Optimizing MySQL SELECT statements
The core logic of a database application is performed through SQL statements, whether issued directly through an interpreter or submitted behind the scenes through an API. The tuning guidelines of this post will help to speed up all kinds of MySQL applications. The guidelines cover SQL operations that read and write data, the behind-the-scenes overhead for SQL operations in general, and operations used in specific scenarios such as database monitoring.
Optimizing SELECT Statements
Queries, in the form of SELECT statements, perform all the lookup operations in the database. Tuning these statements is a top priority, whether to achieve sub-second response times for dynamic web pages, or to chop hours off the time to generate huge overnight reports.
Besides SELECT statements, the tuning techniques for queries also apply to constructs such as CREATE TABLE…AS SELECT, INSERT INTO…SELECT, and WHERE clauses in DELETE statements. Those statements have additional performance considerations because they combine write operations with the read-oriented query operations.
Speed of SELECT statements:
- To make a slow SELECT … WHERE query faster, the first thing to check is whether you can add an index. Set up indexes on columns used in the WHERE clause, to speed up evaluation, filtering, and the final retrieval of results. To avoid wasted disk space, construct a small set of indexes that speed up many related queries used in your application. Indexes are especially important for queries that reference different tables, using features such as
joins and foreign keys. You can use the EXPLAIN statement to determine which indexes are used for a
SELECT. - Isolate and tune any part of the query, such as a function call, that takes excessive time. Depending on how the query is structured, a function could be called once for every row in the result set, or even once for every row in the table, greatly magnifying any inefficiency.
- Minimize the number of full table scans in your queries, particularly for big tables.
- Keep table statistics up to date by using the ANALYZE TABLE statement periodically, so the optimizer has the information needed to construct an efficient execution plan.
- Learn the tuning techniques, indexing techniques, and configuration parameters that are specific to the storage engine for each table. Both InnoDB and MyISAM have sets of guidelines for enabling and sustaining high performance in queries.
- Avoid transforming the query in ways that make it hard to understand, especially if the optimizer does some of the same transformations automatically.
- If a performance issue is not easily solved by one of the basic guidelines, investigate the internal details of the specific query by reading the EXPLAIN plan and adjusting your indexes, WHERE clauses, join clauses, and so on. (When you reach a certain level of expertise, reading the EXPLAIN plan might be your first step for every query.)
- Adjust the size and properties of the memory areas that MySQL uses for caching. With efficient use of the MyISAM key cache, InnoDB buffer pool, and the MySQL query cache, repeated queries run faster because the results are retrieved from memory the second and subsequent times.
- Even for a query that runs fast using the cache memory areas, you might still optimize further so that they require less cache memory, making your application more scalable. Scalability means that your application can handle more simultaneous users, larger requests, and so on without experiencing a big drop in performance.
- Deal with locking issues, where the speed of your query might be affected by other sessions accessing the tables at the same time.
