Database index optimization:
Design and create indexes appropriately to speed up queries and reduce lock contention. Based on query demand analysis, determine which columns need to be indexed to avoid excessive or unnecessary indexes.
Database query optimization:
Write efficient SQL query statements and optimize complex queries to avoid full table scans and unnecessary join operations involving large amounts of data.
1. Use appropriate WHERE clause: Use WHERE clause in query to filter unnecessary rows to reduce database read operations. At the same time, ensuring that the WHERE clause uses index columns can improve query performance.
SELECT * FROM users WHERE name = 'John';2. Avoid using wildcard characters: % or _ as the beginning of the LIKE operator, which will cause a full table scan. Try to put wildcards at the end to take advantage of the index.
SELECT * FROM products WHERE name LIKE 'App%';3. When using JOIN operations, ensure that there are appropriate indexes or association conditions to avoid full table joins.
SELECT * FROM orders
JOIN customers ON orders.customer_id = customers.id;4. Use LIMIT to limit the size of the result set to avoid returning a large amount of unnecessary data.
SELECT * FROM products LIMIT 10;5. Use subqueries or correlated subqueries rationally to avoid multiple queries.
SELECT name, total FROM customers
WHERE total > (SELECT AVG(total) FROM customers);6. Consider using aggregate functions and GROUP BY clauses to perform statistical queries instead of obtaining the entire result set and then processing it.
SELECT product_category, COUNT(*) as count FROM products
GROUP BY product_category;7. Try to avoid using SELECT *, but clearly list the columns that need to be queried.
SELECT id, name FROM customers;Configuration tuning:
According to the server hardware configuration and database load, adjust MySQL configuration parameters, such as cache size, number of connections, etc., to improve performance and concurrent processing capabilities.
Query cache:
Turn on the appropriate query caching function to cache frequently accessed query results and reduce frequent queries to the database.
1. Check whether the query cache is enabled: First, you need to confirm whether MySQL has enabled the query cache. This can be determined by checking the value of the query_cache_type configuration parameter:
SHOW VARIABLES LIKE 'query_cache_type'; If the query result value is ON it means query cache is enabled; if it is OFF it means it is not enabled.
2. Configure query cache: If query cache is not enabled, relevant configuration is required. Modify the MySQL configuration file (my.cnf or my.ini), find the [mysqld] section, and add or modify the following lines in it:
query_cache_type = 1 query_cache_size = 64M query_cache_type Set to 1 to enable query cache, query_cache_size is the memory used for query cache Size, adjust according to server resources.
3. Restart MySQL: After saving the changes, restart the MySQL service to make the configuration take effect.
4. Check the hit rate of the query cache: Use the following command to view the query cache information, including the cache hit rate.
SHOW STATUS LIKE 'Qcache%'; Among them, Qcache_hits is the number of successful hits in the query cache, Qcache_inserts is the number of insertions into the query cache, Qcache_not_cached is the number of queries that are not suitable for caching. These statistics can be used to evaluate the effect of query caching.
Sub-table and sub-library:
Through data sharding and distributed architecture, data is split into multiple databases to reduce the load pressure on a single database and improve concurrent processing capabilities.
Read and write separation:
Use master-slave replication or cluster architecture to separate read operations and write operations to different database servers to improve read performance and concurrent processing capabilities.
Regular maintenance and optimization:
Regularly perform database maintenance tasks, such as table optimization, index reconstruction, database statistics, etc., to clean up useless data and ensure the healthy operation of the database.
Vertical split and horizontal split:
Depending on the characteristics and load of the data, you can consider splitting the data in the large table into multiple small tables, or in some cases horizontally splitting the database to distribute the data to different storage nodes.