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After MySQL database optimization, we can not only reduce the redundancy of the database, but also change the running speed of the database. Here are 19 very good MySQL database optimization methods for reference.

To be clear: the following optimizations are based on "Mysql- index-BTree type"

1. EXPLAIN

To do MySQL optimization, we need to make good use of EXPLAIN to view the SQL execution plan.

Let's give a simple example to mark the data that we want to focus on (1, 2, 3, 4, 5).

Type column, connection type. A good sql statement should at least reach the range level. Avoid the occurrence of all-level key columns, using the index name. If no index is selected, the value is NULL. You can use mandatory indexing key_len columns, index length rows columns, number of rows scanned. The value is a pre-estimated extra column, which is detailed. Note that the common unfriendly values are: Using filesort, Using temporary II. IN should not contain too many values in SQL statements.

MySQL optimizes IN accordingly, that is, all the constants in IN are stored in an array that is sorted. However, if the number is higher, the consumption is also relatively large. Another example: select id from t where num in (1 between 2) for consecutive values, don't use in if you can, or use a connection instead.

3. The SELECT statement must specify the field name.

SELECT * adds a lot of unnecessary consumption (cpu, io, memory, network bandwidth); increases the possibility of using overlay indexes; and front breaks also need to be updated when the table structure changes. Therefore, it is required to put the field name directly after the select.

4. Use limit 1 when only one piece of data is needed

This is to make the type column in EXPLAIN reach the const type

If the index is not used in the sort field, sort as little as possible

6. If other fields in the restriction do not have an index, use or as little as possible

If one of the fields on both sides of the or is not an index field, and the other condition is not an index field, it will cause the query not to move the index. In many cases, using union all or union (when necessary) instead of "or" will get better results.

7. Replace union with union all as much as possible

The main difference between union and union all is that the former needs to merge the result sets and then carry out unique filtering operation, which will involve sorting, increasing a large number of CPU operations, and increasing resource consumption and delay. Of course, the prerequisite for union all is that there is no duplicate data in both result sets.

Do not use ORDER BY RAND ()

Select id from `dynamic` order by rand () limit 1000

The above sql statement can be optimized to

Select id from `dynamic`t1 join (select rand () * (select max (id) from `dynamic`) as nid) T2 on t1.id > t2.nid limit 1000

Distinguish between in and exists, not in and not exists

Select * from table A where id in (select id from form B)

The above sql statement is equivalent to

Select * from table A where exists (select * from form B where table B.id = table A.id)

The difference between in and exists is mainly caused by the change of the driver order (which is the key to the performance change). If it is exists, then the outer layer table is the driver table and is accessed first, and if it is IN, then the subquery is executed first. Therefore, IN is suitable for situations where the appearance is large and the inner table is small; EXISTS is suitable for situations where the appearance is small and the inner table is large.

With regard to not in and not exists, the recommended use of not exists is not just a matter of efficiency, but there may be logic problems with not in. How to write a sql statement instead of not exists efficiently?

Original sql statement

Select colname... From A form where a.id not in (select b.id from B form)

Efficient sql statement

Select colname... From A table Left join B table on where a.id = b.id where b.id is null

The extracted result set is shown in the following figure. Table An is not the data in Table B.

Use reasonable paging methods to improve the efficiency of paging

Select id,name from product limit 866613, 20

When using the above sql statement for paging, someone may find that as the amount of data in the table increases, the direct use of limit paging queries will become slower and slower.

The method of optimization is as follows: you can take the id of the maximum number of rows of the previous page, and then limit the starting point of the next page according to this maximum id. In this column, the largest id on the previous page is 866612. Sql can be written as follows:

Select id,name from product where id > 866612 limit 20

11. Segmented query

In some user selection pages, the time range selected by some users may be too large, resulting in slow query. The main reason is that there are too many scan lines. At this time, you can use the program, segment query, loop traversal, merge the results to display.

As shown in the following sql statement, a segmented query can be used when the number of rows scanned is more than one million.

Avoid judging the null value of a field in the where clause

The judgment of null causes the engine to abandon the use of indexes and perform a full table scan.

XIII. Fuzzy query with% prefix is not recommended

For example, LIKE "% name" or LIKE "% name%", this kind of query can cause the index to fail and perform a full table scan. But you can use LIKE "name%".

Then how to query% name%?

As shown in the following figure, although the index is added to the secret field, it is not used in the explain result

So how to solve this problem? answer: use full-text index

Select id,fnum,fdst from dynamic_201606 where user_name like'% zhangsan%'; is often used in our queries. With such a statement, the ordinary index can not meet the query requirements. Fortunately, in MySQL, there is a full-text index to help us.

The sql syntax for creating a full-text index is:

ALTER TABLE `dynamic_ 201606` ADD FULLTEXT INDEX `idx_user_ name` (`user_ name`)

The sql statements that use full-text indexing are:

Select id,fnum,fdst from dynamic_201606 where match (user_name) against ('zhangsan' in boolean mode)

Note: before you need to create a full-text index, contact DBA to determine whether it can be created. At the same time, we should pay attention to the difference between the writing of query statements and ordinary indexes.

Avoid expression operations on fields in the where clause

such as

Select user_id,user_project from user_base where age*2=36

The arithmetic operation is performed on the field in the, which will cause the engine to give up using the index. It is recommended to change it to

Select user_id,user_project from user_base where age=36/2

Avoid implicit type conversions

Type conversion occurs when the type of the column field is inconsistent with the type of the parameter passed in the where clause. It is recommended to determine the parameter type in the where first.

For federated indexes, follow the leftmost prefix rule

For columns, the index contains the field id,name,school, either directly with the id field or in the same order as id,name, but name;school cannot use this index. Therefore, when creating a federated index, we must pay attention to the order of the index fields, and put the commonly used query fields first.

If necessary, you can use force index to force a query to walk an index

Sometimes the MySQL optimizer takes the index it sees fit to retrieve the sql statement, but maybe the index it uses is not what we want. At this point, force index can be used to force the optimizer to use our index.

Pay attention to the scope query statement

For federated indexes, if there is a scope query, such as between, >

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