turns-00050.parquet:33226
7661c4a0262e90264479dc2b
turn 1/1gpt-4o-2024-08-06EnglishFrance667 words
degenerate_repetitionAbsentFinal dense release
USER
You are a database expert and SQL optimizer. You will be given a SQL query and its rewritten variant. The rewritten query improves performance upon the original while maintaining equivalence. Your task is to create a new pair of original and rewritten queries that invoke a similar rewrite.
[ORIGINAL QUERY]```select ca_state,cd_gender,cd_marital_status,cd_dep_count,count(*) cnt1,avg(cd_dep_count),stddev_samp(cd_dep_count),sum(cd_dep_count),cd_dep_employed_count,count(*) cnt2,avg(cd_dep_employed_count),stddev_samp(cd_dep_employed_count),sum(cd_dep_employed_count),cd_dep_college_count,count(*) cnt3,avg(cd_dep_college_count),stddev_samp(cd_dep_college_count),sum(cd_dep_college_count) from customer c,customer_address ca,customer_demographics where c.c_current_addr_sk = ca.ca_address_sk and cd_demo_sk = c.c_current_cdemo_sk and exists (select * from store_sales,date_dim where c.c_customer_sk = ss_customer_sk and ss_sold_date_sk = d_date_sk and d_year = 1999 and d_qoy < 4) and (exists (select * from web_sales,date_dim where c.c_customer_sk = ws_bill_customer_sk and ws_sold_date_sk = d_date_sk and d_year = 1999 and d_qoy < 4) or exists (select * from catalog_sales,date_dim where c.c_customer_sk = cs_ship_customer_sk and cs_sold_date_sk = d_date_sk and d_year = 1999 and d_qoy < 4)) group by ca_state,cd_gender,cd_marital_status,cd_dep_count,cd_dep_employed_count,cd_dep_college_count order by ca_state,cd_gender,cd_marital_status,cd_dep_count,cd_dep_employed_count,cd_dep_college_count limit 100;```
[REWRITTEN QUERY]```select ca_state, cd_gender, cd_marital_status, cd_dep_count, count(*) cnt1, avg(cd_dep_count), stddev_samp(cd_dep_count), sum(cd_dep_count), cd_dep_employed_count, count(*) cnt2, avg(cd_dep_employed_count), stddev_samp(cd_dep_employed_count), sum(cd_dep_employed_count), cd_dep_college_count, count(*) cnt3, avg(cd_dep_college_count), stddev_samp(cd_dep_college_count), sum(cd_dep_college_count) from customer c join customer_address ca on c.c_current_addr_sk = ca.ca_address_sk join customer_demographics cd on cd.cd_demo_sk = c.c_current_cdemo_sk where c.c_customer_sk in ( select ss_customer_sk from store_sales join date_dim on ss_sold_date_sk = d_date_sk where d_year = 1999 and d_qoy < 4 ) and ( c.c_customer_sk in ( select ws_bill_customer_sk from web_sales join date_dim on ws_sold_date_sk = d_date_sk where d_year = 1999 and d_qoy < 4 ) or c.c_customer_sk in ( select cs_ship_customer_sk from catalog_sales join date_dim on cs_sold_date_sk = d_date_sk where d_year = 1999 and d_qoy < 4 ) ) group by ca_state, cd_gender, cd_marital_status, cd_dep_count, cd_dep_employed_count, cd_dep_college_count order by ca_state, cd_gender, cd_marital_status, cd_dep_count, cd_dep_employed_count, cd_dep_college_count limit 100;```
Now, consider the TPC-DS schema and create a new original and equivalent rewritten query pair such that the rewrite operation is similar to the provided demonstration.Keep the following points in mind while creating the new query pair:
1. The original query must not have any CTEs.
2. The original query must make use of the following fact tables: ['store_returns', 'inventory']. Below is the join graph for these fact tables:
['store_returns(sr_reason_sk) references reason(r_reason_sk);', 'store_returns(sr_addr_sk) references customer_address(ca_address_sk);', 'store_returns(sr_item_sk) references item(i_item_sk);']
['inventory(inv_date_sk) references date_dim(d_date_sk);', 'inventory(inv_item_sk) references item(i_item_sk);', 'inventory(inv_warehouse_sk) references warehouse(w_warehouse_sk);']
5. The original query must employ 1 joins.
6. The original query must employ the following aggregate functions: ['min'].
7. The original query must employ the following operators in its WHERE clause: [].
8. The rewritten query must be equivalent to the original query, and must follow a similar rewrite pattern as in the given example. Enclose the original and rewritten queries within ``` and ```.ASSISTANT
Here is the pair of original and rewritten SQL queries as per your requirements: [ORIGINAL QUERY] ```sql select sr_reason_sk, sr_item_sk, min(sr_return_amt) min_return_amt from store_returns sr, inventory inv where sr.sr_item_sk = inv.inv_item_sk and exists ( select * from reason r where sr.sr_reason_sk = r.r_reason_sk and r.r_reason_desc = 'Return due to quality' ) and exists ( select * from date_dim dd, warehouse ww where inv.inv_date_sk = dd.d_date_sk and inv.inv_warehouse_sk = ww.w_warehouse_sk and dd.d_year = 2020 ) group by sr_reason_sk, sr_item_sk order by sr_reason_sk, sr_item_sk limit 100; ``` [REWRITTEN QUERY] ```sql select sr_reason_sk, sr_item_sk, min(sr_return_amt) min_return_amt from store_returns sr join inventory inv on sr.sr_item_sk = inv.inv_item_sk where sr.sr_reason_sk in ( select r_reason_sk from reason where r_reason_desc = 'Return due to quality' ) and inv.inv_date_sk in ( select d_date_sk from date_dim where d_year = 2020 ) and inv.inv_warehouse_sk in ( select w_warehouse_sk from warehouse ) group by sr_reason_sk, sr_item_sk order by sr_reason_sk, sr_item_sk limit 100; ``` ### Explanation: - The original query uses correlated subqueries with `EXISTS` to filter data based on conditions in the `reason` and `date_dim` tables. - The rewritten query replaces these `EXISTS` conditions with `IN` clauses. - The `EXISTS` conditions are rewritten by moving the join operations inside the subqueries to be more explicit using `JOIN` constructs, similar to how the transformation was performed in your example. - The rewritten query ensures that only relevant item stock levels from `inventory` and return reasons from `reason` are considered, maintaining the logical output as the original query.