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WITH v1 AS (
SELECT
i_category AS i_category,
i_brand AS i_brand,
s_store_name AS s_store_name,
s_company_name AS s_company_name,
d_year AS d_year,
d_moy AS d_moy,
SUM(ss_sales_price) AS sum_sales,
AVG(SUM(ss_sales_price)) OVER (PARTITION BY i_category, i_brand, s_store_name, s_company_name, d_year) AS avg_monthly_sales,
RANK() OVER (
PARTITION BY i_category, i_brand, s_store_name, s_company_name
ORDER BY d_year, d_moy
) AS rn
FROM item, store_sales, date_dim, store
WHERE
ss_item_sk = i_item_sk
AND ss_sold_date_sk = d_date_sk
AND ss_store_sk = s_store_sk
AND (
d_year = 2001
OR (
d_year = 2001 - 1 AND d_moy = 12
)
OR (
d_year = 2001 + 1 AND d_moy = 1
)
)
GROUP BY
i_category,
i_brand,
s_store_name,
s_company_name,
d_year,
d_moy
), v2 AS (
SELECT
v1.i_category AS i_category,
v1.i_brand AS i_brand,
v1.d_year AS d_year,
v1.d_moy AS d_moy,
v1.avg_monthly_sales AS avg_monthly_sales,
v1.sum_sales AS sum_sales,
v1_lag.sum_sales AS psum,
v1_lead.sum_sales AS nsum
FROM v1, v1 AS v1_lag, v1 AS v1_lead
WHERE
v1.i_category = v1_lag.i_category
AND v1.i_category = v1_lead.i_category
AND v1.i_brand = v1_lag.i_brand
AND v1.i_brand = v1_lead.i_brand
AND v1.s_store_name = v1_lag.s_store_name
AND v1.s_store_name = v1_lead.s_store_name
AND v1.s_company_name = v1_lag.s_company_name
AND v1.s_company_name = v1_lead.s_company_name
AND v1.rn = v1_lag.rn + 1
AND v1.rn = v1_lead.rn - 1
)
SELECT
TOP 100
*
FROM v2
WHERE
d_year = 2001
AND avg_monthly_sales > 0
AND CASE
WHEN avg_monthly_sales > 0
THEN ABS(sum_sales - avg_monthly_sales) / avg_monthly_sales
ELSE NULL
END > 0.1
ORDER BY
sum_sales - avg_monthly_sales,
avg_monthly_sales;
Step details
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Green: scan · yellow: join · purple: row movement. Rows and costs are optimiser estimates; costs use model units.