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Mysql慢查询优化
来源:cnblogs  作者:realeo  时间:2023/9/15 13:31:52  对本文有异议

Mysql慢查询优化实战

效果:效率提升十倍左右

  • 优化前

    mysql> use test_old;
    Database changed
    mysql> set profiling = 'ON';
    Query OK, 0 rows affected
    
    mysql> show variables like 'profiling';
    +---------------+-------+
    | Variable_name | Value |
    +---------------+-------+
    | profiling     | ON    |
    +---------------+-------+
    1 row in set
    
    mysql> show profiles;
    +----------+------------+---------------------------------------------------------------------------------------------+
    | Query_ID | Duration   | Query                                                                                       |
    +----------+------------+---------------------------------------------------------------------------------------------+
    |        1 |    0.00419 | show variables like 'profiling'                                                             |
    |        2 | 1.78590175 | SELECT * FROM `table_test` where test_id in ('99863885543', '99863900221', '99821363824') |
    +----------+------------+---------------------------------------------------------------------------------------------+
    2 rows in set
    
    mysql> EXPLAIN SELECT * FROM `table_test` where test_id in ('99863885543', '99863900221', '99821363824');
    +----+-------------+----------------------+------------+-------+---------------+----------+---------+------+--------+----------+----------------------------------+
    | id | select_type | table                | partitions | type  | possible_keys | key      | key_len | ref  | rows   | filtered | Extra                            |
    +----+-------------+----------------------+------------+-------+---------------+----------+---------+------+--------+----------+----------------------------------+
    |  1 | SIMPLE      | table_test | NULL       | range | test_id      | test_id | 33      | NULL | 170496 |      100 | Using index condition; Using MRR |
    +----+-------------+----------------------+------------+-------+---------------+----------+---------+------+--------+----------+----------------------------------+
    1 row in set
    
  • 优化后

    mysql> use test;
    Database changed
    mysql> set profiling = 'ON';
    Query OK, 0 rows affected
    
    mysql> show variables like 'profiling';
    +---------------+-------+
    | Variable_name | Value |
    +---------------+-------+
    | profiling     | ON    |
    +---------------+-------+
    1 row in set
    
    mysql> show profiles;
    +----------+-----------+---------------------------------------------------------------------------------------------+
    | Query_ID | Duration  | Query                                                                                       |
    +----------+-----------+---------------------------------------------------------------------------------------------+
    |        1 | 0.0060565 | show variables like 'profiling'                                                             |
    |        2 | 0.1755525 | SELECT * FROM `table_test` where test_id in ('99863885543', '99863900221', '99821363824') |
    +----------+-----------+---------------------------------------------------------------------------------------------+
    2 rows in set
    
    mysql> EXPLAIN SELECT * FROM `table_test` where test_id in ('99863885543', '99863900221', '99821363824');
    +----+-------------+----------------------+--------------------------------------------------------------------------------------+-------+------------------+---------+---------+------+--------+----------+-------------+
    | id | select_type | table                | partitions                                                                           | type  | possible_keys    | key     | key_len | ref  | rows   | filtered | Extra       |
    +----+-------------+----------------------+--------------------------------------------------------------------------------------+-------+------------------+---------+---------+------+--------+----------+-------------+
    |  1 | SIMPLE      | table_test | p20221126,p20221127,p20221128,p20221129,p20221130,p20221201,p20221202,p20221203,pmax | range | PRIMARY,test_id | PRIMARY | 32      | NULL | 185501 |      100 | Using where |
    +----+-------------+----------------------+--------------------------------------------------------------------------------------+-------+------------------+---------+---------+------+--------+----------+-------------+
    1 row in set
    

说说在 MySQL 中一条查询 SQL 是如何执行的

  1. 取得链接,使用使用到 MySQL 中的连接器。
  2. 查询缓存,key 为 SQL 语句,value 为查询结果,如果查到就直接返回。不建议使用次缓存, 在 MySQL 8.0 版本已经将查询缓存删除,也就是说 MySQL 8.0 版本后不存在此功能。
  3. 分析器,分为词法分析和语法分析。此阶段只是做一些 SQL 解析,语法校验。所以一般语法错 误在此阶段。
  4. 优化器,是在表里有多个索引的时候,决定使用哪个索引;或者一个语句中存在多表关联的时 候(join),决定各个表的连接顺序。
  5. 执行器,通过分析器让 SQL 知道你要干啥,通过优化器知道该怎么做,于是开始执行语句。执 行语句的时候还要判断是否具备此权限,没有权限就直接返回提示没有权限的错误;有权限则 打开表,根据表的引擎定义,去使用这个引擎提供的接口,获取这个表的第一行,判断 id 是都 等于 1。如果是,直接返回;如果不是继续调用引擎接口去下一行,重复相同的判断,直到取 到这个表的最后一行,最后返回。

