第38期:MySQL 时间类分区具体实现

适用分区或者说分表最多的场景依然是针对时间字段做拆分, 这节我们详细讲讲如何更好的基于时间字段来拆分。分别按照年、月、日几个维度的实现方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选择跟业务检索请求密切相关。比如保留10年数据,每次查询基于某个具体年份做为过滤条件,那按照年拆分肯定最好。例如下面SQL:

select * from ytt_pt1 where log_date >='2018-01-01' and log_date < '2019-01-01';

那我们来看下按照年单独拆分的实际例子:表ytt_pt1 ,包含1000W条记录,以年为粒度建立分区表。

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: 0

mysql> 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询效果:以下SQL 直接走分区p0008,查询时间0.91秒, 这个时间不算短,后期可以增加过滤条件来减少查询时间。

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: ALL
possible_keys: NULL
          key: NULL
      key_len: NULL
          ref: NULL
         rows: 998002
     filtered: 11.11
        Extra: Using where
1 row in set, 1 warning (0.00 sec)

需要注意:查询只能基于字段来直接过滤,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,处理方法和单表查询一样。比如语句:

select count(*) from ytt_pt1 where year(log_date) = '2018' ;

看下执行情况: MySQL扫描所有分区,查询执行时间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: ALL
possible_keys: NULL
          key: NULL
      key_len: NULL
          ref: NULL
         rows: 9982648
     filtered: 100.00
        Extra: Using where
1 row in set, 1 warning (0.00 sec)

如果非坚持这种写法,可以给优化器一个提示,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拟列:

mysql> select count(*) from ytt_pt1 partition(p0008) where year(log_date) = '2018' ;
+----------+
| count(*) |
+----------+
|  1000204 |
+----------+
1 row in set (0.84 sec)

如果查询按照月作为维度过滤比较频繁,那肯定是按照月来拆最好;比如需要检索2020年当月的某些记录来做后续数据处理,大致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
按照月来拆分,有以下两种写法:

第一种: 直接按照月来拆12个分区: 下面表 ytt_pt1_month1 分区类型为 LIST ,基于函数 month 直接计算。

mysql> show create table ytt_pt1_month1\G
*************************** 1. row ***************************
       Table: ytt_pt1_month1
Create 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)

比如要查询2020年前半个月的记录:查询限定在分区 p0001 里,但是时间不太理想,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: ALL
possible_keys: NULL
          key: NULL
      key_len: NULL
          ref: NULL
         rows: 848224
     filtered: 50.00
        Extra: Using where
1 row in set, 1 warning (0.00 sec)

第二种: 对于每年的数据,单独划分12个分区,也就是按照年月联合维度来分区,一共有 144 个分区,每个分区对应具体某一年某一月数据。

添加这个分区稍微麻烦些,克隆表 ytt_pt1_month1 为 ytt_pt1_month2 ,这里写个存储过程来添加分区信息:

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)

结果类似这样:

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,来观察下刚才那个获取2020年前半个月记录的查询:

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: ALL
possible_keys: NULL
          key: NULL
      key_len: NULL
          ref: NULL
         rows: 85498
     filtered: 50.00
        Extra: Using where
1 row in set, 1 warning (0.00 sec)

查询时间为0.06秒,比第一种拆分方法要快10倍。

那是不是说按照年月联合来分区一定比按照单月来分区更加优化?也不一定,如果查询语句过滤条件包含分区的固定月,比如每年的12月份都要查,这时候直接按月来分区肯定要优于按照年月来分区。

第三,以天为维度做拆分

按照天为维度过滤比较频繁的查询,以天来拆分最好。比如只想查询2020年1月1日当天的数据,大致 SQL 如下:

select * from ytt_pt1 where log_date = '2020-01-01'

类似按照年月联合维度,写个脚本或者存储过程来添加分区,这里唯一要注意的一点就是 MySQL 分区表数量有限制,最大为8192个,所以如果按照天来分区,存放10年数据,分区数量为3650个,也在限制之内。

修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,以天来查询时间一定是最短的,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)

此时这样的查询要是基于年或者月性能肯定不是最优。

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)

可以看到,此类查询基于其他方法分区时间明显比按天来的长。

总结下:

本篇主要讲述日常工作中最常用的数据拆分方式:按时间来拆分。时间字段又常按照年,月,日三种不同的维度来具体实现,并且详细讲述了具体的实现方式以及适用场景。


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