话不多说 直接上代码
select * from(SELECT Datepart(month, start.add_time) AS month, Count(DISTINCT( start.id )) AS count, Count(CASE WHEN distribution_processing = 215 THEN 1 END) AS gxd_count, Count(CASE WHEN distribution_processing = 48 THEN 1 END) AS jxz_count, Count(CASE WHEN distribution_processing = 247 THEN 1 END) AS jzy_count, Count(CASE WHEN distribution_processing = 244 THEN 1 END) AS yxy_count, Count(CASE WHEN distribution_processing = 266 THEN 1 END) AS zry_count, Count(CASE WHEN distribution_processing = 268 THEN 1 END) AS zyf_count, Count(CASE WHEN Datediff(ss, start.add_time, stop.add_time) > ( CASE WHEN expected.task_timeout_hour_4 != 0 THEN ( expected.task_timeout_hour_4 - 0.5 ) * 3600 WHEN expected.task_timeout_hour_4 = 0 THEN expected.task_timeout_hour_4 * 3600 END )THEN 1 END) AS timeout_count FROM yh_account_record LEFT JOIN yh_account_log AS start ON start.acc_id = yh_account_record.id AND start.state_type_start = 50 AND start.state_type = 4 LEFT JOIN yh_account_log AS stop ON stop.acc_id = yh_account_record.id AND stop.state_type_start = 4 AND stop.state_type = 11 LEFT JOIN yh_account_log AS cancel ON cancel.acc_id = yh_account_record.id AND cancel.state_type_start = 4 AND cancel.state_type = 8 LEFT JOIN yh_exchange_member ON yh_account_record.exc_bro_id = yh_exchange_member.id LEFT JOIN yh_exchange AS expected ON yh_exchange_member.exc_id = expected.id WHERE yh_account_record.distribution_processing is not null AND start.add_time BETWEEN '2020-1-1' AND '2020-12-31' GROUP BY Datepart(month, start.add_time) )as start, (select Datepart(month, cancel.add_time) as month, Count(DISTINCT( cancel.id )) AS invalid_count from yh_account_record LEFT JOIN yh_account_log AS cancel ON cancel.acc_id = yh_account_record.id AND cancel.state_type_start = 4 AND cancel.state_type = 8 WHERE yh_account_record.distribution_processing is not null AND cancel.add_time BETWEEN '2020-1-1' AND '2020-12-31' GROUP BY Datepart(month, cancel.add_time) )as cancel where start.month=cancel.month
上述代码是对两个查询结果做笛卡尔连接 同时用where语句过滤掉无用数据
SELECT Datepart(month, start.add_time) AS month, Count(DISTINCT( start.id )) AS count, Count(CASE WHEN distribution_processing = 215 THEN 1 END) AS gxd_count, Count(CASE WHEN distribution_processing = 48 THEN 1 END) AS jxz_count, Count(CASE WHEN distribution_processing = 247 THEN 1 END) AS jzy_count, Count(CASE WHEN distribution_processing = 244 THEN 1 END) AS yxy_count, Count(CASE WHEN distribution_processing = 266 THEN 1 END) AS zry_count, Count(CASE WHEN distribution_processing = 268 THEN 1 END) AS zyf_count, Count(CASE WHEN Datediff(ss, start.add_time, stop.add_time) > ( CASE WHEN expected.task_timeout_hour_4 != 0 THEN ( expected.task_timeout_hour_4 - 0.5 ) * 3600 WHEN expected.task_timeout_hour_4 = 0 THEN expected.task_timeout_hour_4 * 3600 END )THEN 1 END) AS timeout_count, Count(DISTINCT( cancel.id )) AS invalid_count FROM yh_account_record LEFT JOIN yh_account_log AS start ON start.acc_id = yh_account_record.id AND start.state_type_start = 50 AND start.state_type = 4 LEFT JOIN yh_account_log AS stop ON stop.acc_id = yh_account_record.id AND stop.state_type_start = 4 AND stop.state_type = 11 LEFT JOIN yh_account_log AS cancel ON cancel.acc_id = yh_account_record.id AND cancel.state_type_start = 4 AND cancel.state_type = 8 LEFT JOIN yh_exchange_member ON yh_account_record.exc_bro_id = yh_exchange_member.id LEFT JOIN yh_exchange AS expected ON yh_exchange_member.exc_id = expected.id WHERE yh_account_record.distribution_processing is not null AND convert(char(10),start.add_time,23) BETWEEN '2020-1-1' AND '2020-12-31' GROUP BY Datepart(month, start.add_time)
上述代码是通过对数据做左链接,同时通过on关键字控制链接后的结果集,通过where关键字筛选输出结果。
两者最大的不同在于,前者的代码稍微变长了一点,但是对于条件的作用更加的独立,直接作用在查询结果上,比如对于分月统计每月的xx状态数据以及yy状态数据,通过左链接的sql语句where 条件只能写在单一条件上,而使用笛卡尔积的sql语句可以使where 语句独立作用在自己的个体上。