Strategies for MySQL Query Performance Optimization

MySQL query performance optimization needs to start from the core points, including rational use of indexes, optimization of SQL statements, table structure design and partitioning strategies, and utilization of cache and monitoring tools. 1. Use indexes reasonably: create indexes on commonly used query fields, avoid full table scanning, pay attention to the combined index order, do not add indexes in low selective fields, and avoid redundant indexes. 2. Optimize SQL queries: Avoid SELECT *, do not use functions in WHERE, reduce subquery nesting, and optimize paging query methods. 3. Table structure design and partitioning: select paradigm or anti-paradigm according to read and write scenarios, select appropriate field types, clean data regularly, and consider horizontal tables to divide tables or partition by time. 4. Utilize cache and monitoring: Use Redis cache to reduce database pressure, enable slow query log analysis bottleneck SQL, and combine connection pooling and batch operations to improve efficiency.

MySQL query performance optimization is actually not that mysterious, the key is to start from several core points. If the index is correct, the query will be naturally fast; SQL is well written and execution is high; system configuration and table structure design will also affect the final performance. The following aspects are the most worthwhile places in daily development to optimize.

1. Use index rationally

The more indexes, the better, but you need to be "used". For example, adding indexes to fields often used for query conditions (such as user ID and timestamp) can greatly improve the search speed.

• Avoid full table scanning : When there is no suitable index, the database will look up row by line, which is inefficient.
• Pay attention to the order of combined indexes : For example, if you create a joint index (user_id, create_time) and use create_time only for querying, this index will not work.
• Do not add indexes in low-selectivity fields : for example, the gender field only has two values: male/female, and the indexing effect is not great.

A common misunderstanding is that adding indexes to each field can improve performance, which actually leads to slow writes and may waste storage space.

2. Optimize SQL query statements

Many times, slow queries are not because of the large amount of data, but because SQL is not efficient enough. Some writing methods will make MySQL do a lot of extra work.

Frequently asked questions include:

• Use SELECT * : Only the required fields are taken to reduce network transmission and memory consumption.
• Use functions in WHERE conditions: for example, WHERE YEAR(create_time) = 2023 , which will cause the index to fail.
• Subqueries are too deep nested: appropriately rewritten into JOIN operations, which is usually more efficient.
• The offset of pagination query is too large: for example, LIMIT 1000000, 10 , it is recommended to combine primary keys or timestamps to segment query.

For example, if an order table has millions of data, and directly use LIMIT offset, size to check the page tens of thousands of pages, the response will be very slow. At this time, you can consider first checking out the primary key ID and then querying the specific data accordingly.

3. Table structure design and partitioning strategy

A good table structure design can not only improve query efficiency, but also reduce redundant data and maintenance costs.

• Appropriate normalization/De-normalization : In scenarios where more reads and less writes, appropriate redundancy can reduce the number of JOINs.
• Choose the appropriate field type : For example, it is not appropriate to use CHAR(10) to save the mobile phone number, and you should use VARCHAR or integer type.
• Regularly clean and archive historical data : Too much old data will affect overall performance and can be managed by partitioning by time.

For very large tables, you can consider using horizontal partition tables or partition tables . For example, partition the log data by month, so that when checking data for a specific time period, unrelated partitions can be skipped and efficiency can be improved.

4. Utilize cache and monitoring tools

Sometimes optimizing SQL and indexes has reached its limit, and you can use external means to relieve the pressure on the database.

• Query cache (although MySQL 8.0 is abandoned) : If it is a scenario where there is more reads and less writes, you can use cache middleware like Redis to reduce the burden on the database.
• Slow query log analysis : Turn on the slow query log, and use mysqldumpslow or pt-query-digest tools to find the SQL that is dragging you down.
• Connection pooling and batch operations : Reduce the overhead of frequent connection establishment and improve write efficiency by inserting multiple records at a time.

For example, you can run the slow query analysis script regularly every day, automatically filter out the most time-consuming SQL, and give priority to optimizing these bottlenecks.

Basically that's it. MySQL performance optimization is not something that can be achieved overnight, but is more based on daily accumulation and continuous observation. The key is to know where problems are prone to problems, and then make targeted adjustments.

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