慢sql定位

table_test表信息

-- ----------------------------
-- Table structure for table_test
-- ----------------------------
DROP TABLE IF EXISTS `table_test`;
CREATE TABLE `table_test` (
  `id` int(11) NOT NULL AUTO_INCREMENT COMMENT 'id',
  `test_id` varchar(10) DEFAULT NULL COMMENT '场景id',
  `data_time` timestamp NULL DEFAULT NULL COMMENT '数据时间',
  `service_id` varchar(50) DEFAULT NULL COMMENT '流量类型',
  `total_traffic` varchar(50) DEFAULT NULL COMMENT '总流量',
  `ul_traffic` varchar(50) DEFAULT NULL COMMENT '上行流量',
  `dl_traffic` varchar(50) DEFAULT NULL COMMENT '下行流量',
  `tcp_conn_req_times` varchar(50) DEFAULT NULL COMMENT 'TCP连接请求次数',
  `tcp_conn_succ_times` varchar(50) DEFAULT NULL COMMENT 'TCP连接成功次数',
  `tcp_conn_succ_rat` varchar(50) DEFAULT NULL COMMENT 'TCP连接成功次率',
  `tcp_conn_total_delay` varchar(50) DEFAULT '0' COMMENT 'TCP连接建立总时长',
  `tcp_conn_avg_delay` varchar(50) DEFAULT '0' COMMENT 'TCP连接建立平均时延',
  `tcp_ul_rtt_total_delay` varchar(50) DEFAULT '0' COMMENT 'TCP上行RTT总时延',
  `tcp_dl_rtt_total_delay` varchar(50) DEFAULT '0' COMMENT 'TCP下行RTT总时延',
  `tcp_ul_rtt_stat_times` varchar(50) DEFAULT NULL COMMENT 'TCP上行RTT总次数',
  `tcp_dl_rtt_stat_times` varchar(50) DEFAULT NULL COMMENT 'TCP下行RTT总次数',
  `tcp_ul_rtt_avg_delay` varchar(50) DEFAULT '0' COMMENT 'TCP上行RTT平均时延',
  `tcp_dl_rtt_avg_delay` varchar(50) DEFAULT '0' COMMENT 'TCP下行RTT平均时延',
  `day_id` varchar(50) DEFAULT NULL COMMENT 'day_id',
  PRIMARY KEY (`id`) USING BTREE,
  KEY `test_id` (`test_id`) USING BTREE,
  KEY `data_time` (`data_time`) USING BTREE
) ENGINE=InnoDB AUTO_INCREMENT=19671082 DEFAULT CHARSET=utf8 ROW_FORMAT=DYNAMIC COMMENT='热门app';
  1. 开启slow_query_log

    mysql> set global slow_query_log = 'ON';
    Query OK, 0 rows affected
    
    mysql> show variables like 'slow_query_log%';
    +---------------------+------------------------------------------+
    | Variable_name       | Value                                    |
    +---------------------+------------------------------------------+
    | slow_query_log      | ON                                       |
    | slow_query_log_file | /usr/local/mysql/data/hadoop102-slow.log |
    +---------------------+------------------------------------------+
    2 rows in set
    
  2. 修改long_query_time阈值

    mysql> set global long_query_time = 0.5;
    Query OK, 0 rows affected
    
    -- 经过测试,发现设置global时,只针对新的会话有效,对当前会话无效。
    -- 所以还需要针对当前会话设置一次。
    mysql> set long_query_time = 0.5;
    Query OK, 0 rows affected
    
    mysql> show variables like 'long_query_time';
    +-----------------+----------+
    | Variable_name   | Value    |
    +-----------------+----------+
    | long_query_time | 0.500000 |
    +-----------------+----------+
    1 row in set
    
    mysql> 
    

执行超过配置时间的慢sql查看日志

[realeo@hadoop102 ~]$ cd /usr/local/mysql/data/
[realeo@hadoop102 data]$ sudo cat hadoop102-slow.log
/usr/sbin/mysqld, Version: 5.7.27 (MySQL Community Server (GPL)). started with:
Tcp port: 3306  Unix socket: /var/lib/mysql/mysql.sock
Time                 Id Command    Argument
# Time: 2023-04-03T14:46:51.877547Z
# User@Host: root[root] @  [192.168.10.1]  Id:   118
# Query_time: 1.284210  Lock_time: 0.000317 Rows_sent: 91767  Rows_examined: 91767
use test;
SET timestamp=1680533211;
SELECT * FROM `table_test` where test_id in ('99863885543', '99863900221', '99821363824');

