实用分区或者说分表最多的场景仍然是针对工夫字段做拆分， 这节咱们具体讲讲如何更好的基于工夫字段来拆分。别离依照年、月、日几个维度的实现办法以及一些细节注意事项。
第一，以年为维度做拆分
日期字段拆分粒度的抉择跟业务检索申请密切相关。比方保留10年数据，每次查问基于某个具体年份做为过滤条件，那依照年拆分必定最好。例如上面SQL：
select * from ytt_pt1 where log_date >='2018-01-01' and log_date < '2019-01-01';那咱们来看下依照年独自拆分的理论例子：表ytt_pt1 ，蕴含1000W条记录，以年为粒度建设分区表。
mysql> create table ytt_pt1(id bigint, log_date date);Query OK, 0 rows affected (0.18 sec)mysql> insert into ytt_pt1 select id,log_date from ytt_p1 limit 10000000;Query OK, 10000000 rows affected (3 min 49.53 sec)Records: 10000000 Duplicates: 0 Warnings: 0mysql> ALTER TABLE ytt_pt1 PARTITION BY RANGE (year(log_date)) -> ( -> PARTITION p0001 VALUES LESS THAN (2012), -> PARTITION p0002 VALUES LESS THAN (2013), -> PARTITION p0003 VALUES LESS THAN (2014), -> PARTITION p0004 VALUES LESS THAN (2015), -> PARTITION p0005 VALUES LESS THAN (2016), -> PARTITION p0006 VALUES LESS THAN (2017), -> PARTITION p0007 VALUES LESS THAN (2018), -> PARTITION p0008 VALUES LESS THAN (2019), -> PARTITION p0009 VALUES LESS THAN (2020), -> PARTITION p0010 VALUES LESS THAN (2021), -> PARTITION p_max VALUES LESS THAN (maxvalue) -> );Query OK, 10000000 rows affected (2 min 33.31 sec)Records: 10000000 Duplicates: 0 Warnings: 0看下按年为粒度的查问成果：以下SQL 间接走分区p0008，查问工夫0.91秒， 这个工夫不算短，前期能够减少过滤条件来缩小查问工夫。
mysql> select count(*) from ytt_pt1 where log_date >='2018-01-01' and log_date < '2019-01-01';+----------+| count(*) |+----------+| 1000204 |+----------+1 row in set (0.91 sec)mysql> explain select count(*) from ytt_pt1 where log_date >='2018-01-01' and log_date < '2019-01-01'\G*************************** 1. row *************************** id: 1 select_type: SIMPLE table: ytt_pt1 partitions: p0008 type: ALLpossible_keys: NULL key: NULL key_len: NULL ref: NULL rows: 998002 filtered: 11.11 Extra: Using where1 row in set, 1 warning (0.00 sec)须要留神：查问只能基于字段来间接过滤，如果基于字段表达式来过滤，MySQL 不确定走哪个分区，会扫描所有分区，解决办法和单表查问一样。比方语句：
select count(*) from ytt_pt1 where year(log_date) = '2018' ;看下执行状况: MySQL扫描所有分区，查问执行工夫9秒多。
mysql> select count(*) from ytt_pt1 where year(log_date) = '2018' ;+----------+| count(*) |+----------+| 1000204 |+----------+1 row in set (9.19 sec)mysql> explain select count(*) from ytt_pt1 where year(log_date) = '2018' \G*************************** 1. row *************************** id: 1 select_type: SIMPLE table: ytt_pt1 partitions: p0001,p0002,p0003,p0004,p0005,p0006,p0007,p0008,p0009,p0010,p_max type: ALLpossible_keys: NULL key: NULL key_len: NULL ref: NULL rows: 9982648 filtered: 100.00 Extra: Using where1 row in set, 1 warning (0.00 sec)如果非保持这种写法，能够给优化器一个提醒，具体到指定分区去检索数据，或者是基于字段表达式建一个虚构列：
mysql> select count(*) from ytt_pt1 partition(p0008) where year(log_date) = '2018' ;+----------+| count(*) |+----------+| 1000204 |+----------+1 row in set (0.84 sec)如果查问依照月作为维度过滤比拟频繁，那必定是依照月来拆最好；比方须要检索2020年当月的某些记录来做后续数据处理，大抵SQL如下：
select * from ytt_pt1_按月拆分表 where log_date in ('2020-01-01'，'2020-01-02'，...)第二，以月为维度做拆分
依照月来拆分，有以下两种写法：
第一种： 间接依照月来拆12个分区: 上面表 ytt_pt1_month1 分区类型为 LIST ，基于函数 month 间接计算。
