1. Data type mismatch
Detailed description: MySQL may attempt to perform implicit conversions when comparing values of different data types. If such transformations lead to increased complexity or the inability to directly exploit the index, it will cause the index to fail.
Examples and solutions：
-- Error Example：Data type mismatch select * from users where id = '123'; -- id yes int type，'123' yes字符串 -- Correct example：确保数据type一致 select * from users where id = 123; -- 使用正确的数据type -- If you have to use string input，Can be explicitly converted select * from users where cast(id as char) = '123';
Solution: Make sure that the values in the query condition match the column's data type. If you have to use different types of values, explicitly perform type conversion. At the application level, make sure that the parameters passed to the database are correct.
2. Implicit conversion
Detailed description: Implicit conversion refers to mysql automatically converting one data type to another data type. This transformation may change the original query pattern, resulting in index failure.
Examples and solutions：
-- Error Example：Implicit conversion select * from users where age = 25 + 0.0; -- Forced floating point operation -- Correct example：Avoid unnecessary mathematical operations select * from users where age = 25; -- Use integers directly
Solution: Try to keep the query conditions simple and clear, and avoid unnecessary mathematical operations or other operations that may lead to implicit conversions. Ensure consistency of data types when writing SQL statements.
3. Function or expression
Detailed description: Applying functions or complex expressions to index columns prevents mysql from using the index because it requires computing the result of each row, thus losing the advantage of indexing. MySQL 8.0 introduces expression indexing (also known as function indexing) which can help alleviate this problem in some cases.
Examples and solutions：
-- Error Example：Use functions on index columns select * from articles where length(title) > 10; -- Improvement method（Depend on demand） select * from articles where title like '___________%'; -- Assume that the title has at least11Characters -- Or create an expression index（mysql 8.0+） create index idx_title_length on articles ((length(title))); select * from articles where length(title) > 10;
Solution: Avoid using functions on index columns as much as possible. If you have to, consider creating an expression index or redesigning the query logic. For older versions of MySQL, refactoring queries to avoid using functions may be the only option.
4. Columns after scope query
Detailed description: In composite indexes, once a range condition occurs, mysql cannot use subsequent index parts, because these parts can no longer effectively narrow the search range. The design of composite indexes should take into account the query pattern.
Examples and solutions：
create index idx_name on table (col1, col2); select * from table where col1 = 'value1' and col2 > 'value2'; -- If you often need to col2 Range query，Consider creating a separate index create index idx_col2 on table (col2);
Solution: For frequently used range queries, indexes should be created separately for the columns involved. At the same time, when designing composite indexes, the query mode should be taken into account and try to let the equal value conditions appear before the range conditions.
5. Like query
Detailed description：likeWhen the pattern starts with a wildcard, mysql cannot use indexes to speed up queries because it requires scanning for all possible prefixes. However, if a wildcard appears at the end of the pattern, the index can still be used.
Examples and solutions：
-- Unsatisfactory query
select * from names where name like '%john%';
-- Improvement method（According to actual situation）
-- If it is a tail fuzzy match，You can use indexes
select * from names where name like 'john%';
-- Or use full text index（Suitable for large text searches）
alter table names add fulltext(name);
select * from names where match(name) against('john');
-- For prefix matching，You can use indexes覆盖
select * from names where name >= 'john' and name < 'johnz';Solution: Try to avoid using wildcardsLIKEQuery. If such a feature is indeed needed, you can consider using full-text indexing or other specialized search engines. For prefix matching, effective utilization of indexes can be achieved through range query.
6. or Conditions
Detailed description:useorConditions on different columns connected may cause mysql to fail to effectively utilize indexes, especially whenorWhen conditions span multiple different columns. MySQL 5.6 and later support index merging strategies, which can improve performance in some cases.
Examples and solutions：
-- Unsatisfactory query select * from users where first_name = 'john' or last_name = 'smith'; -- Improvement method（According to actual situation） -- If the query frequency is high，Consider creating a composite index create index idx_first_last_name on users (first_name, last_name); -- Or reconstruct the query logic，If used union select * from users where first_name = 'john' union all select * from users where last_name = 'smith'; -- Using index merge（mysql 5.6+） explain select * from users where first_name = 'john' or last_name = 'smith';
Solution: Evaluate whether performance can be improved by creating composite indexes or refactoring query logic. For some cases,UNIONProbably a better choice. examineEXPLAINOutput to see if index merging is enabled.
