Introduction to mysql test environment with millions of data

Mysql is widely used as a very excellent free database. Usually, the data of the projects we develop rarely exceed one million. Recently, I have spent a lot of time on in-depth research on the optimization of mysql in the case of millions of data. I encountered many problems and solved them, and I would like to share them with you. Your valuable opinions are welcome!

Related recommendations: "MySQL Tutorial"

Test Environment

The total number of data is 3 million, occupying about 1G of disk space

Data structure

表1 news [ 文章表 引擎 myisam 字符集 utf-8 ]
-----------------------------------------------------
idint11主键自动增加
cateint11索引
titlevarchar200标题(便于基础搜索做了索引)
contenttext文章正文
dateint11文章发布时间(时间戳形式)

表2 cate [ 文章分类表 引擎 myisam 字符集 utf-8 ]
-----------------------------------------------------
cate_idint11主键自动增加
cate_namevarchar200文章标题

Total number of queries

myIsam 引擎下
select count(*) as total from news
//耗时 0.001秒 极快
//带上条件
select count(*) as total from news where cate = 1
耗时 0.046秒 可以接受的速度
innodb 引擎下
select count(*) as total from news
//耗时 0.7秒 很慢
select count(*) as total from news where cate = 1
耗时 0.7秒 很慢

Why are the query speeds of the two engines so different? ?

InnoDB does not save the specific number of rows in the table. That is to say, when executing select count(*) from table, InnoDB has to scan the entire table to calculate how many rows there are.

MyISAM simply reads the number of saved rows.

Note that when the count(*) statement contains the where condition, the operations of the two tables are somewhat different. InnoDB type tables use count(*) or count (primary key), plus the where col condition. The col column is a column with a unique constraint index other than the primary key of the table. This way the query speed will be very fast. That is to avoid a full table scan.

Summary

mysql uses count(*) to query the total number of data with 3 million pieces of data (myisam engine) and contains conditions (indexes are set correctly) and the running time is normal . For data that is frequently read we recommend using the myIsam engine.

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