Loading plan…
SELECT
TOP 100
s_store_name,
s_company_id,
s_street_number,
s_street_name,
s_street_type,
s_suite_number,
s_city,
s_county,
s_state,
s_zip,
SUM(CASE WHEN (
sr_returned_date_sk - ss_sold_date_sk <= 30
) THEN 1 ELSE 0 END) AS [30 days],
SUM(
CASE
WHEN (
sr_returned_date_sk - ss_sold_date_sk > 30
)
AND (
sr_returned_date_sk - ss_sold_date_sk <= 60
)
THEN 1
ELSE 0
END
) AS [31- INTERVAL '60' DAY],
SUM(
CASE
WHEN (
sr_returned_date_sk - ss_sold_date_sk > 60
)
AND (
sr_returned_date_sk - ss_sold_date_sk <= 90
)
THEN 1
ELSE 0
END
) AS [61- INTERVAL '90' DAY],
SUM(
CASE
WHEN (
sr_returned_date_sk - ss_sold_date_sk > 90
)
AND (
sr_returned_date_sk - ss_sold_date_sk <= 120
)
THEN 1
ELSE 0
END
) AS [91- INTERVAL '120' DAY],
SUM(CASE WHEN (
sr_returned_date_sk - ss_sold_date_sk > 120
) THEN 1 ELSE 0 END) AS [>120 days]
FROM store_sales, store_returns, store, date_dim AS d1, date_dim AS d2
WHERE
d2.d_year = 1999
AND d2.d_moy = 9
AND ss_ticket_number = sr_ticket_number
AND ss_item_sk = sr_item_sk
AND ss_sold_date_sk = d1.d_date_sk
AND sr_returned_date_sk = d2.d_date_sk
AND ss_customer_sk = sr_customer_sk
AND ss_store_sk = s_store_sk
GROUP BY
s_store_name,
s_company_id,
s_street_number,
s_street_name,
s_street_type,
s_suite_number,
s_city,
s_county,
s_state,
s_zip
ORDER BY
s_store_name,
s_company_id,
s_street_number,
s_street_name,
s_street_type,
s_suite_number,
s_city,
s_county,
s_state,
s_zip;
Step details
Loading plan…
Green: scan · yellow: join · purple: row movement. Rows and costs are optimiser estimates; costs use model units.