之前的篇章咱们探讨的都是基于单列的分区表，那有无必要建设基于多列的分区表？这种分区表数据分布是否平均？有无非凡的利用场景？有无非凡的优化策略？本篇基于这些问题来进行重点解读。
MySQL 不仅反对基于单列分区，也反对基于多列分区。比方基于字段（f1,f2,f3)来建设分区表，应用办法和应用场景都有些相似于联结索引。比方上面查问语句，同时对列(f1,f2,f3) 进行过滤。
select * from p1 where f1 = 2 and f2 = 2 and f3 = 2;多列分区表的前提是参加分区的列检索频率均等，如果不均等，就没有必要应用多列分区。
咱们还是以具体实例来验证下多列分区的优缺点以及实用场景，这样了解起来更加透彻。
建设一张表p1，字段r1,r2,r3别离取值为1-8，1-5，1-5.
create table p1(r1 int,r2 int,r3 int,log_date datetime);依照字段(r1,r2,r3) 的散布范畴，我来写个存储过程解决下表p1，变为分区表。存储过程代码如下：
DELIMITER $$USE `ytt_new`$$DROP PROCEDURE IF EXISTS `sp_add_partition_ytt_new_p1`$$CREATE DEFINER=`root`@`%` PROCEDURE `sp_add_partition_ytt_new_p1`()BEGIN DECLARE i,j,k INT UNSIGNED DEFAULT 1; SET @stmt = ''; SET @stmt_begin = 'ALTER TABLE p1 PARTITION BY RANGE COLUMNS (r1,r2,r3)('; WHILE i <= 8 DO set j = 1; while j <= 5 do set k = 1; while k <= 5 do SET @stmt = CONCAT(@stmt,' PARTITION p',i,j,k,' VALUES LESS THAN (',i,',',j,',',k,'),'); set k = k + 1; end while; set j = j + 1; end while; SET i = i + 1; END WHILE; SET @stmt_end = 'PARTITION p_max VALUES LESS THAN (maxvalue,maxvalue,maxvalue))'; SET @stmt = CONCAT(@stmt_begin,@stmt,@stmt_end); PREPARE s1 FROM @stmt; EXECUTE s1; DROP PREPARE s1; SET @stmt = NULL; SET @stmt_begin = NULL; SET @stmt_end = NULL; END$$DELIMITER ;调用存储过程，变更表p1为多列分区表，此时表p1有201个分区，记录数为500W条。
mysql> call sp_add_partition_ytt_new_p1;Query OK, 0 rows affected (14.89 sec)mysql> select count(partition_name) as partition_count from information_schema.partitions where table_schema = 'ytt_new' and table_name ='p1';+-----------------+| partition_count |+-----------------+| 201 |+-----------------+1 row in set (0.00 sec)mysql> select count(*) from p1;+----------+| count(*) |+----------+| 5000000 |+----------+1 row in set (12.01 sec)用同样的办法建设一张分区表p2，来对单列分区表与多列分区表在一些场景下的性能做下比照：
分区表p2依照字段r1分区，仅仅分了9个。
mysql> CREATE TABLE `p2` ( `r1` int DEFAULT NULL, `r2` int DEFAULT NULL, `r3` int DEFAULT NULL, `log_date` datetime DEFAULT NULL) ENGINE=InnoDBPARTITION BY RANGE COLUMNS(r1)(PARTITION p1 VALUES LESS THAN (1) , PARTITION p2 VALUES LESS THAN (2) , PARTITION p3 VALUES LESS THAN (3) , PARTITION p4 VALUES LESS THAN (4) , PARTITION p5 VALUES LESS THAN (5) , PARTITION p6 VALUES LESS THAN (6) , PARTITION p7 VALUES LESS THAN (7) , PARTITION p8 VALUES LESS THAN (8) , PARTITION p_max VALUES LESS THAN (MAXVALUE) )1 row in set (0.00 sec)mysql> insert into p2 select * from p1;Query OK, 5000000 rows affected (1 min 37.92 sec)Records: 5000000 Duplicates: 0 Warnings: 0多个字段等值过滤的性能比照：同样的查问条件，表p1（执行工夫0.02秒）比p2（执行工夫0.49秒）要快几十倍。
mysql> select count(*) from p1 where r1 = 2 and r2 = 2 and r3 = 2;+----------+| count(*) |+----------+| 24992 |+----------+1 row in set (0.02 sec)mysql> select count(*) from p2 where r1 = 2 and r2 = 2 and r3 = 2;+----------+| count(*) |+----------+| 24992 |+----------+1 row in set (0.49 sec)查看两者执行打算比照： 同样的查问，表p1扫描行数只有2W多，而表p2扫描行数有62W行，相差微小。
