An effective indexing strategy needs to be combined with query patterns, data distribution and business needs, rather than blindly added. 1. Understand common query paths, prioritize the establishment of joint indexes for multi-field combination, sorting or grouping operations, and pay attention to index order; 2. Avoid excessive indexing to reduce write overhead, clean redundant indexes regularly, and view unused indexes through the system view; 3. Use overlay indexes to make the index itself contain the fields required for query, reduce table back operations, and improve reading efficiency; 4. Consider partitioning and indexing for super-large tables, select partition keys that are consistent with the query conditions, and establish a reasonable index for each partition, but the complexity and performance improvements are required.

Indexing is a key tool to improve MySQL query performance, but for large tables, blindly adding indexes may slow down writing and waste storage space. A truly effective indexing strategy is not the more, the better, but should be designed in combination with query patterns, data distribution and business needs.

1. Understand the query mode and create indexes as needed

Don't add indexes when you see the WHERE clause. You need to figure out what the commonly used query path looks like. for example:

• Which fields often appear in WHERE conditions?
• Is there a situation where multiple fields combine query?
• Is there a sort (ORDER BY) or a grouping (GROUP BY) operation?

For example: you have an order table orders. If most queries are filtered by a combination of user_id and created_at , then a joint index (user_id, created_at) is more efficient than two single column indexes.

Note: Union indexes are in order, (a, b) can support WHERE a = ? and WHERE a = ? AND b = ? , but not WHERE b = ? .

2. Avoid over-index and control write overhead

Every time data is inserted, updated, or deleted, MySQL needs to update all relevant indexes synchronously. If you add seven or eight indexes to a table with tens of millions of rows, the writing speed will be significantly slower.

Suggested practices:

• Delete indexes that are no longer used, especially the "temporary" indexes left behind during the testing phase
• For fields that are frequently updated but rarely queried, try to avoid indexing
• Use ALTER TABLE ... DROP INDEX to clean redundant indexes regularly

You can use the following statement to view unused indexes:

 SELECT * FROM sys.schema_unused_indexes;

This can help you find out which indexes can actually be deleted.

3. Use overlay index to reduce back-to-table

Overwriting index means: the index itself contains all the fields required for the query, so the database does not need to search for data rows in the primary key index, saving I/O.

For example, suppose you have a query like this:

 SELECT name FROM users WHERE status = 'active';

If you have an index on the status field but only include this field, then MySQL still needs to go back to the table to check the name. However, if a joint index (status, name) is established, the index can be directly hit and completed the query.

This optimization method is particularly suitable for report-type queries with more reads and less reads.

4. Consider the coordination between partition and index

When the data volume of a single table is very large (such as hundreds of millions of records), even if there is an index, it may affect the query efficiency because the index tree is too deep. You can consider using partition tables at this time.

Common partitioning methods include partitioning by time range, partitioning by hash, etc. What should be noted is:

• The partition key is best consistent with the fields in the query conditions.
• Each partition has its own index structure, so you must re-evaluate the index strategy after partitioning.
• Don't think that using partitions will definitely be fast, it also brings a certain degree of complexity.

For example, if you have a log table, add millions of new data every day, you can partition the range by log_date and add an index of (user_id, log_date) to each partition, which can not only quickly locate user behavior, but also control the partition size.

Basically that's it. Index optimization is not something that can be achieved overnight. The key is to continuously observe query performance, analyze execution plans, and regularly maintain the index structure. Not complicated but easy to ignore.

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