The main considerations for optimizing queries are:
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
Indexes are especially important for queries that
reference different tables, using features such as
foreign keys. You
can use the
statement to determine which indexes are used for a
Section 8.3.1, “How MySQL Uses Indexes” and
Section 8.8.1, “Optimizing Queries with EXPLAIN”.
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
MyISAM have sets of guidelines for
enabling and sustaining high performance in queries. For
details, see Section 8.5.5, “Optimizing InnoDB Queries”
and Section 8.6.1, “Optimizing MyISAM 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
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, 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.