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MySQL Index Optimization and Query Performance Improvement

In database systems, indexes are an important means of improving query performance. As a widely used relational database management system, the optimization of MySQL indexes is directly related to the overall performance of the system and user experience. This article will combine the content of reference materials to deeply explore the basic concepts of MySQL indexes, the leftmost prefix matching principle, common scenarios of index failure, optimization strategies, and application scenarios.

1. Basic concepts of MySQL index

An index is a data structure in a database management system that sorts the values ​​of one or more columns in a database table. An index can be used to quickly access specific information in a database table. MySQL supports multiple types of indexes, including B-Tree indexes, hash indexes, and full-text indexes, among which B-Tree indexes are the most commonly used.

2. Leftmost prefix matching principle

The leftmost prefix matching principle is an important rule in MySQL when using composite indexes. It requires that the query conditions must be matched continuously starting from the leftmost column of the composite index, otherwise the index will not be fully utilized. This means that when designing a composite index, you need to arrange the order of the index columns reasonably according to the actual query situation.

3. Common Scenarios of Index Failure

  1. Querying with SELECT *: When you query with SELECT *, MySQL cannot use indexes for profiling, which may result in inefficient queries.
  2. Calculating or using functions on index columns: This will cause the index to become invalid because MySQL needs to calculate or convert the values ​​of the index columns before comparison, making it impossible to directly use the index.
  3. LIKE fuzzy matching starts with a wildcard character: such as LIKE '%value'. In this case, MySQL cannot use the index to search.
  4. Implicit type conversion caused by type mismatch: When the data type in the query condition does not match the data type of the index column, MySQL will perform implicit type conversion, which may also cause the index to fail.
  5. Index invalidation caused by comparing two columns: For example, qty < total. If neither column is an index column or is not indexed in an appropriate manner, this comparison may cause the index to be invalid.
  6. Use OR instead of UNION: When querying with OR on multiple index columns, you can often get better performance if you can replace OR with UNION, because UNION can take advantage of the index in each subquery separately.
  7. Use NOT IN instead of NOT EXISTS: In most cases, NOT EXISTS queries are more efficient than NOT IN because NOT EXISTS can take advantage of indexes, while NOT IN may result in a full table scan.

4. Index Optimization Strategy

  1. Design indexes reasonably: Design appropriate composite indexes based on the actual query requirements, and pay attention to the order of index columns.
  2. **Avoid SELECT ***: Try to specify the columns that need to be queried to reduce the amount of data transferred and also help utilize indexes.
  3. Optimize query conditions: Avoid performing calculations or using functions on index columns, and try to keep query conditions directly corresponding to index columns.
  4. Reasonable use of LIKE fuzzy matching: Avoid fuzzy matching starting with wildcards as much as possible. If you must use it, consider using full-text indexing.
  5. Pay attention to data type matching: ensure that the data type in the query condition is consistent with the data type of the index column to avoid implicit type conversion.
  6. Optimize comparison operations: When possible, avoid comparing two columns in a query by adjusting the table structure and query logic.
  7. Reasonable use of UNION and NOT EXISTS: In appropriate scenarios, replace OR with UNION and replace NOT IN with NOT EXISTS to improve query efficiency.

5. Application scenarios of MySQL index

  1. Frequently queried fields: For fields that need to be queried frequently, indexing should be given priority to increase query speed.
  2. Uniqueness check: For fields that need to ensure uniqueness, a unique index can be created, which not only ensures the uniqueness of the data but also improves query efficiency.
  3. Foreign key columns: In associated queries, foreign key columns are often frequently queried fields, so indexes should be created to improve the efficiency of associated queries.
  4. Sorting and grouping fields: In queries that require sorting or grouping of results, the sorting and grouping fields should be indexed to increase the speed of sorting and grouping.

Conclusion

MySQL index optimization is an important means to improve database query performance. By rationally designing indexes, optimizing query conditions, and adopting appropriate optimization strategies, the query efficiency and overall performance of the MySQL database can be significantly improved. In actual applications, we need to select appropriate index strategies and optimization methods based on specific business needs and query patterns.