Full table scanning may be faster in MySQL than using indexes. Specific cases include: 1) the data volume is small; 2) when the query returns a large amount of data; 3) when the index column is not highly selective; 4) when the complex query. By analyzing query plans, optimizing indexes, avoiding over-index and regularly maintaining tables, you can make the best choices in practical applications.

MySQL indexes will fail when querying without using index columns, mismatching data types, improper use of prefix indexes, using functions or expressions for querying, incorrect order of index columns, frequent data updates, and too many or too few indexes. . 1. Do not use index columns for queries. In order to avoid this situation, you should use appropriate index columns in the query; 2. Data types do not match. When designing the table structure, you should ensure that the index columns match the data types of the query; 3. , Improper use of prefix index, you can use prefix index.

Common situations: 1. Use functions or operations; 2. Implicit type conversion; 3. Use not equal to (!= or <>); 4. Use the LIKE operator and start with a wildcard; 5. OR conditions; 6. NULL Value; 7. Low index selectivity; 8. Leftmost prefix principle of composite index; 9. Optimizer decision; 10. FORCE INDEX and IGNORE INDEX.

MySQL index leftmost principle principle and code examples In MySQL, indexing is one of the important means to improve query efficiency. Among them, the index leftmost principle is an important principle that we need to follow when using indexes to optimize queries. This article will introduce the principle of the leftmost principle of MySQL index and give some specific code examples. 1. The principle of index leftmost principle The index leftmost principle means that in an index, if the query condition is composed of multiple columns, then only the leftmost column in the index can be queried to fully satisfy the query conditions.

MySQL supports four index types: B-Tree, Hash, Full-text, and Spatial. 1.B-Tree index is suitable for equal value search, range query and sorting. 2. Hash index is suitable for equal value searches, but does not support range query and sorting. 3. Full-text index is used for full-text search and is suitable for processing large amounts of text data. 4. Spatial index is used for geospatial data query and is suitable for GIS applications.

MySQL index cardinality has a significant impact on query performance: 1. High cardinality index can more effectively narrow the data range and improve query efficiency; 2. Low cardinality index may lead to full table scanning and reduce query performance; 3. In joint index, high cardinality sequences should be placed in front to optimize query.

MySQL indexes are divided into the following types: 1. Ordinary index: matches value, range or prefix; 2. Unique index: ensures that the value is unique; 3. Primary key index: unique index of the primary key column; 4. Foreign key index: points to the primary key of another table ; 5. Full-text index: full-text search; 6. Hash index: equal match search; 7. Spatial index: geospatial search; 8. Composite index: search based on multiple columns.

How to use MySQL indexes rationally and optimize database performance? Design protocols that technical students need to know! Introduction: In today's Internet era, the amount of data continues to grow, and database performance optimization has become a very important topic. As one of the most popular relational databases, MySQL’s rational use of indexes is crucial to improving database performance. This article will introduce how to use MySQL indexes rationally, optimize database performance, and provide some design rules for technical students. 1. Why use indexes? An index is a data structure that uses