慢Sql分析

  1. 开启show profile

    -- 仅对当前会话开启
    mysql> set profiling = 'ON';
    Query OK, 0 rows affected
    
    mysql> show variables like 'profiling';
    +---------------+-------+
    | Variable_name | Value |
    +---------------+-------+
    | profiling     | ON    |
    +---------------+-------+
    1 row in set
    
  2. 查看会话中sql执行情况

    mysql> show profiles;
    +----------+------------+---------------------------------------------------------------------------------------------+
    | Query_ID | Duration   | Query                                                                                       |
    +----------+------------+---------------------------------------------------------------------------------------------+
    |        1 | 0.00193025 | show variables like 'profiling'                                                             |
    |        2 |   2.095192 | SELECT * FROM `table_test` where test_id in ('99863885543', '99863900221', '99821363824') |
    +----------+------------+---------------------------------------------------------------------------------------------+
    2 rows in set
    
  3. 查看当前会话某条sql执行记录的资源消耗情况

    mysql> show profile cpu, block io for query 2;
    +----------------------+----------+----------+------------+--------------+---------------+
    | Status               | Duration | CPU_user | CPU_system | Block_ops_in | Block_ops_out |
    +----------------------+----------+----------+------------+--------------+---------------+
    | starting             | 0.000125 | 6.8E-5   | 4.5E-5     |            0 |             0 |
    | checking permissions | 1E-5     | 5E-6     | 3E-6       |            0 |             0 |
    | Opening tables       | 0.00114  | 0.001145 | 0          |            0 |             0 |
    | init                 | 5.1E-5   | 3.1E-5   | 1.6E-5     |            0 |             0 |
    | System lock          | 1E-5     | 6E-6     | 4E-6       |            0 |             0 |
    | optimizing           | 1E-5     | 6E-6     | 4E-6       |            0 |             0 |
    | statistics           | 0.000519 | 0.000521 | 0          |            0 |             0 |
    | preparing            | 1.8E-5   | 1.6E-5   | 0          |            0 |             0 |
    | executing            | 3E-6     | 2E-6     | 0          |            0 |             0 |
    | Sending data         | 2.093149 | 1.301995 | 0.998561   |            0 |           384 |
    | end                  | 4.5E-5   | 0        | 1.9E-5     |            0 |             0 |
    | query end            | 1.2E-5   | 0        | 1.2E-5     |            0 |             0 |
    | closing tables       | 1.7E-5   | 0        | 1.7E-5     |            0 |             0 |
    | freeing items        | 2E-5     | 0        | 2E-5       |            0 |             0 |
    | logging slow query   | 5.1E-5   | 0        | 5.1E-5     |            0 |             0 |
    | cleaning up          | 1.5E-5   | 0        | 1.4E-5     |            0 |             0 |
    +----------------------+----------+----------+------------+--------------+---------------+
    16 rows in set
    

    show profile常用查询参数:

    • all:显示所有的开销信息。
    • block io:显示块io开销。
    • context switches: 上下文切换开销。
    • cpu:显示cpu开销信息。
    • ipc:显示发送和接受开销信息。
    • memory:显示内存开销信息。
    • page faults:显示页面错误开销信息。
    • source:显示和 source_function,source_file,source_line 相关的开销信息。
    • swaps:显示交换次数开销信息。

