MySQL database query performance optimization strategy

Optimize queries

Use the Explain statement to analyze query statements

Explain is used to analyze SELECT query statements. Developers can optimize query statements by analyzing Explain results.

By analyzing the query statements, we can understand the execution of the query statements, find out the bottlenecks of the query statement execution, and thus optimize the query statements.

Using index query

One of the most effective ways to improve performance in MySQL is to design reasonable indexes for the data tables.

Indexes provide a way to efficiently access data and speed up queries.

If no index is used during the query, the query statement will scan all records in the table. When the amount of data is large, the query speed will be very slow.

Using indexes for querying, the query statement can quickly locate the records to be queried according to the index, thereby reducing the number of query records and achieving the purpose of improving query speed.

Several special cases (indexes do not work when querying using indexed fields)

• When using the Like keyword, if the first character of the matching string is "%", the index will not work. If the first keyword is not "%", the index will work.
• MySQL can create indexes for multiple fields. An index can include 16 fields. The index will only be used when the first of these fields is used in the query condition.
• The index works only when the query keyword is only OR and the columns on both sides of OR are indexes.
  

Optimizing Subqueries

Although subqueries can make query statements more flexible, their execution efficiency is not high because MySQL needs to create a temporary table for the query results of the inner query statement.

You can use a join query instead of a subquery. A join query does not require the creation of a temporary table and is faster than a subquery.

Optimizing data access

1. Reduce the amount of data requested

• Only return the necessary columns. It is best not to use the select * syntax.
• Only necessary rows are returned, and the limit statement is used to limit the number of data to be obtained;
• Cache repeatedly queried data: Using cache can avoid querying in the database, especially when the data is frequently queried repeatedly, the query performance improvement brought by cache will be very obvious.
  

2. Reduce the number of rows scanned by the server

The most efficient approach is to: use indexes to cover queries;

Refactoring query method

1. Split large queries

If a large query is executed all at once, it may lock a lot of data at once, fill up the entire transaction log, exhaust system resources, and block many small but important queries.

2. Decompose large connection queries

Break a large join query into a single table query for each table, and then do the join in the application.

The benefits of doing this are:

1. Make the cache more efficient. For a join query, if one of the tables changes, the entire query cache cannot be used. For multiple queries after decomposition, even if the query of one of the tables changes, the query cache for other tables can still be used.
2. Decompose into single-table queries, the cached results of these single-table queries are more likely to be used by other queries, thereby reducing the query of redundant records.
3. Reduce lock contention.
4. Connecting at the application layer makes it easier to split the database, making it easier to achieve high performance and scalability.
   

Optimize database structure

1. Split a table with many fields into multiple tables

For a table with many fields, if some fields are used very infrequently, these fields can be separated to form a new table.

When a table has a large amount of data, it will be slow due to the presence of fields with low frequency of use.

2. Add an intermediate table

For tables that often require joint queries, you can create an intermediate table to improve query efficiency.

3. Optimize the speed of inserting records

When inserting records, the main factors affecting the insertion speed are index, uniqueness check, number of records inserted at a time, etc. These can be optimized separately according to these situations.

Optimizing MySQL Server

1. Optimize server hardware

Aiming at performance bottlenecks, improving hardware configuration can increase the speed of database query and update.

• Configure larger memory.
• Configure a high-speed disk system to reduce the waiting time for reading disks.
• Reasonably allocate disk IO.
• Configure multiple processors. MySQL is a multi-threaded database. Multiple processors can execute multiple threads simultaneously.
  

2. Optimize MySQL parameters

Optimizing MySQL parameters can improve resource utilization and thus improve server performance.

The above is the details of the MySQL database query performance optimization strategy. For more information on MySQL query performance optimization, please pay attention to other related articles on 123WORDPRESS.COM!