In today's digital age, databases are a core component of many applications. MySQL is a popular open-source relational database that is widely used in various scenarios. In MySQL, indexes are the key to improving query performance. This article will take an in-depth look at MySQL indexes and use the explain command to reveal how they work.
MySQL indexes are used to retrieve data quickly. In relational databases, indexes can avoid full table scans and improve query efficiency. MySQL supports multiple types of indexes, such as B-tree indexes, hash indexes, and full-text indexes. Proper use of indexes can significantly improve database performance.
explain is a command in MySQL that is used to view the query execution plan. With Explain, we can understand how MySQL uses indexes to execute queries. Here's an explanation of some of the key columns in the output of the explain command:
id: In SQL, the order of execution is from largest to smallest, following the following rules: 1For records with the same ID, the order of execution is from top to bottom2.If it is a subquery, the ordinal number of the id is incremented, and the priority is proportional to the id value, so the record with a higher id value will be executed earlier3.If multiple records with the same ID exist, they can be considered to be the same group, and they are executed sequentially from top to bottom. At the same time, in each group, the higher the ID value, the higher the priority, the earlier the execution.
select_type：
table: The table column shows which table was accessed. In general, a table column is fairly simple: it's just that table, or an alias for that table. However, if there is a subquery or union operation in the from clause, the table column becomes more complex.
If there is a subquery in the from clause, the table column is, where n is the ID of the subquery
When there is a union, the table column of the union result contains a list of ids that participate in the union, such as:
type: indicates the way in which MySQL finds the required rows in the table, also known as the access type, and the type scanning mode is from fast to slow: system > const > eq ref > ref > range > index > all.
possible keys: Displays the indexing options available to MySQL, indicating the indexes that can be found for records in the table. If the query involves a column with an index, the index will be displayed, but it doesn't mean that it will be used by the query.
key: Displays the key (index) that MySQL actually decides to use.
key len: The index length used in the query can be calculated by the number of bytes in the index of the index column.
ref: Shows the column or constant used by the previous table to find a value in the index of the key column record.
rows: Represents an estimate of the number of rows to be read in order to find the rows you need. The lower the value, the better.
filtered: A pessimistic estimate of the percentage of records in the table that meet a certain criteria. If you multiply the rows column by this percentage, you can see that MySQL estimates the number of rows it will associate with a table in the query plan.
extra：
Avoid full table scanning: Avoid using all join types and use more efficient connection types, such as range or index, by viewing the type field.
Use indexes wisely: Check the possible keys and key fields to ensure that the columns in the query condition have appropriate indexes. Avoid using unnecessary indexes and ensure that the indexes you use are optimal.
Reduced sort operation: when the extra field is displayed"using filesort", MySQL needs to sort the results, which consumes a lot of CPU resources. Sorting operations can be reduced by adding appropriate indexes.
Avoid using temporary tables: when the extra field is displayed"using temporary", MySQL needs to use a temporary table to store query results. This increases disk IO operations and affects performance. You can avoid using temporary tables by optimizing your query criteria.
Adjust query statements: Sometimes, the way you write your query statements can also affect performance. Experiment with different spellings or combinations to find the most appropriate query.
In database operations, indexes are an important means of improving query speed. However, many times we may fail the index due to some operations or improper settings. Not only does this slow down queries, but it can also lead to degraded system performance.
Index column implicit type conversion
When we compare columns with different data types in a query, MySQL may do an implicit type conversion, resulting in index invalidation. For example, when we use a column of type string to compare with a column of type number, MySQL converts the string to a number for comparison. To avoid this, we should try to avoid comparing columns of different data types, or explicitly specifying the data type when querying.
Use functions or operations
Using a function or performing an operation on an index column causes the index to become invalid. For example, when we use a function to compute or transform an index column, MySQL will scan the entire table before performing the calculation or transformation operation, which bypasses the index. To avoid this, we should try to avoid using functions or performing operations on index columns.
The data types do not match
When we compare columns with different data types in a query, MySQL may do an implicit type conversion, resulting in index invalidation. For example, when we use a column of type string to compare with a column of type number, MySQL converts the string to a number for comparison. To avoid this, we should try to avoid comparing columns of different data types, or explicitly specifying the data type when querying.
Search using like
When we use like search in a query, if the wildcard (%) is at the beginning of the string, then the index will be invalidated. For example, when we use like'%abc'When searching, MySQL scans the entire table for matching strings. To avoid this, we should try to avoid using like searches, or putting wildcards at the end of the string.
Too much index fragmentation
When there is too much index fragmentation, MySQL needs to rebuild or reorganize the index, which may cause the index to become invalid. To avoid this, we can optimize and maintain the database on a regular basis, e.g. by usingoptimize tablecommand to rebuild tables and indexes.
Insufficient optimization of query statements
Sometimes our query statements may not be optimized enough for MySQL to make effective use of indexes. For example, when we query with multiple OR conditions, MySQL may choose to perform a full table scan instead of using an index. To avoid this, we can use the explain statement to analyze the query execution plan and optimize it.
This article provides an in-depth analysis of how mySQL indexes work, how to use the explain command, and shares practical optimization tips. By using indexes wisely and optimizing queries, you can improve the performance and responsiveness of your MySQL database and provide better support for your applications. Hopefully, this article will help you better understand and apply MySQL indexes to improve your database performance.
MySQL index