turns-00049.parquet:12372
e4e7fdbb58c93bd74cbd541b
turn 1/1gpt-4o-2024-08-06EnglishUnited States833 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]```with ss_items as (select i_item_id item_id ,sum(ss_ext_sales_price) ss_item_rev from store_sales ,item ,date_dim where ss_item_sk = i_item_sk and d_date in (select d_date from date_dim where d_week_seq = (select d_week_seq from date_dim where d_date = '2001-06-16')) and ss_sold_date_sk = d_date_sk group by i_item_id), cs_items as (select i_item_id item_id ,sum(cs_ext_sales_price) cs_item_rev from catalog_sales ,item ,date_dim where cs_item_sk = i_item_sk and d_date in (select d_date from date_dim where d_week_seq = (select d_week_seq from date_dim where d_date = '2001-06-16')) and cs_sold_date_sk = d_date_sk group by i_item_id), ws_items as (select i_item_id item_id ,sum(ws_ext_sales_price) ws_item_rev from web_sales ,item ,date_dim where ws_item_sk = i_item_sk and d_date in (select d_date from date_dim where d_week_seq =(select d_week_seq from date_dim where d_date = '2001-06-16')) and ws_sold_date_sk = d_date_sk group by i_item_id) select ss_items.item_id ,ss_item_rev ,ss_item_rev/((ss_item_rev+cs_item_rev+ws_item_rev)/3) * 100 ss_dev ,cs_item_rev ,cs_item_rev/((ss_item_rev+cs_item_rev+ws_item_rev)/3) * 100 cs_dev ,ws_item_rev ,ws_item_rev/((ss_item_rev+cs_item_rev+ws_item_rev)/3) * 100 ws_dev ,(ss_item_rev+cs_item_rev+ws_item_rev)/3 average from ss_items,cs_items,ws_items where ss_items.item_id=cs_items.item_id and ss_items.item_id=ws_items.item_id and ss_item_rev between 0.9 * cs_item_rev and 1.1 * cs_item_rev and ss_item_rev between 0.9 * ws_item_rev and 1.1 * ws_item_rev and cs_item_rev between 0.9 * ss_item_rev and 1.1 * ss_item_rev and cs_item_rev between 0.9 * ws_item_rev and 1.1 * ws_item_rev and ws_item_rev between 0.9 * ss_item_rev and 1.1 * ss_item_rev and ws_item_rev between 0.9 * cs_item_rev and 1.1 * cs_item_rev order by item_id ,ss_item_rev limit 100;```
[REWRITTEN QUERY]```with dates as (select d_date from date_dim where d_week_seq = (select d_week_seq from date_dim where d_date = '2001-06-16')), ss_items as (select i_item_id item_id ,sum(ss_ext_sales_price) ss_item_rev from store_sales ,item ,date_dim where ss_item_sk = i_item_sk and d_date in (select d_date from dates) and ss_sold_date_sk = d_date_sk group by i_item_id), cs_items as (select i_item_id item_id ,sum(cs_ext_sales_price) cs_item_rev from catalog_sales ,item ,date_dim where cs_item_sk = i_item_sk and d_date in (select d_date from dates) and cs_sold_date_sk = d_date_sk group by i_item_id), ws_items as (select i_item_id item_id ,sum(ws_ext_sales_price) ws_item_rev from web_sales ,item ,date_dim where ws_item_sk = i_item_sk and d_date in (select d_date from dates) and ws_sold_date_sk = d_date_sk group by i_item_id) select ss_items.item_id ,ss_item_rev ,ss_item_rev/((ss_item_rev+cs_item_rev+ws_item_rev)/3) * 100 ss_dev ,cs_item_rev ,cs_item_rev/((ss_item_rev+cs_item_rev+ws_item_rev)/3) * 100 cs_dev ,ws_item_rev ,ws_item_rev/((ss_item_rev+cs_item_rev+ws_item_rev)/3) * 100 ws_dev ,(ss_item_rev+cs_item_rev+ws_item_rev)/3 average from ss_items,cs_items,ws_items where ss_items.item_id=cs_items.item_id and ss_items.item_id=ws_items.item_id and ss_item_rev between 0.9 * cs_item_rev and 1.1 * cs_item_rev and ss_item_rev between 0.9 * ws_item_rev and 1.1 * ws_item_rev and cs_item_rev between 0.9 * ss_item_rev and 1.1 * ss_item_rev and cs_item_rev between 0.9 * ws_item_rev and 1.1 * ws_item_rev and ws_item_rev between 0.9 * ss_item_rev and 1.1 * ss_item_rev and ws_item_rev between 0.9 * cs_item_rev and 1.1 * cs_item_rev order by item_id ,ss_item_rev 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 contain CTEs.
