目录
- 问题
- 验证存储空间的区别
- 1、准备两张表
- 2、准备数据
- 3、验证存储空间
- 4、结论
- 验证性能区别
- 1、验证索引覆盖查询
- 2、验证索引查询
- 3、验证全表查询和排序
- 最终结论
问题
我们在设计表结构的时候，设计规范里面有一条如下规则：对于可变长度的字段，在满足条件的前提下，尽可能使用较短的变长字段长度。为什么这么规定，主要编程客栈基于两个方面
基于存储空间的考虑
基于性能的考虑
网上说Varchar(50)和varchar(500)存储空间上是一样的，真的是这样吗？基于性能考虑，是因为过长的字段会影响到查询性能？
本文我将带着这两个问题探讨验证一下：验证存储空间的区别
1、准备两张表
CREATE TABLE `category_info_varchar_50` ( `id` bigint(20) NOT NULL AUTO_INCREMENT COMMENT '主键', `name` varchar(50) NOT NULL COMMENT '分类名称', `is_show` tinyint(4) NOT NULL DEFAULT '0' COMMENT '是否展示：0 禁用，1启用', `sort` int(11) NOT NULL DEFAULT '0' COMMENT '序号', `deleted` tinyint(1) DEFAULT '0' COMMENT '是否删除', `create_time` datetime NOT NULL COMMENT '创建时间', `update_time` datetime NOT NULL COMMENT '更新时间', PRIMARY KEY (`id`) USING BTREE, KEY `idx_name` (`name`) USING BTREE COMMENT '名称索引' ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COMMENT='分类'; CREATE TABLE `category_info_varchar_500` ( `id` bigint(20) NOT NULL AUTO_INCREMENT COMMENT '主键js', `name` varchar(500) NOT NULL COMMENT '分类名称', `is_show` tinyint(4) NOT NULL DEFAULT '0' COMMENT '是否展示：0 禁用，1启用',php `sort` int(11) NOT NULL DEFAULT '0' COMMENT '序号', `deleted` tinyint(1) DEFAULT '0' COMMENT '是否删除', `create_time` datetime NOT NULL COMMENT '创建时间', `update_time` datetime NOT NULL COMMENT '更新时间', PRIMARY KEY (`id`) USING BTREE, KEY `idx_name` (`name`) USING BTREE COMMENT '名称索引' ) ENGINE=InnoDB AUTO_INCREMENT=288135 DEFAULT CHARSET=utf8mb4 COMMENT='分类';
2、准备数据
给每张表插入相同的数据，为了凸显不同，插入100万条数据
DELIMITER $$
CREATE PROCEDURE BATchInsertData(IN total INT)
BEGIN
DECLARE start_idx INT DEFAULT 1;
DECLARE end_idx INT;
DECLARE batch_size INT DEFAULT 500;
DECLARE insert_values TEXT;
SET end_idx = LEAST(total, start_idx + batch_size - 1);
WHILE start_idx <= total DO
SET insert_values = '';
WHILE start_idx <= end_idx DO
SET insert_values = CONCAT(insert_values, CONCAT('(\'name', start_idx, '\', 0, 0, 0, NOW(), NOW()),'));
SET start_idx = start_idx + 1;
END WHILE;
SET insert_values = LEFT(insert_values, LENGTH(insert_values) - 1); -- Remove the trailing comma
SET @sql = CONCAT('INSERT INTO category_info_varchar_50 (name, is_show, sort, deleted, create_time, update_time) VALUES ', insert_values, ';');
PREPARE stmt FROM @sql;
EXECUTE stmt;
SET @sql = CONCAT('INSERT INTO category_info_varchar_500 (name, is_show, sort, deleted, create_time, update_time) VALUES ', insert_values, ';');
PREPARE stmt FROM @sql;
EXECUTE stmt;
SET end_idx = LEAST(total, start_idx + batch_size - 1);
END WHILE;
END$$
DELIMITER ;
CALL batchInsertData(1000000);
3、验证存储空间
查询第一张表SQL
SELECT
table_schema AS "数据库",
table_name AS "表名",
table_rows AS "记录数",
TRUNCATE ( data_length / 1024 / 1024, 2 ) AS "数据容量（MB）",
TRUNCATE ( index_length / 1024 / 1024, 2 ) AS "索引容量（MB）"
FROM
information_schema.TABLES
WHERE
table_schema = 'test_mysql_field'
and TABLE_NAME = 'category_info_varchar_50'
ORDER BY
data_length DESC,
index_length DESC;
查询结果
查询第二张表SQL
SELECT
table_schema AS "数据库",
table_name AS "表名",
table_rows AS "记录数",
TRUNCATE ( data_length / 1024 / 1024, 2 ) AS "数据容量（MB）",