mysql> show create table ytt_pt1_month1\G*************************** 1. row *************************** Table: ytt_pt1_month1Create Table: CREATE TABLE `ytt_pt1_month1` ( `id` bigint DEFAULT NULL, `log_date` date DEFAULT NULL) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_0900_ai_ci/*!50100 PARTITION BY LIST (month(`log_date`))(PARTITION p0001 VALUES IN (1) ENGINE = InnoDB, PARTITION p0002 VALUES IN (2) ENGINE = InnoDB, PARTITION p0003 VALUES IN (3) ENGINE = InnoDB, PARTITION p0004 VALUES IN (4) ENGINE = InnoDB, PARTITION p0005 VALUES IN (5) ENGINE = InnoDB, PARTITION p0006 VALUES IN (6) ENGINE = InnoDB, PARTITION p0007 VALUES IN (7) ENGINE = InnoDB, PARTITION p0008 VALUES IN (8) ENGINE = InnoDB, PARTITION p0009 VALUES IN (9) ENGINE = InnoDB, PARTITION p0010 VALUES IN (10) ENGINE = InnoDB, PARTITION p0011 VALUES IN (11) ENGINE = InnoDB, PARTITION p0012 VALUES IN (12) ENGINE = InnoDB) */1 row in set (0.00 sec)比方要查问2020年前半个月的记录：查问限定在分区 p0001 里，然而工夫不太现实，得0.66秒。
mysql> select count(*) from ytt_pt1_month1 where log_date in ('2020-01-01','2020-01-02','2020-01-03','2020-01-04','2020-01-05','2020-01-06','2020-01-07','2020-01-08','2020-01-09','2020-01-10','2020-01-11','2020-01-12','2020-01-13','2020-01-14','2020-01-15');+----------+| count(*) |+----------+| 41540 |+----------+1 row in set (0.66 sec)mysql> explain select count(*) from ytt_pt1_month1 where log_date in ('2020-01-01','2020-01-02','2020-01-03','2020-01-04','2020-01-05','2020-01-06','2020-01-07','2020-01-08','2020-01-09','2020-01-10','2020-01-11','2020-01-12','2020-01-13','2020-01-14','2020-01-15')\G*************************** 1. row *************************** id: 1 select_type: SIMPLE table: ytt_pt1_month1 partitions: p0001 type: ALLpossible_keys: NULL key: NULL key_len: NULL ref: NULL rows: 848224 filtered: 50.00 Extra: Using where1 row in set, 1 warning (0.00 sec)第二种： 对于每年的数据，独自划分12个分区，也就是依照年月联结维度来分区，一共有 144 个分区，每个分区对应具体某一年某一月数据。
增加这个分区略微麻烦些，克隆表 ytt_pt1_month1 为 ytt_pt1_month2 ，这里写个存储过程来增加分区信息：
DELIMITER $$USE `ytt`$$DROP PROCEDURE IF EXISTS `sp_add_partition_ytt_pt1_month2`$$CREATE DEFINER=`root`@`%` PROCEDURE `sp_add_partition_ytt_pt1_month2`()BEGIN DECLARE i,j INT UNSIGNED DEFAULT 1; DECLARE v_tmp_date DATE; SET @stmt = ''; SET @stmt_begin = 'ALTER TABLE ytt_pt1_month2 PARTITION BY RANGE COLUMNS (log_date)('; SET i = 2010; WHILE i <= 2020 DO SET j = 1; WHILE j <= 12 DO SET v_tmp_date = CONCAT(i,'-01-01'); SET @stmt = CONCAT(@stmt,'PARTITION p',i,'_',LPAD(j,2,"0"),' VALUES LESS THAN (''',DATE_ADD(v_tmp_date,INTERVAL j MONTH),'''),'); SET j = j + 1; END WHILE; SET i = i + 1; END WHILE; SET @stmt_end = 'PARTITION p_max VALUES LESS THAN (maxvalue))'; SET @stmt = CONCAT(@stmt_begin,@stmt,@stmt_end); PREPARE s1 FROM @stmt; EXECUTE s1; DROP PREPARE s1; SET @stmt = NULL; SET @stmt_begin = NULL; SET @stmt_end = NULL; END$$DELIMITER ;mysql> call sp_add_partition_ytt_pt1_month2;Query OK, 0 rows affected (2 min 20.48 sec)后果相似这样：
PARTITION p2010_01 VALUES LESS THAN ('2010-02-01') ENGINE = InnoDB, ... PARTITION p2010_12 VALUES LESS THAN ('2011-01-01') ENGINE = InnoDB, PARTITION p2011_01 VALUES LESS THAN ('2011-02-01') ENGINE = InnoDB,... PARTITION p2011_12 VALUES LESS THAN ('2012-01-01') ENGINE = InnoDB, ... PARTITION p2020_12 VALUES LESS THAN ('2021-01-01') ENGINE = InnoDB, PARTITION p_max VALUES LESS THAN (MAXVALUE) ENGINE = InnoDB)加好分区后，来察看下方才那个获取2020年前半个月记录的查问：
