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 not have any CTEs.
2. The original query must make use of the following fact tables: ['store_sales']. Below is the join graph for these fact tables:
['store_sales(ss_store_sk) references store(s_store_sk);', 'store_sales(ss_item_sk) references item(i_item_sk);', 'store_sales(ss_customer_sk) references customer(c_customer_sk);']
4. The original query must employ 2 joins.
5. The original query must employ the following operators in its WHERE clause: [].
6. 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 a new pair of original and rewritten queries following the same rewrite pattern:
[ORIGINAL QUERY]
```sql
select i_item_id, sum(ss_net_profit) as total_profit
from store_sales, item, store
where ss_item_sk = i_item_sk
and ss_store_sk = s_store_sk
and s_state in (select s_state from store where s_city = 'Seattle')
group by i_item_id
order by total_profit desc;
```
[REWRITTEN QUERY]
```sql
with seattle_store_states as (
select s_state from store where s_city = 'Seattle'
)
select i_item_id, sum(ss_net_profit) as total_profit
from store_sales, item, store
where ss_item_sk = i_item_sk
and ss_store_sk = s_store_sk
and s_state in (select s_state from seattle_store_states)
group by i_item_id
order by total_profit desc;
```
### Explanation
- **Original Query**: The original query identifies the items sold in stores located in cities whose state is the same as stores in 'Seattle'. This is computed inline in the WHERE clause.
- **Rewritten Query**: The subquery that computes the list of states with stores in 'Seattle' is extracted to a common table expression (CTE) named `seattle_store_states`. This change improves performance by computing the list of states only once, rather than recalculating it for each row in the `store_sales` table.
This rewrite follows the same pattern observed in the initial example: extracting a repeated subquery into a named CTE, reducing redundant computations, and potentially optimizing query performance.