存储引擎/索引结构选择

Hash索引与B+树索引的区别

  1. Hash索引不能进行范围性的一个查找,因为hash指向的数据是无序的,而B+树的叶子节点是个有序的链表。Hash索引仅能满足(=、<>)和in查询。如果进行范围查询,哈希型索引,时间复杂化会退化为O(n)而树型的有序特性,依然能保持O(log2n)的高效率
  2. Hash索引不支持联合索引的最左侧原则(即联合索引的部分索引无法使用),而B+树可以。对于联合索引来说,Hash索引在计算Hash值得时候将索引键合并后再一起计算Hash值,所以不会针对每个索引单独计算hash值。因此如果用到联合索引的一个或者多个索引时,无法被利用。
  3. Hash不支持OrderBy排序,以为Hash索引指向的数据无序,因此无法起到排序的作用。而B+树索引数据是有序的,可以起到对该字段order by排序优化的作用,同理,我们也无法对hash索引进行模糊查找,而B+树使用模糊查询的方式时,like后面后模糊查询的话就可以起到优化作用。
  4. 对于InnoDB的哈希索引,确切的应该这么说:
    1. InnoDB用户无法手动创建哈希索引,这一层上说,InnoDB确实不支持哈希索引;
    2. InnoDB会自调优(self-tuning),如果判定建立自适应哈希索引(Adaptive Hash Index, AHI),能够提升查询效率,InnoDB自己会建立相关哈希索引,这一层上说,InnoDB又是支持哈希索引的;
mysql> show profiles;
+----------+------------+---------------------------------------------------------------------------------------------+
| Query_ID | Duration   | Query                                                                                       |
+----------+------------+---------------------------------------------------------------------------------------------+
|        1 | 0.00187275 | show variables like 'profiling'                                                             |
|        2 | 1.63446275 | SELECT * FROM `table_test` where test_id in ('99863885543', '99863900221', '99821363824') |
+----------+------------+---------------------------------------------------------------------------------------------+
2 rows in set

mysql> show index from `table_test`;
+----------------------+------------+-----------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+
| Table                | Non_unique | Key_name  | Seq_in_index | Column_name | Collation | Cardinality | Sub_part | Packed | Null | Index_type | Comment | Index_comment |
+----------------------+------------+-----------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+
| table_test |          0 | PRIMARY   |            1 | id          | A         |     1302941 | NULL     | NULL   |      | BTREE      |         |               |
| table_test |          1 | test_id  |            1 | test_id    | A         |          94 | NULL     | NULL   | YES  | BTREE      |         |               |
| table_test |          1 | data_time |            1 | data_time   | A         |          75 | NULL     | NULL   | YES  | BTREE      |         |               |
+----------------------+------------+-----------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+
3 rows in set

字段设计优化

  • 字段类型:确认长度的字段采用char类型
  • 字段长度:索引字段即常用区分字段尽量简短
mysql> SELECT max(LENGTH(test_id))  FROM `table_test`;
+-----------------------+
| max(LENGTH(test_id)) |
+-----------------------+
|                     8 |
+-----------------------+
1 row in set

mysql> show profiles;
+----------+------------+---------------------------------------------------------------------------------------------+
| Query_ID | Duration   | Query                                                                                       |
+----------+------------+---------------------------------------------------------------------------------------------+
|        1 | 0.00324825 | show variables like 'profiling'                                                             |
|        2 |  12.979875 | alter table table_test modify test_id char(8)                                    |
|        3 |   1.683254 | SELECT * FROM `table_test` where test_id in ('99863885543', '99863900221', '99821363824') |
+----------+------------+---------------------------------------------------------------------------------------------+
3 rows in set

查询语句优化

  • 查询语句的优化对于MySQL大数据查询速度的提升非常重要。应该避免使用SELECT *,因为这会导致MySQL检索整个表的所有列,从而降低查询速度。应该只查询需要的列,并使用WHERE子句限制检索的行数。

  • MySQL组合索引(复合索引)的最左优先原则。最左优先就是说组合索引的第一个字段必须出现在查询组句中,这个索引才会被用到。只要组合索引最左边第一个字段出现在Where中,那么不管后面的字段出现与否或者出现顺序如何,MySQL引擎都会自动调用索引来优化查询效率。

  • 在创建多列索引时,要根据业务需求,where 子句中使用最频繁的一列放在最左边。

索引字段优化

  • 大多数情况下通过test_id来查询,根据此字段建索引
-- 查看当前表信息
show create table table_test;

-- 创建新增索引
ALTER TABLE table_test ADD INDEX test_id_idx (test_id(8));
  • 其次可根据查询场景合理建立组合索引

