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MySQL performance tuning practice

MySQLperformanceTuning Practical Practice: A Comprehensive Guide from Theory to Practice

MySQL is widely usedRelational databaseThe management system has its performance directly affects the response speed and user experience of the entire application. This article aims to provide a comprehensive guide to MySQL performance tuning from theory to practice, coveringPerformance Analysis, system parameter adjustment, query optimization, architecture design and other aspects, help you to diagnose and optimize in a targeted manner when facing performance bottlenecks.

1. Performance analysis and monitoring

1.1 Monitoring indicators and tools

  • System resource monitoring: Pay attention to the use of system resources such as CPU, memory, disk I/O, and network. Commonly used system monitoring tools include top, htop, iostat, vmstat, etc.
  • MySQL state variable: Use the SHOW GLOBAL STATUS command to view the internal status of MySQL, such as querying cache hit rate, number of connections, thread state distribution, lock waiting situation, index usage statistics, etc.
  • Slow query log: Turn on slow query log (slow_query_log) to record queries whose execution time exceeds the threshold (long_query_time). By analyzing slow query logs, positioning time-consuming queries.
  • Performance analyzer: For example, pt-query-digest in Percona Toolkit can summarize and analyze the slow query logs to find the most resource-consuming queries and potential problems.

1.2 Performance analysis tools

  • EXPLAIN: Use EXPLAIN or EXPLAIN FORMAT=JSON to analyze the query execution plan to check whether to use indexes, scanned row counts, temporary tables, file sorting, etc.
  • Performance Schema: MySQL's built-in performance analysis framework provides detailed query execution statistics, such as CPU, I/O consumption, lock waiting, etc.
  • Profiler: Enable query profiling function (performance_schema.events_statements_profiling) to obtain the detailed execution time distribution of a single query.

1.3 Instance-level monitoring and alarm

  • Cloud service monitoring: For example, the instance monitoring panel provided by Alibaba Cloud RDS for MySQL includes key indicators such as CPU utilization, IOPS, connection count, QPS, disk space, etc., and supports customized alarm rules.
  • Third-party monitoring tools: such as Prometheus, Grafana, etc., integrate MySQL Exporter to collect performance data to achieve refined monitoring and visual display.

2. System parameter tuning

2.1 Interpretation of key parameters

  • innodb_buffer_pool_size: InnoDB buffer pool size, usually set to 60%-80% of the total server memory, used to cache table data and indexes, and reduce disk I/O.
  • query_cache_size: query cache size, suitable for scenarios with small result sets and more duplicate queries. However, pay attention to cache failure, management overhead and other issues. If necessary, you can turn off query cache.
  • max_connections: Maximum number of connections, set reasonably according to concurrent visits. Too high may cause memory overflow, and too low may cause connection rejection.
  • innodb_flush_log_at_trx_commit: Transaction log refresh policy affects data security and write performance. Usually set to 1 (synced once per second) or 2 (synced every time a transaction is committed).

2.2 Parameter tuning steps

  • Baseline settings: Preliminary setting of parameter values ​​based on server hardware configuration and business load characteristics.
  • Monitoring verification: Observe the system resource usage and MySQL status variables, and verify whether the parameter settings are reasonable.
  • Gradually adjust: For the problems found, adjust the relevant parameters in a targeted manner. The adjustment range should not be too large each time to avoid causing new problems.
  • Performance comparison: Perform performance benchmark tests before and after adjustment, and compare the tuning effects, such as TPS, response time, etc.
  • Continuous optimization: With business development and hardware upgrades, review the parameter settings regularly and make necessary adjustments.

3. Query optimization

3.1 SQL writing specifications

  • Avoid full table scanning: Use indexes to filter data and reduce unnecessary scans.
  • Use JOIN reasonably: minimize the number of JOINs and avoid Cartesian products. Priority is given to INNER JOIN, and LEFT JOIN or RIGHT JOIN if necessary.
  • Avoid using negative conditions, functions, and complex expressions in WHERE clauses: these may cause index failure.
  • Use LIMIT paging: For large table queries, add LIMIT to limit the number of results returned, and avoid file sorting with ORDER BY + primary key index.

3.2 Index optimization

  • Select the appropriate column to create an index: consider factors such as query conditions, data distribution, and update frequency, and give priority to creating indexes for high-frequency query conditions, connection fields, and sort fields.
  • Use overwrite index: The index contains all columns required for query, avoid backing tables, and improve query speed.
  • Regularly check and maintain indexes: Clean invalid indexes, create or adjust indexes based on new query patterns, monitor and rebuild indexes in time to reduce fragmentation.

3.3 Query rewrite and refactor

  • Decompose complex queries: Split large queries into multiple small queries, leverage caches, reduce lock competition, and facilitate optimizer processing.
  • Batch operations replace cyclic queries: For example, using statements such as INSERT … SELECT, UPDATE … WHERE IN, etc., to reduce network round-trip and lock competition.
  • Use temporary or derived tables: Process complex intermediate results to avoid multiple calculations or full table scans.

IV. Architectural design and expansion

4.1 Vertical split and horizontal split

  • Vertical split: Split a large table into multiple small tables according to the business module or data type, and spread on different servers.
  • Horizontal splitting: According to certain rules (such as user ID modulo, region, etc.), data is evenly distributed to multiple tables or databases and accessed through routing rules.

4.2 Read and write separation

  • Master-slave replication: Through MySQL's replication function, write operations (master libraries) and read operations (slave libraries) are separated, reducing the pressure on the main library and improving concurrent reading capabilities.
  • Read-only examples: For example, the read-only instance provided by Alibaba Cloud RDS for MySQL automatically synchronizes the main library data without the need to build a replication environment by yourself.

4.3 Cache and middleware

  • Query cache: such as Redis and Memcached, cache hotspot data and reduce database access.
  • Database middleware: such as ShardingSphere and MyCAT provide data sharding, read and write separation, transaction management and other functions to simplify distributed database management.

5. Summary

MySQL performance tuning is a system project that involves monitoring and analysis, parameter adjustment, query optimization, architecture design and other links. In practice, the above strategies should be comprehensively used in combination with specific business scenarios, and continuously monitor, analyze and adjust to achieve the best performance status. At the same time, embraceCloud Serviceand emerging technologies, such as cloud databases, database agents, intelligent optimization tools, etc., can further improve tuning efficiency and ensure the high performance, high availability and easy management of the database system.