mysql> explain select count(*) from p1 where r1 = 2 and r2 = 2 and r3 = 2\G*************************** 1. row *************************** id: 1 select_type: SIMPLE table: p1 partitions: p223 type: ALL... rows: 24711 filtered: 0.10 Extra: Using where1 row in set, 1 warning (0.00 sec)mysql> explain select count(*) from p2 where r1 = 2 and r2 = 2 and r3 = 2\G*************************** 1. row *************************** id: 1 select_type: SIMPLE table: p2 partitions: p3 type: ALL... rows: 623239 filtered: 0.10 Extra: Using where1 row in set, 1 warning (0.00 sec)如果过滤字段不残缺呢？比方不检索最初一列，再次做下比照：同样表p1（0.1秒）比表p2（0.52秒）执行工夫要少几倍。
mysql> select count(*) from p1 where r1 = 2 and r2 = 2;+----------+| count(*) |+----------+| 124649 |+----------+1 row in set (0.10 sec)mysql> select count(*) from p2 where r1 = 2 and r2 = 2;+----------+| count(*) |+----------+| 124649 |+----------+1 row in set (0.52 sec)那只检索第一列呢：这次表p1和p2执行工夫上差不多，p2略微占优势。
mysql> select count(*) from p1 where r1 = 2 ;+----------+| count(*) |+----------+| 624599 |+----------+1 row in set (0.56 sec)mysql> select count(*) from p2 where r1 = 2 ;+----------+| count(*) |+----------+| 624599 |+----------+1 row in set (0.45 sec)看下执行打算比照：表p1扫描的分区数为26个，表p2仅扫描1个分区，分区数量上表p2绝对少很多。
mysql> explain select count(*) from p1 where r1 = 2 \G*************************** 1. row *************************** id: 1 select_type: SIMPLE table: p1 partitions: p211,p212,p213,p214,p215,p221,p222,p223,p224,p225,p231,p232,p233,p234,p235,p241,p242,p243,p244,p245,p251,p252,p253,p254,p255,p311 type: ALL... rows: 648074 filtered: 10.00 Extra: Using where1 row in set, 1 warning (0.00 sec)mysql> explain select count(*) from p2 where r1 = 2 \G*************************** 1. row *************************** id: 1 select_type: SIMPLE table: p2 partitions: p3 type: ALL... rows: 623239 filtered: 10.00 Extra: Using where1 row in set, 1 warning (0.00 sec)如果把字段r1拿掉呢？执行工夫也相差无几，表p1和表p2都会扫描所有分区。
mysql> select count(*) from p1 where r2 = 2;+----------+| count(*) |+----------+| 998700 |+----------+1 row in set (3.87 sec)mysql> select count(*) from p2 where r2 = 2;+----------+| count(*) |+----------+| 998700 |+----------+1 row in set (3.75 sec)那鉴于此，再来探讨一个问题：对于多列分区，字段的排列程序是否重要？
对于这个程序要和咱们查问语句对应的过滤条件来一一阐明。 相似上面两类 SQL ：
SQL 1： select * from p1 where r1 = 2 and r2 = 2 and r3 = 2;对于SQL 1，程序无关紧要，因为三个列在查问时都已蕴含；
SQL 2: select * from p1 where r1 = 2 and r2 = 2;对于SQL 2 ， (r1,r2,r3) 和 (r2,r1,r3) 都能够满足。
SQL 3： select * from p1 where r2 = 2 and r3 = 2;对于SQL 3， (r2,r3,r1) 和 (r3,r2,r1) 也都能够满足。
用同样的办法来建设分区表p3，分区字段程序为(r2,r3,r1):
mysql> show create table p3\G*************************** 1. row *************************** Table: p3Create Table: CREATE TABLE `p3` ( `r1` int DEFAULT NULL, `r2` int DEFAULT NULL, `r3` int DEFAULT NULL, `log_date` datetime DEFAULT NULL) ENGINE=InnoDB /*!50500 PARTITION BY RANGE COLUMNS(r2,r3,r1)(PARTITION p111 VALUES LESS THAN (1,1,1) ENGINE = InnoDB,...对于表p3来讲：上面这条 SQL 执行工夫比表p1要快几十倍，因为分区字段程序不同，表p1要扫描所有分区能力出后果。
mysql> select count(*) from p3 where r2 = 1 and r3 = 4 ;+----------+| count(*) |+----------+| 199648 |+----------+1 row in set (0.22 sec)mysql> select count(*) from p1 where r2 = 1 and r3 = 4 ;+----------+| count(*) |+----------+| 199648 |+----------+1 row in set (5.05 sec)所以对于多列分区表，正如结尾讲的一样，它和联结索引的应用办法、注意事项、应用场景也都很相似。对于某些特定的场景，应用多列分区能显著放慢查问性能。