Interviewer: How to quickly query tens of millions of data?

Let’s look at an interview scenario first:

• Interviewer: Let’s talk about 10 million data. How did you inquire?
• Little brother: Query directly by paging, use limit paging.
• Interviewer: Have you ever done it in practice?
• Little brother: There must be one

Maybe some friends have never encountered a table with tens of millions of data, and they don’t know how to query it. What happens when there is tens of millions of data?

Today I will show you how to practice. This time it is based on the MySQL 5.7.26 version for testing

Preparing data

What should I do if I don’t have 10 million data?

Create it

Code to create 10 million? That's impossible, it's too slow, and it might take a whole day. You can use database scripts to execute much faster.

Create table

CREATE TABLE `user_operation_log`  (
  `id` int(11) NOT NULL AUTO_INCREMENT,
  `user_id` varchar(64) CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci NULL DEFAULT NULL,
  `ip` varchar(20) CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci NULL DEFAULT NULL,
  `op_data` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci NULL DEFAULT NULL,
  `attr1` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci NULL DEFAULT NULL,
  `attr2` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci NULL DEFAULT NULL,
  `attr3` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci NULL DEFAULT NULL,
  `attr4` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci NULL DEFAULT NULL,
  `attr5` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci NULL DEFAULT NULL,
  `attr6` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci NULL DEFAULT NULL,
  `attr7` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci NULL DEFAULT NULL,
  `attr8` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci NULL DEFAULT NULL,
  `attr9` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci NULL DEFAULT NULL,
  `attr10` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci NULL DEFAULT NULL,
  `attr11` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci NULL DEFAULT NULL,
  `attr12` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci NULL DEFAULT NULL,
  PRIMARY KEY (`id`) USING BTREE
) ENGINE = InnoDB AUTO_INCREMENT = 1 CHARACTER SET = utf8mb4 COLLATE = utf8mb4_general_ci ROW_FORMAT = Dynamic;

Create data script

Using batch insertion, the efficiency will be much faster, and every 1000 items will be committed. If the amount of data is too large, it will also lead to batch insertion. Insertion efficiency is slow

DELIMITER ;;
CREATE PROCEDURE batch_insert_log()
BEGIN
  DECLARE i INT DEFAULT 1;
  DECLARE userId INT DEFAULT 10000000;
 set @execSql = 'INSERT INTO `test`.`user_operation_log`(`user_id`, `ip`, `op_data`, `attr1`, `attr2`, `attr3`, `attr4`, `attr5`, `attr6`, `attr7`, `attr8`, `attr9`, `attr10`, `attr11`, `attr12`) VALUES';
 set @execData = '';
  WHILE i<=10000000 DO
   set @attr = "&#39;测试很长很长很长很长很长很长很长很长很长很长很长很长很长很长很长很长很长的属性&#39;";
  set @execData = concat(@execData, "(", userId + i, ", &#39;10.0.69.175&#39;, &#39;用户登录操作&#39;", ",", @attr, ",", @attr, ",", @attr, ",", @attr, ",", @attr, ",", @attr, ",", @attr, ",", @attr, ",", @attr, ",", @attr, ",", @attr, ",", @attr, ")");
  if i % 1000 = 0
  then
     set @stmtSql = concat(@execSql, @execData,";");
    prepare stmt from @stmtSql;
    execute stmt;
    DEALLOCATE prepare stmt;
    commit;
    set @execData = "";
   else
     set @execData = concat(@execData, ",");
   end if;
  SET i=i+1;
  END WHILE;

END;;
DELIMITER ;

开始测试

田哥的电脑配置比较低：win10 标压渣渣i5 读写约500MB的SSD

由于配置低，本次测试只准备了3148000条数据，占用了磁盘5G(还没建索引的情况下)，跑了38min，电脑配置好的同学，可以插入多点数据测试

SELECT count(1) FROM `user_operation_log`

返回结果：3148000

三次查询时间分别为：

• 14060 ms
• 13755 ms
• 13447 ms

普通分页查询

MySQL 支持 LIMIT 语句来选取指定的条数数据， Oracle 可以使用 ROWNUM 来选取。

MySQL分页查询语法如下：

SELECT * FROM table LIMIT [offset,] rows | rows OFFSET offset

• 第一个参数指定第一个返回记录行的偏移量
• 第二个参数指定返回记录行的最大数目

下面我们开始测试查询结果：

SELECT * FROM `user_operation_log` LIMIT 10000, 10

查询3次时间分别为：

• 59 ms
• 49 ms
• 50 ms

这样看起来速度还行，不过是本地数据库，速度自然快点。

换个角度来测试

相同偏移量，不同数据量

SELECT * FROM `user_operation_log` LIMIT 10000, 10
SELECT * FROM `user_operation_log` LIMIT 10000, 100
SELECT * FROM `user_operation_log` LIMIT 10000, 1000
SELECT * FROM `user_operation_log` LIMIT 10000, 10000
SELECT * FROM `user_operation_log` LIMIT 10000, 100000
SELECT * FROM `user_operation_log` LIMIT 10000, 1000000

