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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 AS ss_store_sk,
AVG(revenue) AS ave
FROM (
SELECT
ss_store_sk AS ss_store_sk,
ss_item_sk AS 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 1201 AND 1201 + 11
GROUP BY
ss_store_sk,
ss_item_sk
) AS sa
GROUP BY
ss_store_sk
) AS sb, (
SELECT
ss_store_sk AS ss_store_sk,
ss_item_sk AS 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 1201 AND 1201 + 11
GROUP BY
ss_store_sk,
ss_item_sk
) AS 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
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.