To make SQL queries faster, the key is to use indexes reasonably. 1. Understand different types of indexes: B-Tree is suitable for equal value and range queries, hash indexes only support equal value matching, full-text indexes are used for fuzzy matching, and combined indexes must follow the principle of leftmost prefix. 2. Avoid over-index and incorrect indexing: low cardinality sequences, improper field order, and unreasonable prefix length of large fields will affect performance, and unused indexes should be cleaned regularly. 3. Use EXPLAIN to analyze the execution plan: judge whether the index takes effect and optimize the structure through the type, key, rows and Extra fields. 4. Selection strategy according to business scenarios: high-frequency reading systems can appropriately build coverage indexes, while high-concurrent writing systems need to control the number of indexes to improve efficiency while balancing query and update costs.

If you want SQL queries to run faster, using indexes reasonably is one of the most direct and effective ways. However, many people's understanding of indexes is still at the stage of "adding quickly", and in fact, if you don't use them well, it may slow down performance. The key is to understand the applicable scenarios of data structures, query patterns and indexes.

1. Understand different types of indexes

Not all indexes are the same, and different types are suitable for different query requirements:

• B-Tree index : The most common type, suitable for equal value lookups and range queries (such as WHERE id = 5 or WHERE created_at > '2024-01-01' ).
• Hash index : Only supports equal value matching, fast speed but not scope query, and is used more frequently in memory tables.
• Full-text index : used for fuzzy matching of text content, such as MySQL's FULLTEXT index.
• Combined index (joint index) : Indexes created by multiple fields together, the order is very important and must comply with the principle of leftmost prefix.

For example: If you often check WHERE user_id = 1 AND status = 'active' , then creating a composite index for (user_id, status) will be more efficient than creating two indexes separately.

2. Avoid over-index and incorrect indexing

Although indexes can speed up queries, they also come at a cost:

• Each new index takes up disk space
• Insert, update, and delete operations will slow down because the database needs to maintain the index.
• Improper indexing can also mislead the optimizer to choose an inefficient execution plan

Common errors include:

• Create indexes on low cardinality sequences (such as gender fields)
• Ignore the order of fields in the query conditions, causing the combined index to fail
• Prefixed indexing of large fields (such as long text) is too short or too long

It is recommended to analyze and check the query log regularly, find the indexes that are not used, and clean them up in time.

3. Use EXPLAIN to analyze execution plans

Want to know if your index is effective? Don't guess, just take a look at EXPLAIN .

Taking MySQL as an example, execute:

 EXPLAIN SELECT * FROM users WHERE email = 'test@example.com';

Focus on these fields:

• type : If it is ref , range or const , it is better. If it is ALL , it means that the index is not taken.
• key : Which index is actually used.
• rows : Estimate the number of rows scanned, the smaller the better.
• Extra : If there is Using filesort or Using temporary , it means that the sorting or temporary table operations are heavy, and you can consider optimizing the index structure.

4. Choose the appropriate indexing strategy based on business scenarios

Not every query needs to be indexed, and not all indexes are worth retaining. It is to be judged based on the specific business:

• If it is a report system with high frequency reading and low frequency writing, it is no problem to build a few more coverage indexes appropriately.
• If it is a system with high concurrent writes, the number of indexes must be controlled to avoid affecting the write performance.
• You can consider using "overwrite index", that is, the index itself contains all the fields required for the query, so that there is no need to go back to the table query, which is more efficient.

For example:

 SELECT name, age FROM users WHERE department = 'IT';

If you create a composite index for (department, name, age) , you can get the results directly from the index without accessing the data table.

Basically that's it. Index optimization seems simple, but in fact there are many details. The key is to analyze and adjust it based on actual queries.

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