使用EXPLAIN分析

含义可参考:https://blog.csdn.net/jibaole/article/details/121293188

mysql> EXPLAIN SELECT * FROM `table_test` where test_id in ('99863885543', '99863900221', '99821363824');
+----+-------------+----------------------+------------+-------+---------------+----------+---------+------+--------+----------+----------------------------------+
| id | select_type | table                | partitions | type  | possible_keys | key      | key_len | ref  | rows   | filtered | Extra                            |
+----+-------------+----------------------+------------+-------+---------------+----------+---------+------+--------+----------+----------------------------------+
|  1 | SIMPLE      | table_test | NULL       | range | test_id      | test_id | 33      | NULL | 163326 |      100 | Using index condition; Using MRR |
+----+-------------+----------------------+------------+-------+---------------+----------+---------+------+--------+----------+----------------------------------+
1 row in set
  • 可用于分析常见索引失效问题,例如字符串字段作为索引时需要在where中加单引号''
mysql> EXPLAIN SELECT * FROM `table_test` where test_id in (99863885543,99863900221,99821363824);
+----+-------------+----------------------+------------+------+---------------+------+---------+------+---------+----------+-------------+
| id | select_type | table                | partitions | type | possible_keys | key  | key_len | ref  | rows    | filtered | Extra       |
+----+-------------+----------------------+------------+------+---------------+------+---------+------+---------+----------+-------------+
|  1 | SIMPLE      | table_test | NULL       | ALL  | test_id      | NULL | NULL    | NULL | 1397231 |       30 | Using where |
+----+-------------+----------------------+------------+------+---------------+------+---------+------+---------+----------+-------------+
1 row in set

分区优化

分区表是将大表分成小表的一种方法。在处理大数据时,使用分区表可以大大提高查询速度。分区表将数据分成多个分区,每个分区可以独立地进行查询。当进行查询时,MySQL只需要扫描需要的分区,而不是整个表。

  1. 在进行自动增加分区前一定得先对表手动分几个区

    -- 创建复合主键
    alter table table_test drop primary key,add primary key(`test_id`,`data_time`,`id`);
    
    ALTER TABLE table_test PARTITION BY RANGE (UNIX_TIMESTAMP(data_time))(
    	PARTITION p20221126
    	VALUES
    		LESS THAN (
    			UNIX_TIMESTAMP('2022-11-27')
    		),
    		PARTITION p20221127
    	VALUES
    		LESS THAN (
    			UNIX_TIMESTAMP('2022-11-28')
    		),
    		PARTITION p20221128
    	VALUES
    		LESS THAN (
    			UNIX_TIMESTAMP('2022-11-29')
    		),
    		PARTITION p20221129
    	VALUES
    		LESS THAN (
    			UNIX_TIMESTAMP('2022-11-30')
    		)
    )
    
    -- 如果有大于分区上限的值想插入表中,系统会返还错误,为了兼容这种情况,我们可以新增一个分区,上限为maxvalue。所有大于当前上限的值都会放入这个分区:
    alter table table_test add partition(partition pmax values less than(maxvalue));
    ALTER TABLE table_test ADD PARTITION (PARTITION p20221130 VALUES LESS THAN (TO_DAYS ('2022-11-30')))
    
    -- 删除分区,同时清除历史数据
    alter table table_test drop partition p20221127;
    
  2. 查询表分区信息

    mysql> SELECT PARTITION_NAME,PARTITION_METHOD,PARTITION_EXPRESSION,PARTITION_DESCRIPTION,
    
    TABLE_ROWS,SUBPARTITION_NAME,SUBPARTITION_METHOD,SUBPARTITION_EXPRESSION
    
    FROM information_schema.PARTITIONS
    
    WHERE TABLE_SCHEMA=SCHEMA() AND TABLE_NAME='table_test';
    +----------------+------------------+---------------------------+-----------------------+------------+-------------------+---------------------+-------------------------+
    | PARTITION_NAME | PARTITION_METHOD | PARTITION_EXPRESSION      | PARTITION_DESCRIPTION | TABLE_ROWS | SUBPARTITION_NAME | SUBPARTITION_METHOD | SUBPARTITION_EXPRESSION |
    +----------------+------------------+---------------------------+-----------------------+------------+-------------------+---------------------+-------------------------+
    | p20221126      | RANGE            | UNIX_TIMESTAMP(data_time) | 1669536000            |     470450 | NULL              | NULL                | NULL                    |
    | p20221127      | RANGE            | UNIX_TIMESTAMP(data_time) | 1669622400            |     378562 | NULL              | NULL                | NULL                    |
    | p20221128      | RANGE            | UNIX_TIMESTAMP(data_time) | 1669708800            |     419724 | NULL              | NULL                | NULL                    |
    | p20221129      | RANGE            | UNIX_TIMESTAMP(data_time) | 1669795200            |     135171 | NULL              | NULL                | NULL                    |
    +----------------+------------------+---------------------------+-----------------------+------------+-------------------+---------------------+-------------------------+
    4 rows in set
    
