优化过的语句如下
| select emp.id from cm_log cl inner join employee emp on cl.ref_table = 'Employee' and cl.ref_oid = emp.id where cl.last_upd_date >='2013-11-07 15:03:00' and cl.last_upd_date<='2013-11-08 16:00:00' and emp.is_deleted = 0 union select emp.id from cm_log cl inner join emp_certificate ec on cl.ref_table = 'EmpCertificate' and cl.ref_oid = ec.id inner join employee emp on emp.id = ec.emp_id where cl.last_upd_date >='2013-11-07 15:03:00' and cl.last_upd_date<='2013-11-08 16:00:00' and emp.is_deleted = 0 |
2.不需要了解业务场景，只需要改造的语句和改造之前的语句保持结果一致
3.现有索引可以满足，不需要建索引
4.用改造后的语句实验一下，只需要10ms 降低了近200倍！
| +----+--------------+------------+--------+---------------------------------+-------------------+---------+-----------------------+------+-------------+ | id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra | +----+--------------+------------+--------+---------------------------------+-------------------+---------+-----------------------+------+-------------+ | 1 | PRIMARY | cl | range | cm_log_cls_id,idx_last_upd_date | idx_last_upd_date | 8 | NULL | 379 | Using where | | 1 | PRIMARY | emp | eq_ref | PRIMARY | PRIMARY | 4 | meituanorg.cl.ref_oid | 1 | Using where | | 2 | UNION | cl | range | cm_log_cls_id,idx_last_upd_date | idx_last_upd_date | 8 | NULL | 379 | Using where | | 2 | UNION | ec | eq_ref | PRIMARY,emp_certificate_empid | PRIMARY | 4 | meituanorg.cl.ref_oid | 1 | | | 2 | UNION | emp | eq_ref | PRIMARY | PRIMARY | 4 | meituanorg.ec.emp_id | 1 | Using where | | NULL | UNION RESULT | <union1,2> | ALL | NULL | NULL | NULL | NULL | NULL | | +----+--------------+------------+--------+---------------------------------+-------------------+---------+-----------------------+------+-------------+ 53 rows in set (0.01 sec) |
明确应用场景
举这个例子的目的在于颠覆我们对列的区分度的认知，一般上我们认为区分度越高的列，越容易锁定更少的记录，但在一些特殊的情况下，这种理论是有局限性的
| select * from stage_poi sp where sp.accurate_result=1 and ( sp.sync_status=0 or sp.sync_status=2 or sp.sync_status=4 ); |
0.先看看运行多长时间,951条数据6.22秒，真的很慢
| 951 rows in set (6.22 sec) |
1.先explain，rows达到了361万，type = ALL表明是全表扫描
