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https://issues.apache.org/jira/browse/SPARK-50873?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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KeKe Zhu updated SPARK-50873:
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Attachment: query16-opt.PNG
> An optimization for SparkOptimizer to prune the column in subquery
> ------------------------------------------------------------------
>
> Key: SPARK-50873
> URL: https://issues.apache.org/jira/browse/SPARK-50873
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 3.5.3
> Reporter: KeKe Zhu
> Priority: Major
> Attachments: query16-opt.PNG, query16-org.PNG
>
>
> I used Spark 3.5+iceberg 1.6.1 to run TPCDS test. When doing performance
> analysis, I found that there is a potential optimization for SparkOptimizer.
> The optimiztion is about column pruning of DatasourceV2 (DSV2).
> In SparkOptimizer, the column pruning of DSV2 is executed in
> V2ScanRelationPushDown rule. However, there is a series of optimiztion rules
> after V2ScanRelationPushDown, those optimization rule may rewrite subquery
> and generate Project or Filter operator that can be used for column pruning,
> but column pruning will not be execute again, resulting in the generated
> physical plan reading the entire table instead of only reading the required
> columns.
> For example，there is the query 16 in TPCDS:
> {code:java}
> set spark.queryID=query16.tpl;
> select
> count(distinct cs_order_number) as `order count`
> ,sum(cs_ext_ship_cost) as `total shipping cost`
> ,sum(cs_net_profit) as `total net profit`
> from
> catalog_sales cs1
> ,date_dim
> ,customer_address
> ,call_center
> where
> d_date between '2002-2-01' and
> (cast('2002-2-01' as date) + interval 60 days)
> and cs1.cs_ship_date_sk = d_date_sk
> and cs1.cs_ship_addr_sk = ca_address_sk
> and ca_state = 'KS'
> and cs1.cs_call_center_sk = cc_call_center_sk
> and cc_county in ('Daviess County','Barrow County','Walker County','San
> Miguel County',
> 'Mobile County'
> )
> and exists (select *
> from catalog_sales cs2
> where cs1.cs_order_number = cs2.cs_order_number
> and cs1.cs_warehouse_sk <> cs2.cs_warehouse_sk)
> and not exists(select *
> from catalog_returns cr1
> where cs1.cs_order_number = cr1.cr_order_number)
> order by count(distinct cs_order_number)
> limit 100; {code}
> The final Optimized Plan of the query is as below picture, we can see that
> there are two talbes (catalog_sale & catalog_returns) are readed all data and
> do project，which certainly cause low performance for iceberg.
> !query16-org.PNG!
>
>
> My current solution: I write an optimiztion rule and add it to the
> SparkOptimizer, now i get the expect optimized plan and get a much better
> performance result.
>
> I want to know is there any other solution for this problem? contate me
> anytime.
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