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MySQL is a widely used relational database management system. Due to its high performance, scalability and open source nature, it has become the first choice for many enterprises and individuals. However, as the amount of data continues to increase and the complexity of the data continues to increase, MySQL's performance problems begin to emerge.
One of the important performance issues is query time. Query time refers to the time it takes for a MySQL query. The shorter the query time, the higher the performance of MySQL and the ability to handle more query requests. To address this problem, we can improve MySQL performance through query time analyzer.
What is Query Time Analyzer?
Query time analyzer is a performance analysis tool provided by MySQL, which can help users analyze the execution time of SQL query statements, find queries with long execution times, and then optimize the corresponding queries. The query time analyzer mainly provides two analysis methods:
How to use Query Time Analyzer to improve MySQL performance?
The following is a description of how to use the query time analyzer to improve MySQL performance for the two query time analyzer methods.
Explain select * from table where id=1;
Executing the above command will output the execution plan of the current query, for example:
explain select * from table where id=1; | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
id | select_type | table | partitions | type | possible_keys | key | key_len | ref | rows | filtered | Extra |
1 | SIMPLE | table | NULL | ref | PRIMARY | PRIMARY | 4 | const | 1 | 100.0 | Using index |
Among them, id represents the sequence number of each operation in the execution plan; select_type represents the type of the current operation; table represents the table name of the operation; type represents the index type used by the operation; possible_keys represents the index that may be used; key represents the final The selected index; key_len represents the length of the index; ref represents the columns used in the index; rows represents the estimated number of rows in the query results; filtered represents the filtered proportion of the query results; Extra represents other related information.
Based on the above output content, you can judge the execution efficiency of SQL query statements and identify possible problems, such as too many table scans, lack of indexes, etc.
(2) Use index optimization statements
The query time analyzer based on the Explain method can also use index optimization statements to optimize the execution plan of SQL query statements. The specific format is as follows:
ALTER TABLE table_name ADD INDEX index_name (columns);
For example:
alter table table add index (id);
After executing this statement, the id field of the table will be Add an index to make the query statement locate records that meet the conditions faster when querying.
[mysqld]
profiling=eva
profiling_history_size=20
The above code indicates that the eva storage method is enabled and the maximum number of historical records for recording SQL information is 20.
set profiling = 1;
Or set the timeout:
set profiling = 1; set profiling_history_size=20; set profiling_history_size=1000000;
Under normal circumstances, while executing the SQL statement, the profiling log file will monitor the occupied capacity , once the capacity limit is exceeded, MYSQL will stop recording.
(2) View the Profiling log
After the Profiling process is completed, you can view the Profiling log through the following command:
show profiles;
This command will output all executed Profiling information of SQL statements, including execution time of SQL statements, number of rows scanned, sorting method, index usage, etc. By analyzing this record, you can find out where MySQL's performance bottleneck lies and optimize accordingly.
Summary
Through the query time analyzer, you can comprehensively understand the execution process and performance bottlenecks of MySQL query statements, which helps to optimize SQL query statements and improve the performance of MySQL. In practical applications, it is necessary to tailor the corresponding query time analysis plan based on specific business conditions in order to better exert the effect of the analysis tool.
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