_price#13, ss_ext_sales_price#15,
ss_ext_wholesale_cost#16]
: : : +- *(6) BroadcastHashJoin [ss_addr_sk#6],
[ca_address_sk#66], Inner, BuildRight, ((((ca_state#74 IN (TX,OH) AND
(ss_net_profit#22 >= 100.00)) AND (ss_net_profit#22 <= 200.00)) OR
((ca_state#74 IN (OR,NM,KY) AND (ss_net_profit#22 >= 150.00)) AND
(ss_net_profit#22 <= 300.00))) OR ((ca_state#74 IN (VA,TX,MS) AND
(ss_net_profit#22 >= 50.00)) AND (ss_net_profit#22 <= 250.00)))
: : : :- *(6) Project [ss_sold_date_sk#0,
ss_cdemo_sk#4, ss_hdemo_sk#5, ss_addr_sk#6, ss_quantity#10, ss_sales_price#13,
ss_ext_sales_price#15, ss_ext_wholesale_cost#16, ss_net_profit#22]
: : : : +- *(6) BroadcastHashJoin [ss_store_sk#7],
[s_store_sk#23], Inner, BuildRight
: : : : :- *(6) Filter ((((ss_net_profit#22 >=
100.00) AND (ss_net_profit#22 <= 200.00)) OR ((ss_net_profit#22 >= 150.00) AND
(ss_net_profit#22 <= 300.00))) OR ((ss_net_profit#22 >= 50.00) AND
(ss_net_profit#22 <= 250.00)))
: : : : : +- *(6) ColumnarToRow
: : : : : +- FileScan parquet
default.store_sales[ss_sold_date_sk#0,ss_cdemo_sk#4,ss_hdemo_sk#5,ss_addr_sk#6,ss_store_sk#7,ss_quantity#10,ss_sales_price#13,ss_ext_sales_price#15,ss_ext_wholesale_cost#16,ss_net_profit#22]
Batched: true, DataFilters: [((((ss_net_profit#22 >= 100.00) AND
(ss_net_profit#22 <= 200.00)) OR ((ss_net_profit#22 >= 150.0..., Format:
Parquet, Location:
InMemoryFileIndex[file:/Users/yumwang/opensource/spark/sql/core/spark-warehouse/org.apache.spark....,
PartitionFilters: [], PushedFilters:
[Or(Or(And(GreaterThanOrEqual(ss_net_profit,100.00),LessThanOrEqual(ss_net_profit,200.00)),And(Gr...,
ReadSchema:
struct<ss_sold_date_sk:int,ss_cdemo_sk:int,ss_hdemo_sk:int,ss_addr_sk:int,ss_store_sk:int,ss_quan...
: : : : +- BroadcastExchange
HashedRelationBroadcastMode(List(cast(input[0, int, true] as bigint))),
[id=#213]
: : : : +- *(1) ColumnarToRow
: : : : +- FileScan parquet
default.store[s_store_sk#23] Batched: true, DataFilters: [], Format: Parquet,
Location:
InMemoryFileIndex[file:/Users/yumwang/opensource/spark/sql/core/spark-warehouse/org.apache.spark....,
PartitionFilters: [], PushedFilters: [], ReadSchema: struct<s_store_sk:int>
: : : +- BroadcastExchange
HashedRelationBroadcastMode(List(cast(input[0, int, true] as bigint))),
[id=#222]
: : : +- *(2) Project [ca_address_sk#66,
ca_state#74]
: : : +- *(2) Filter ((ca_country#76 = United
States) AND ((ca_state#74 IN (TX,OH) OR ca_state#74 IN (OR,NM,KY)) OR
ca_state#74 IN (VA,TX,MS)))
: : : +- *(2) ColumnarToRow
: : : +- FileScan parquet
default.customer_address[ca_address_sk#66,ca_state#74,ca_country#76] Batched:
true, DataFilters: [(ca_country#76 = United States), ((ca_state#74 IN (TX,OH)
OR ca_state#74 IN (OR,NM,KY)) OR ca_st..., Format: Parquet, Location:
InMemoryFileIndex[file:/Users/yumwang/opensource/spark/sql/core/spark-warehouse/org.apache.spark....,
PartitionFilters: [], PushedFilters: [EqualTo(ca_country,United States),
Or(Or(In(ca_state, [TX,OH]),In(ca_state, [OR,NM,KY])),In(ca_s..., ReadSchema:
struct<ca_address_sk:int,ca_state:string,ca_country:string>
: : +- BroadcastExchange
HashedRelationBroadcastMode(List(cast(input[0, int, true] as bigint))),
[id=#231]
: : +- *(3) Project [d_date_sk#79]
: : +- *(3) Filter (d_year#85 = 2001)
: : +- *(3) ColumnarToRow
: : +- FileScan parquet
default.date_dim[d_date_sk#79,d_year#85] Batched: true, DataFilters:
[(d_year#85 = 2001)], Format: Parquet, Location:
InMemoryFileIndex[file:/Users/yumwang/opensource/spark/sql/core/spark-warehouse/org.apache.spark....,
PartitionFilters: [], PushedFilters: [EqualTo(d_year,2001)], ReadSchema:
struct<d_date_sk:int,d_year:int>
: +- BroadcastExchange
HashedRelationBroadcastMode(List(cast(input[0, int, true] as bigint))),
[id=#238]
: +- *(4) ColumnarToRow
: +- FileScan parquet
default.customer_demographics[cd_demo_sk#52,cd_marital_status#54,cd_education_status#55]
Batched: true, DataFilters: [], Format: Parquet, Location:
InMemoryFileIndex[file:/Users/yumwang/opensource/spark/sql/core/spark-warehouse/org.apache.spark....,
PartitionFilters: [], PushedFilters: [], ReadSchema:
struct<cd_demo_sk:int,cd_marital_status:string,cd_education_status:string>
+- BroadcastExchange
HashedRelationBroadcastMode(List(cast(input[0, int, true] as bigint))),
[id=#246]
+- *(5) Filter (((hd_dep_count#64 = 3) OR (hd_dep_count#64 =
1)) OR (hd_dep_count#64 = 1))
+- *(5) ColumnarToRow
+- FileScan parquet
default.household_demographics[hd_demo_sk#61,hd_dep_count#64] Batched: true,
DataFilters: [(((hd_dep_count#64 = 3) OR (hd_dep_count#64 = 1)) OR
(hd_dep_count#64 = 1))], Format: Parquet, Location:
InMemoryFileIndex[file:/Users/yumwang/opensource/spark/sql/core/spark-warehouse/org.apache.spark....,
PartitionFilters: [], PushedFilters:
[Or(Or(EqualTo(hd_dep_count,3),EqualTo(hd_dep_count,1)),EqualTo(hd_dep_count,1))],
ReadSchema: struct<hd_demo_sk:int,hd_dep_count:int>
```
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