7. Full table scanning is more efficient
Detailed description: For very small tables or queries that return most rows, full table scanning may be faster than using indexes, because index access involves additional I/O operations. The mysql optimizer weighs the pros and cons and decides the most appropriate execution plan.
Examples and solutions：
-- For small table，Even with indexes, you may choose full table scanning select * from small_table; -- For large table，If the query returns a large number of rows，Optimizers also tend to be full table scanning select * from large_table where some_condition;
Solution: Understand the behavior of MySQL optimizer and do not blindly rely on indexes. Sometimes, full table scanning is the best choice for specific small tables or high coverage queries. Regularly analyze query performance to ensure that the optimizer makes the right decisions.
8. Low index selectivity
Detailed description: Low selectivity means that the index column contains a large number of duplicate values, which greatly reduces the effect of the index. In this case, mysql may consider full table scanning more efficient. Highly selective indexes can significantly improve query performance.
Examples and solutions：
-- The selectivity of the gender column is very low select * from employees where gender = 'm'; -- Improvement method（According to actual situation） -- Try to avoid creating independent indexes on low-selectivity columns，Unless they form a composite index with other highly selective columns create index idx_gender_salary on employees (gender, salary);
Solution: Avoid creating independent indexes on low-selectivity columns. Combining it with other highly selective columns can be considered to form a composite index. passanalyze tableCommands get statistics and evaluate index selectivity.
9. Insufficient coverage index
Detailed description: When the selected column in the query is not in the index, mysql must return to the table to obtain the complete row information, which increases the additional I/O cost and reduces the efficiency of the index. Overwriting indexes can significantly reduce read time.
Examples and solutions：
-- Suppose there is a coverage index idx_id_name Include id and name List select id, name, address from customers where id = 123; -- Improvement method create index idx_id_name_address on customers (id, name, address);
Solution: Create an overlay index, that is, include all required columns in the query. This allows you to directly get the required data in the index without returning to the table. Note that overwriting indexes, while improving read speed, may affect write performance and therefore require balanced considerations.
10. Statistical information is inaccurate
Detailed description: The mysql optimizer relies on the table's statistics to determine the query plan. If these statistics are outdated or inaccurate, the optimizer may make incorrect decisions. Maintaining good statistics is essential for optimizing queries.
Examples and solutions：
-- Analyze the table to update statistics analyze table your_table; -- Or use optimize table Reconstruct the table and update statistics optimize table your_table; -- exist mysql 8.0 Versions above，You can also use system variables to control the collection of statistics set persist optimizer_switch='histogram=on';
Solution: Run regularlyanalyze tableoroptimize tableCommands to keep statistics accurate. This is especially important for large tables. In MySQL 8.0 and above, histogram statistics can be enabled to better reflect data distribution.
11. Lock contention
Detailed description: In high concurrency environments, the existence of a lock mechanism may lead to a decrease in index efficiency, which is useless even if there is a suitable index. Locking problems not only affect index efficiency, but may also lead to other concurrency problems such as deadlocks.
Examples and solutions：
-- In high load systems，Frequent updates to a table may cause read operations to wait for write locks to be released -- Solutions include, but are not limited to, adjustment of transaction isolation levels、optimization sql Statement reduces lock time, etc.。 -- Reduce transaction isolation level to reduce locking set session transaction isolation level read committed; -- Using optimistic locking strategies，If you add a version number column alter table your_table add column version int default 0; update your_table set column1 = value1, version = version + 1 where id = specific_id and version = current_version;
Solution: Optimize SQL statements to reduce locking time, consider appropriate transaction isolation levels, and evaluate whether an optimistic locking strategy can be used. For high concurrency environments, architectural optimization measures such as library division and table division, read and write separation are considered.
Diagnosis and optimization using mysql tool
-
explain:use
explainKeywords view query execution plan and understand how mysql handles your query. - show index: Displays index information of the table to help evaluate the validity and applicability of existing indexes.
- performance_schema: Monitor and diagnose mysql performance problems, including locking, thread status, etc.
- slow query log: Record slow query logs and find out those queries that have been executed for too long.
- information_schema: Access information about database metadata, such as table structure, index, etc.
Through the above detailed discussion, we can see that the problem of mysql index failure can often be solved through reasonable query optimization, index design and maintenance. Understanding your data distribution, query patterns, and how the mysql optimizer works is the key to building efficient database applications. Continuously monitoring and optimizing database performance and ensuring full utilization of indexes are important steps to ensure fast and stable application response.
This is the article about the reasons why MySQL index failure is not included. For more information about MySQL index failure, please search for my previous articles or continue browsing the related articles below. I hope you will support me in the future!