TRUNCATE ( index_length / 1024 / 1024, 2 ) AS "索引容量（MB）"
FROM
information_schema.TABLES
WHERE
table_schema = 'test_mysql_field'
and TABLE_NAME = 'category_info_varchar_500'
ORDER BY
data_length DESC,
index_length DESC;
查询结果
4、结论
两张表在占用空间上确实是一样的，并无差别。
验证性能区别
1、验证索引覆盖查询
select name from category_info_varchar_50 where name = 'name100000' -- 耗时0.012s select name from category_info_varchar_500 where name = 'name100000' -- 耗时0.012s select name from category_info_varchar_50 order by name; -- 耗时0.370s select name from category_info_varchar_500 order by name; -- 耗时0.379s
通过索引覆盖查询性能差别不大
2、验证索引查询
select * from category_info_varchar_50 where na编程me = 'name100000'
--耗时 0.012s
select * from category_info_varchar_500 where name = 'name100000'
--耗时 0.012s
select * from category_info_varchar_50 where name in('name100','name1000','name100000','name10000','name1100000',
'name200','name2000','name200000','name20000','name2200000','name300','name3000','name300000','name30000','name3300000',
'name400','name4000','name400000','name40000','name4400000','name500','name5000','name500000','name50000','name5500000',
'name600','name6000','name600000','name60000','name6600000','name700','name7000','name700000','name70000','name7700000','name800',
'name8000','name800000','name80000','name6600000','name900','name9000','name900000','name90000','name9900000')
-- 耗时 0.011s -0.014s
-- 增加 order by name 耗时 0.012s - 0.015s
select * from category_info_varchar_50 where name in('name100','name1000','name100000','name10000','name1100000',
'name200','name2000','name200000','name20000','name2200000','name300','name3000','name300000','name30000','name3300000',
'name400','name4000','name400000','name40000','name4400000','name500','name5000','name500000','name50000','name5500000',
'name600','name6000','name600000','name60000','name6600000','name700','name7000','name700000','name70000','name7700000','name800',
'name8000','name800000','name80000','name6600000','name900','name9000','name900000','name90000','name9900000')
-- 耗时 0.012s -0.014s
-- 增加 order by name 耗时 0.014s - 0.017s
索引范围查询性能基本相同, 增加了order By后开始有一定性能差别；
3、验证全表查询和排序
全表无排序
全python表有排序
select * from category_info_varchar_50 order by name ; --耗时 1.498s select * from category_info_varchar_500 order by name ; --耗时 4.875s
结论：
全表扫描无排序情况下,两者性能无差异,在全表有排序的情况下, 两种性能差异巨大；
分析原因
varchar50 全表执行sql分析
我发现86%的时花在数据传输上,接下来我们看状态部分,关注Created_tmp_files和sort_merge_passes
Created_tmp_files为3
sort_merge_passes为95
varchar500 全表执行sql分析
增加了临时表排序
Created_tmp_files 为 4
sort_merge_passes为645
关于sort_merge_passes, Mysql给出了如下描述：
Number of merge passes that the sort algorithm has had to do. If this value is large, you may want to increase the value of the sort_buffer_size.
其实sort_merge_passes对应的就是MySQL做归并排序的次数，也就是说，如果sort_merge_passes值比较大，说明sort_buffer和要排序的数据差距越大，我们可以通过增大sort_buffer_size或者让填入sort_buffer_size的键值对更小来缓解sort_merge_passes归并排序的次数。
最终结论
至此，我们不难发现，当我们最该字段进行排序操作的时候，Mysql会根据该字段的设计的长度进行内存预估，如果设计过大的可变长度，会导致内存预估的值超出sort_buffer_size的大小，导致mysql采用磁盘临时文件排序,最终影响查询性能。
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