2. The original query must make use of the following fact tables: ['web_sales', 'catalog_sales']. Below is the join graph for these fact tables:
['web_sales(ws_ship_mode_sk) references ship_mode(sm_ship_mode_sk);', 'web_sales(ws_ship_date_sk) references date_dim(d_date_sk);', 'web_sales(ws_web_site_sk) references web_site(web_site_sk);']
['catalog_sales(cs_warehouse_sk) references warehouse(w_warehouse_sk);', 'catalog_sales(cs_ship_mode_sk) references ship_mode(sm_ship_mode_sk);', 'catalog_sales(cs_ship_cdemo_sk) references customer_demographics(cd_demo_sk);']
5. The original query must employ 1 joins.
6. The original query must employ the following aggregate functions: ['min', 'avg'].
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
[ORIGINAL QUERY]
```sql
with ws_data as (
select ws_web_site_sk as site_sk,
min(ws_net_profit) as min_profit,
avg(ws_quantity) as avg_quantity
from web_sales, date_dim
where ws_ship_date_sk = d_date_sk
and d_date between '2001-07-15' and '2001-07-21'
group by ws_web_site_sk
),
cs_data as (
select cs_warehouse_sk as warehouse_sk,
min(cs_net_profit) as min_profit,
avg(cs_quantity) as avg_quantity
from catalog_sales, warehouse
where cs_warehouse_sk = w_warehouse_sk
group by cs_warehouse_sk
)
select ws_data.site_sk, cs_data.warehouse_sk,
ws_data.min_profit as ws_min_profit,
cs_data.min_profit as cs_min_profit,
ws_data.avg_quantity as ws_avg_quantity,
cs_data.avg_quantity as cs_avg_quantity
from ws_data, cs_data
where ws_data.site_sk = cs_data.warehouse_sk;
```
[REWRITTEN QUERY]
```sql
with dates_range as (
select d_date_sk from date_dim
where d_date between '2001-07-15' and '2001-07-21'
),
ws_data as (
select ws_web_site_sk as site_sk,
min(ws_net_profit) as min_profit,
avg(ws_quantity) as avg_quantity
from web_sales
where ws_ship_date_sk in (select d_date_sk from dates_range)
group by ws_web_site_sk
),
cs_data as (
select cs_warehouse_sk as warehouse_sk,
min(cs_net_profit) as min_profit,
avg(cs_quantity) as avg_quantity
from catalog_sales
where cs_warehouse_sk = w_warehouse_sk
group by cs_warehouse_sk
)
select ws_data.site_sk, cs_data.warehouse_sk,
ws_data.min_profit as ws_min_profit,
cs_data.min_profit as cs_min_profit,
ws_data.avg_quantity as ws_avg_quantity,
cs_data.avg_quantity as cs_avg_quantity
from ws_data, cs_data
where ws_data.site_sk = cs_data.warehouse_sk;
```
In this pair of queries, the rewrite involves factoring out the date range filtering into a separate CTE `dates_range`, mimicking the pattern seen in the provided demonstration where a common subquery is extracted for reuse. This reorganization can help optimize execution plans by explicitly defining the data subset upfront, potentially reducing redundant calculations.