查询时间如下：

##1000 items61ms74ms60ms

Quantity	First time	Second time	Third time
10 items	53ms	52ms	47ms
100 items	50ms	60ms	55ms
10000 items	164ms	180ms	217ms
100000 items	1609ms	1741ms	1764ms
1000000 items	16219ms	16889ms	17081ms

From the above results we can conclude: The larger the amount of data, the longer it takes

相同数据量，不同偏移量

SELECT * FROM `user_operation_log` LIMIT 100, 100
SELECT * FROM `user_operation_log` LIMIT 1000, 100
SELECT * FROM `user_operation_log` LIMIT 10000, 100
SELECT * FROM `user_operation_log` LIMIT 100000, 100
SELECT * FROM `user_operation_log` LIMIT 1000000, 100

偏移量	第一次	第二次	第三次
100	36ms	40ms	36ms
1000	31ms	38ms	32ms
10000	53ms	48ms	51ms
100000	622ms	576ms	627ms
1000000	4891ms	5076ms	4856ms

从上面结果可以得出结束：偏移量越大，花费时间越长

SELECT * FROM `user_operation_log` LIMIT 100, 100
SELECT id, attr FROM `user_operation_log` LIMIT 100, 100

如何优化

既然我们经过上面一番的折腾，也得出了结论，针对上面两个问题：偏移大、数据量大，我们分别着手优化

优化偏移量大问题

采用子查询方式

我们可以先定位偏移位置的 id，然后再查询数据

SELECT * FROM `user_operation_log` LIMIT 1000000, 10

SELECT id FROM `user_operation_log` LIMIT 1000000, 1

SELECT * FROM `user_operation_log` WHERE id >= (SELECT id FROM `user_operation_log` LIMIT 1000000, 1) LIMIT 10

查询结果如下：

sql	Spend time
First article	4818ms
The second article (without index)	4329ms
The second article (with index) )	199ms
The third article (without index)	4319ms
The third Bar (with index)	201ms

从上面结果得出结论：

• 第一条花费的时间最大，第三条比第一条稍微好点
• 子查询使用索引速度更快

缺点：只适用于id递增的情况

id非递增的情况可以使用以下写法，但这种缺点是分页查询只能放在子查询里面

注意：某些 mysql 版本不支持在 in 子句中使用 limit，所以采用了多个嵌套select

SELECT * FROM `user_operation_log` WHERE id IN (SELECT t.id FROM (SELECT id FROM `user_operation_log` LIMIT 1000000, 10) AS t)

采用 id 限定方式

这种方法要求更高些，id必须是连续递增，而且还得计算id的范围，然后使用 between，sql如下

SELECT * FROM `user_operation_log` WHERE id between 1000000 AND 1000100 LIMIT 100

SELECT * FROM `user_operation_log` WHERE id >= 1000000 LIMIT 100

查询结果如下：

sql	花费时间
第一条	22ms
第二条	21ms

从结果可以看出这种方式非常快

注意：这里的 LIMIT 是限制了条数，没有采用偏移量

优化数据量大问题

返回结果的数据量也会直接影响速度

SELECT * FROM `user_operation_log` LIMIT 1, 1000000

SELECT id FROM `user_operation_log` LIMIT 1, 1000000

SELECT id, user_id, ip, op_data, attr1, attr2, attr3, attr4, attr5, attr6, attr7, attr8, attr9, attr10, attr11, attr12 FROM `user_operation_log` LIMIT 1, 1000000

查询结果如下：

sql	花费时间
第一条	15676ms
第二条	7298ms
第三条	15960ms

It can be seen from the results that by reducing unnecessary columns, the query efficiency can also be significantly improved

The first and third query speeds are almost the same. At this time, you will definitely complain, then I Why write so many fields? Just * and you’re done.

Note that my MySQL server and client are on the same machine, so the query data is similar. Qualified students can test it. Test the client separately from MySQL

SELECT * Isn’t it nice?

By the way, I would like to add why SELECT * is prohibited. Isn't it delicious because it's simple and mindless?

Two main points:

1. Using "SELECT *" the database needs to parse more objects, fields, permissions, attributes and other related content. When the SQL statement is complex and hard When there are many parses, it will cause a heavy burden on the database.
2. Increase network overhead, * Sometimes useless and large text fields such as log and IconMD5 are mistakenly added, and the data transmission size will increase geometrically. Especially since MySQL and the application are not on the same machine, this overhead is very obvious.

End

Finally, I hope you all can practice it yourself, and you will definitely gain more!

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