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select top 100
s_store_name,
i_item_desc,
sc.revenue,
i_current_price,
i_wholesale_cost,
i_brand
from store, item,
(select ss_store_sk, avg(revenue) as ave
from
(select ss_store_sk, ss_item_sk,
sum(ss_sales_price) as revenue
from store_sales, date_dim
where ss_sold_date_sk = d_date_sk and d_month_seq between 1207 and 1207+11
and ss_sales_price / ss_list_price BETWEEN 41 * 0.01 AND 51 * 0.01
group by ss_store_sk, ss_item_sk) sa
group by ss_store_sk) sb,
(select ss_store_sk, ss_item_sk, sum(ss_sales_price) as revenue
from store_sales, date_dim
where ss_sold_date_sk = d_date_sk and d_month_seq between 1207 and 1207+11
and ss_sales_price / ss_list_price BETWEEN 41 * 0.01 AND 51 * 0.01
group by ss_store_sk, ss_item_sk) sc
where sb.ss_store_sk = sc.ss_store_sk and
sc.revenue <= 0.1 * sb.ave and
s_store_sk = sc.ss_store_sk and
i_item_sk = sc.ss_item_sk
and i_manager_id BETWEEN 54 and 58
and s_state in ('IA','IN','WI')
order by s_store_name, i_item_desc
;
Step details
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Green: scan · yellow: join · purple: row movement. Rows and costs are optimiser estimates; costs use model units.