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WITH cross_items AS (
SELECT
i_item_sk AS ss_item_sk
FROM item, (
SELECT
iss.i_brand_id AS brand_id,
iss.i_class_id AS class_id,
iss.i_category_id AS category_id
FROM store_sales, item AS iss, date_dim AS d1
WHERE
ss_item_sk = iss.i_item_sk
AND ss_sold_date_sk = d1.d_date_sk
AND d1.d_year BETWEEN 1999 AND 1999 + 2
INTERSECT
SELECT
ics.i_brand_id,
ics.i_class_id,
ics.i_category_id
FROM catalog_sales, item AS ics, date_dim AS d2
WHERE
cs_item_sk = ics.i_item_sk
AND cs_sold_date_sk = d2.d_date_sk
AND d2.d_year BETWEEN 1999 AND 1999 + 2
INTERSECT
SELECT
iws.i_brand_id,
iws.i_class_id,
iws.i_category_id
FROM web_sales, item AS iws, date_dim AS d3
WHERE
ws_item_sk = iws.i_item_sk
AND ws_sold_date_sk = d3.d_date_sk
AND d3.d_year BETWEEN 1999 AND 1999 + 2
) AS polaris_derived_1
WHERE
i_brand_id = brand_id AND i_class_id = class_id AND i_category_id = category_id
), avg_sales AS (
SELECT
AVG(quantity * list_price) AS average_sales
FROM (
SELECT
ss_quantity AS quantity,
ss_list_price AS list_price
FROM store_sales, date_dim
WHERE
ss_sold_date_sk = d_date_sk AND d_year BETWEEN 1999 AND 1999 + 2
UNION ALL
SELECT
cs_quantity AS quantity,
cs_list_price AS list_price
FROM catalog_sales, date_dim
WHERE
cs_sold_date_sk = d_date_sk AND d_year BETWEEN 1999 AND 1999 + 2
UNION ALL
SELECT
ws_quantity AS quantity,
ws_list_price AS list_price
FROM web_sales, date_dim
WHERE
ws_sold_date_sk = d_date_sk AND d_year BETWEEN 1999 AND 1999 + 2
) AS x
)
SELECT
TOP 100
channel,
i_brand_id,
i_class_id,
i_category_id,
SUM(sales),
SUM(number_sales)
FROM (
SELECT
'store' AS channel,
i_brand_id,
i_class_id,
i_category_id,
SUM(ss_quantity * ss_list_price) AS sales,
COUNT(*) AS number_sales
FROM store_sales, item, date_dim
WHERE
ss_item_sk IN (
SELECT
ss_item_sk
FROM cross_items
)
AND ss_item_sk = i_item_sk
AND ss_sold_date_sk = d_date_sk
AND d_year = 1999 + 2
AND d_moy = 11
GROUP BY
i_brand_id,
i_class_id,
i_category_id
HAVING
SUM(ss_quantity * ss_list_price) > (
SELECT
average_sales
FROM avg_sales
)
UNION ALL
SELECT
'catalog' AS channel,
i_brand_id,
i_class_id,
i_category_id,
SUM(cs_quantity * cs_list_price) AS sales,
COUNT(*) AS number_sales
FROM catalog_sales, item, date_dim
WHERE
cs_item_sk IN (
SELECT
ss_item_sk
FROM cross_items
)
AND cs_item_sk = i_item_sk
AND cs_sold_date_sk = d_date_sk
AND d_year = 1999 + 2
AND d_moy = 11
GROUP BY
i_brand_id,
i_class_id,
i_category_id
HAVING
SUM(cs_quantity * cs_list_price) > (
SELECT
average_sales
FROM avg_sales
)
UNION ALL
SELECT
'web' AS channel,
i_brand_id,
i_class_id,
i_category_id,
SUM(ws_quantity * ws_list_price) AS sales,
COUNT(*) AS number_sales
FROM web_sales, item, date_dim
WHERE
ws_item_sk IN (
SELECT
ss_item_sk
FROM cross_items
)
AND ws_item_sk = i_item_sk
AND ws_sold_date_sk = d_date_sk
AND d_year = 1999 + 2
AND d_moy = 11
GROUP BY
i_brand_id,
i_class_id,
i_category_id
HAVING
SUM(ws_quantity * ws_list_price) > (
SELECT
average_sales
FROM avg_sales
)
) AS y
GROUP BY
ROLLUP (
channel,
i_brand_id,
i_class_id,
i_category_id
)
ORDER BY
channel,
i_brand_id,
i_class_id,
i_category_id;
Step details
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Green: scan · yellow: join · purple: row movement. Rows and costs are optimiser estimates; costs use model units.