mysql or index failure

MySQL is a popular relational database management system, and indexes are one of the important tools for performance optimization in MySQL. Although indexes can improve query performance, the performance of a MySQL database will be severely affected when indexes become invalid. This article will explore the causes of index failure in MySQL and how to avoid this situation.

1. Reasons for index failure

1. Duplication of data
   When data in a table is duplicated, the MySQL query planner may decide to give up using the index because it thinks Full table scans are faster and more efficient. This is because multiple lookups on the index may take longer than scanning the entire table.

For example, if there is a column named "status" in a table, there are only two values ​​in it, "active" and "inactive". If the number of rows with the value of this column as "active" accounts for 80% of the total data in the table, MySQL will give up using the index and scan the entire table. This is because MySQL believes that index-based queries will involve a lot of I/O operations, while a full table scan will be faster because it only has to find 80% of the rows instead of the entire table.

2. Data type matching
   When executing a string-based query, the index with the same string type will be matched. If a column of "varchar" data type exists in the table, but the query uses the "char" data type, the query planner will choose a full table scan instead of using the index.
3. The order of index columns
   The order of index columns has a great impact on query performance. If the order of the index columns is different from the order of the columns in the query, the query planner may choose to perform a full table scan without using the index. In this case, the optimizer will decide to scan the data within a certain range on the index, and then check whether each matching data row meets the complete query criteria, and if so, they will be returned.

For example, if the query condition is "WHERE age>25 AND name='John'", but the index is created for the "name" column and the "age" column, MySQL will prefix the to find the data in, but because the columns are in a different order, the query planner abandons using the index.

4. Size of the index
   The larger the index in the table, the slower the query optimizer will perform index scans. When the disk space occupied by an index becomes large, the optimizer may abandon using the index and choose to perform a full table scan.

2. Methods to avoid index failure

1. Ensure data consistency
   In order to avoid index failure caused by data duplication, we should ensure that the table data consistency. This can be accomplished through constraints, triggers, and other methods. For example, you can use a unique key or primary key to ensure that each row has a unique identifier.
2. Use the correct data type
   In order to avoid index failure problems caused by data type mismatch, the index should be created using the same data type as the query conditions. At the same time, we should also avoid conversion or function processing of index columns in the "where" clause.
3. Create the correct index order
   In order to avoid index failure problems caused by mismatched index orders, you should create the correct index order by testing and optimizing queries. When writing queries, you should consider index creation on columns included in the "WHERE" and "ORDER BY" clauses. In addition, we should pay attention to the order of multiple index columns in the table, and put the most selective columns first as much as possible to improve query efficiency.
4. Ensure that the index is not too large
   In order to avoid index failure problems caused by too large an index, you should avoid creating too many indexes and indexes that exceed the necessary length. You can check the index and space usage of the table by using tools officially provided by MySQL such as mysqltuner or pt-online-schema-change.

Conclusion

The index in MySQL is one of the important tools for optimizing query performance, but when the index fails, query performance will be seriously affected. We should understand the reasons for index failure and take measures to avoid this happening. By using the correct data types, creating the correct index order, ensuring data consistency, and avoiding overly large indexes, we can optimize the query performance of the MySQL database and improve overall application efficiency.

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