    -- 查询指定分区数据
    SELECT * FROM `table_test` PARTITION(p20221129) where test_id in ('99863885543', '99863900221', '99821363824');
    
  3. 按天自动分区存储过程

    DELIMITER $$
     
     -- 切换数据库test
     USE `test`$$
     
     DROP PROCEDURE IF EXISTS `create_partition_by_day`$$
     
     CREATE DEFINER=`root`@`%` PROCEDURE `create_partition_by_day`()
     BEGIN
     /* 事务回滚,其实放这里没什么作用,ALTER TABLE是隐式提交,回滚不了的。*/
         DECLARE EXIT HANDLER FOR SQLEXCEPTION ROLLBACK;
         START TRANSACTION;
     
     /* 到系统表查出这个表的倒数第二大分区,得到分区的日期。在创建分区的时候,名称就以日期格式存放,方便后面维护 */
         SELECT REPLACE(partition_name,'p','') INTO @P12_Name FROM INFORMATION_SCHEMA.PARTITIONS 
         WHERE table_name='table_test' ORDER BY partition_ordinal_position DESC LIMIT 1,1;
          SET @Max_date= DATE(DATE_ADD(@P12_Name+0, INTERVAL 1 DAY))+0;
     /* 修改表,在最大分区的后面增加一个分区,时间范围加1天 */
         SET @s1=CONCAT('ALTER TABLE table_test REORGANIZE PARTITION pmax INTO (PARTITION p',@Max_date,' VALUES LESS THAN (UNIX_TIMESTAMP (''',DATE(@Max_date+1),''')),partition pmax values less than(maxvalue))');
         /* 输出查看增加分区语句*/
         SELECT @s1;
         PREPARE stmt2 FROM @s1;
         EXECUTE stmt2;
         DEALLOCATE PREPARE stmt2;
     /* 取出最小的分区的名称,并删除掉 。
         注意:删除分区会同时删除分区内的数据,慎重 */
         /*select partition_name into @P0_Name from INFORMATION_SCHEMA.PARTITIONS
         where table_name='table_test' order by partition_ordinal_position limit 1;
         SET @s=concat('ALTER TABLE table_test DROP PARTITION ',@P0_Name);
         PREPARE stmt1 FROM @s;
         EXECUTE stmt1;
         DEALLOCATE PREPARE stmt1; */
     /* 提交 */
         COMMIT ;
      END$$
     
     DELIMITER ;
    
  4. 增加事件执行

    -- 开启任务定时器
    mysql> SET GLOBAL event_scheduler = ON;
    Query OK, 0 rows affected
    
    mysql> SHOW VARIABLES LIKE 'event_scheduler';
    +-----------------+-------+
    | Variable_name   | Value |
    +-----------------+-------+
    | event_scheduler | ON    |
    +-----------------+-------+
    1 row in set
    
    -- 事件定义
    DELIMITER ||
     CREATE EVENT Partition_by_day_event
               ON SCHEDULE
               EVERY 1 day STARTS '2022-11-29 07:00:00'
               DO
           BEGIN  
     
               CALL test.`create_partition_by_day`;  
     
      END ||
     DELIMITER ;  
    

配置可参考:https://www.bbsmax.com/A/gAJG7rZJZR/

性能可参考:https://www.cnblogs.com/mzhaox/p/11201715.html

使用缓存

  • Redis
    • 性能极高 – Redis 能读的速度是 110000 次/s,写的速度是 81000 次 /s 。
    • 基于内存操作,C语言实现,因此相对于Mysql等一些常见关系型数据库基于硬盘存储,大量的I/O操作效率更加高效。

优化服务器硬件

优化服务器硬件可以提高MySQL大数据查询速度。应该使用更快的CPU、更大的内存和更快的硬盘。MySQL可以更快地读取和处理数据。

架构设计

  • 能否根据业务,对该大表使用例如MyCat,对表进行拆分。不过可能在设计上较复杂,且会引入其他问题。
    微信公众号搜索:余生还长着呢

原文链接:https://www.cnblogs.com/realeo/p/17701978.html

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