1. When designing joint indexes, fields with high equal value matching and distinction should be placed in front of the field, and after the range query field should be placed; 2. Use overlay indexes to avoid table back operations and reduce I/O overhead; 3. Sorting and grouping should ensure that the index order and direction are consistent to avoid filesort; 4. Regularly clean useless indexes and avoid duplicate indexes, and reasonably evaluate the comprehensive impact of indexes on query and writing. For complex query scenarios, the index structure should be optimized in combination with execution plan analysis, rather than blindly adding indexes.

When processing complex queries, MySQL's indexing strategy directly affects performance. Many developers know to add indexes to speed up queries, but when faced with complex scenarios such as multi-conditions, multi-table joins, sorting and grouping, they often don’t know how to design an efficient index structure. This article starts from actual needs and talks about several index optimization ideas under common and complex queries.

1. Joint index: Don't let the field order "crack" you

Multiple WHERE conditions often appear in composite queries. At this time, the effect of using single column index is limited, so you should consider using a joint index (Composite Index) . But many people overlook a key point: the order of fields will affect whether the index takes effect .

For example, suppose there is an order table orders with two fields: user_id and created_at . If you frequently check order time range according to users:

 SELECT * FROM orders WHERE user_id = 123 AND created_at BETWEEN '2024-01-01' AND '2024-06-01';

Then it is appropriate to create a joint index like (user_id, created_at) . But if created (created_at, user_id) is created, it will be of limited help to this query.

So suggestion:

• Put fields with high distinction in front
• Put fields with equal values in front
• Put the field of the range query behind

This allows MySQL to use as many indexes as possible for searching and filtering.

2. Cover index: Avoid table reversion and improve efficiency

When your query only requires a few fields, and these fields are all in the index, you can use Covering Index to avoid table back operations, which greatly improves performance.

For example, if executed frequently:

 SELECT order_id, status FROM orders WHERE user_id = 123;

Then you can create an index (user_id, order_id, status) . In this way, MySQL can get the data directly through the index, and there is no need to search it in the clustered index again.

This approach is especially suitable for frequently accessed small result set queries, which can significantly reduce I/O overhead.

3. Sort and grouping: Indexes must also be "right"

ORDER BY and GROUP BY are common performance bottlenecks, especially under large data volumes. If the appropriate index cannot be hit, a filesort or temporary table will be triggered, affecting efficiency.

For example:

 SELECT * FROM orders WHERE user_id = 123 ORDER BY created_at DESC LIMIT 10;

If your index is (user_id, created_at) , then this sort can be indexed. But if the index is just user_id or the order is incorrect, the sorting must be done by memory or disk sorting.

A few suggestions:

• Make sure the sort field appears on the rightmost right of the index
• The sorting direction must be consistent (ASC/DESC)
• If it is a GROUP BY, it is best if the index can exactly match the GROUP BY field.

Note: Sometimes even if the index is added, it may not be used due to JOIN or expression. At this time, you need to check the execution plan (EXPLAIN) to confirm whether it is really hit.

4. Don’t blindly add indexes, consider maintenance costs

Although indexes can improve query speed, they will also bring write burden. Each INSERT, UPDATE, and DELETE operation requires updating the relevant index, and too many indexes will affect the overall performance.

so:

• Clean useless indexes regularly
• Avoid duplicate indexes (such as (a,b) and (a) at the same time)
• Not all slow queries are solved by adding indexes. Sometimes rewriting SQL is more effective.

You can use SHOW INDEX FROM table_name; view existing indexes, or you can combine slow query logs and execution plans to analyze which queries are worth optimizing.

Basically that's it. The more indexes, the better, and it is not enough to just add one. Only by designing appropriate index structures for specific query patterns can the performance potential of MySQL be truly realized.

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