mysql> select count(*) from ytt_pt1_month2 where log_date in ('2020-01-01','2020-01-02','2020-01-03','2020-01-04','2020-01-05','2020-01-06','2020-01-07','2020-01-08','2020-01-09','2020-01-10','2020-01-11','2020-01-12','2020-01-13','2020-01-14','2020-01-15');+----------+| count(*) |+----------+| 41540 |+----------+1 row in set (0.06 sec)mysql> explain select count(*) from ytt_pt1_month2 where log_date in ('2020-01-01','2020-01-02','2020-01-03','2020-01-04','2020-01-05','2020-01-06','2020-01-07','2020-01-08','2020-01-09','2020-01-10','2020-01-11','2020-01-12','2020-01-13','2020-01-14','2020-01-15')\G*************************** 1. row *************************** id: 1 select_type: SIMPLE table: ytt_pt1_month2 partitions: p2020_01 type: ALLpossible_keys: NULL key: NULL key_len: NULL ref: NULL rows: 85498 filtered: 50.00 Extra: Using where1 row in set, 1 warning (0.00 sec)查问工夫为0.06秒，比第一种拆分办法要快10倍。
那是不是说依照年月联结来分区肯定比依照单月来分区更加优化？也不肯定，如果查问语句过滤条件蕴含分区的固定月，比方每年的12月份都要查，这时候间接按月来分区必定要优于依照年月来分区。
第三，以天为维度做拆分
依照天为维度过滤比拟频繁的查问，以天来拆分最好。比方只想查问2020年1月1日当天的数据，大抵 SQL 如下：
select * from ytt_pt1 where log_date = '2020-01-01'相似依照年月联结维度，写个脚本或者存储过程来增加分区，这里惟一要留神的一点就是 MySQL 分区表数量有限度，最大为8192个，所以如果依照天来分区，寄存10年数据，分区数量为3650个，也在限度之内。
批改下之前的存储过程，代码如下：
DELIMITER $$USE `ytt`$$DROP PROCEDURE IF EXISTS `sp_add_partition_ytt_pt1_day`$$CREATE DEFINER=`root`@`%` PROCEDURE `sp_add_partition_ytt_pt1_day`(IN f_year_start YEAR,IN f_year_end YEAR)BEGIN DECLARE v_days INT UNSIGNED DEFAULT 365; DECLARE v_year DATE DEFAULT '2010-01-01'; DECLARE v_partition_name VARCHAR(64) DEFAULT ''; DECLARE v_log_date DATE; DECLARE i,j INT UNSIGNED DEFAULT 1; SET @stmt = ''; SET @stmt_begin = 'ALTER TABLE ytt_pt1_day PARTITION BY RANGE COLUMNS (log_date)('; SET i = f_year_start; WHILE i <= f_year_end DO SET v_year = CONCAT(i,'-01-01'); SET v_days = DATEDIFF(DATE_ADD(v_year,INTERVAL 1 YEAR),v_year); SET j = 1; WHILE j <= v_days DO SET v_log_date = DATE_ADD(v_year,INTERVAL j DAY); SET v_partition_name = CONCAT('p',i,'_',LPAD(j,3,'0')); SET @stmt = CONCAT(@stmt,'PARTITION ',v_partition_name,' VALUES LESS THAN (''',v_log_date,'''),'); SET j = j + 1; END WHILE; SET i = i + 1; END WHILE; SET @stmt_end = 'PARTITION p_max VALUES LESS THAN (maxvalue))'; SET @stmt = CONCAT(@stmt_begin,@stmt,@stmt_end); PREPARE s1 FROM @stmt; EXECUTE s1; DROP PREPARE s1; SELECT NULL,NULL,NULL INTO @stmt,@stmt_begin,@stmt_end;END$$DELIMITER ;mysql> CALL sp_add_partition_ytt_pt1_day('2010','2020');Query OK, 1 row affected (14 min 13.69 sec)接下来，以天来查问工夫肯定是最短的，只有0.01秒。
mysql> select count(*) from ytt_pt1_day where log_date = '2020-01-01';+----------+| count(*) |+----------+| 2675 |+----------+1 row in set (0.01 sec)此时这样的查问要是基于年或者月性能必定不是最优。
mysql> select count(*) from ytt_pt1 where log_date = '2020-01-01';+----------+| count(*) |+----------+| 2675 |+----------+1 row in set (0.68 sec)mysql> select count(*) from ytt_pt1_month1 where log_date = '2020-01-01';+----------+| count(*) |+----------+| 2675 |+----------+1 row in set (0.87 sec)mysql> select count(*) from ytt_pt1_month2 where log_date = '2020-01-01';+----------+| count(*) |+----------+| 2675 |+----------+1 row in set (0.09 sec)能够看到，此类查问基于其余办法分区工夫显著比按天来的长。
总结下：
本篇次要讲述日常工作中最罕用的数据拆分形式：按工夫来拆分。工夫字段又常依照年，月，日三种不同的维度来具体实现，并且具体讲述了具体的实现形式以及实用场景。
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