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SELECT
TOP 100
cd_gender,
cd_marital_status,
cd_education_status,
COUNT(*) AS cnt1,
cd_purchase_estimate,
COUNT(*) AS cnt2,
cd_credit_rating,
COUNT(*) AS cnt3
FROM customer AS c, customer_address AS ca, customer_demographics
WHERE
c.c_current_addr_sk = ca.ca_address_sk
AND ca_state IN ('NE', 'FL', 'KY')
AND cd_demo_sk = c.c_current_cdemo_sk
AND EXISTS(
SELECT
*
FROM store_sales, date_dim
WHERE
c.c_customer_sk = ss_customer_sk
AND ss_sold_date_sk = d_date_sk
AND d_year = 1999
AND d_moy BETWEEN 1 AND 1 + 2
)
AND (
NOT EXISTS(
SELECT
*
FROM web_sales, date_dim
WHERE
c.c_customer_sk = ws_bill_customer_sk
AND ws_sold_date_sk = d_date_sk
AND d_year = 1999
AND d_moy BETWEEN 1 AND 1 + 2
)
AND NOT EXISTS(
SELECT
*
FROM catalog_sales, date_dim
WHERE
c.c_customer_sk = cs_ship_customer_sk
AND cs_sold_date_sk = d_date_sk
AND d_year = 1999
AND d_moy BETWEEN 1 AND 1 + 2
)
)
GROUP BY
cd_gender,
cd_marital_status,
cd_education_status,
cd_purchase_estimate,
cd_credit_rating
ORDER BY
cd_gender,
cd_marital_status,
cd_education_status,
cd_purchase_estimate,
cd_credit_rating;
Step details
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Green: scan · yellow: join · purple: row movement. Rows and costs are optimiser estimates; costs use model units.