MySQL, as a widely used relational database, directly impacts the response speed of business systems. When slow database access occurs, a systematic approach involving multiple dimensions is needed for problem localization and optimization. This article will delve into the common causes of MySQL performance degradation and provide practical optimization solutions.

Diagnosing database performance issues should follow a scientific methodology. First, it's necessary to identify the scope of the problem: is it a global performance degradation or a slow query? Establish a performance baseline using monitoring tools, recording key metrics such as QPS, connection count, and query response time under normal conditions to provide a basis for subsequent comparative analysis.

Hardware resource bottlenecks are a common cause of database performance degradation. A CPU utilization consistently exceeding 70% may indicate insufficient query optimization or a large amount of computational operations. Insufficient memory triggers frequent disk swapping, significantly increasing IO wait time. Disk IO performance, especially when using mechanical hard drives, often becomes the database performance bottleneck.

Commands for monitoring system resource usage:

`top -p $(pgrep mysqld)`
`iostat -x 1`

Query performance issues account for the vast majority of reasons for slow database performance. The lack of effective indexes is a primary problem, as full table scans consume significant I/O resources. Complex joins and subqueries can lead to suboptimal execution plans. Sorting large amounts of data, especially filesort, consumes substantial CPU and memory resources.

Analyzing slow query logs is an effective way to locate problematic queries. Enable slow query logging and set appropriate thresholds:

`sql SET GLOBAL slow_query_log = 1; SET GLOBAL long_query_time = 1; SET GLOBAL log_queries_not_using_indexes = 1;`
Use the `EXPLAIN` command to analyze query execution plans:
`sql EXPLAIN SELECT * FROM users WHERE name LIKE 'john%';`

Improper index design severely impacts query efficiency. Indexes should be designed based on query patterns, covering commonly used WHERE conditions and JOIN fields. Too many indexes increase write operation overhead and consume additional storage space. Index ineffectiveness, such as using functions or expressions on indexed fields, will prevent the index from being used.

SQL statements to check index usage:
SQL SELECT * FROM sys.schema_unused_indexes;
SELECT * FROM information_schema.statistics WHERE table_name = 'your_table';

Inappropriate database configuration can limit performance. Setting the InnoDB buffer pool size too small will lead to frequent disk I/O. Insufficient connection count will cause connection waits, while setting it too high may exhaust system resources. Log file size and flushing strategy will affect write performance.

Key parameters for optimizing InnoDB configuration:

SQL SET GLOBAL innodb_buffer_pool_size = 70-80% of memory;
SET GLOBAL innodb_log_file_size = 1G;
SET GLOBAL innodb_flush_log_at_trx_commit = 2;

Lock contention is particularly prominent in high-concurrency scenarios. Row-level lock waits will block query execution, and table-level locks will prevent concurrent access. Deadlock detection and resolution mechanisms have a certain impact on system performance.

Querying lock status:
sql SHOW ENGINE INNODB STATUS;
SELECT * FROM information_schema.INNODB_LOCKS;

Database architecture design flaws can lead to long-term performance problems. Lack of appropriate partitioning strategies results in excessively large data volumes in single tables. Failure to implement read/write separation concentrates all load on a single instance. Inappropriate data type selection, such as excessively large text fields or incorrect data types.

Example of implementing a table partitioning strategy:

sql CREATE TABLE logs ( id INT NOT NULL AUTO_INCREMENT,
log_date DATE NOT NULL,
message TEXT,
PRIMARY KEY (id, log_date)
) PARTITION BY RANGE (YEAR(log_date)) ( PARTITION p0 VALUES LESS THAN (2023),
PARTITION p1 VALUES LESS THAN (2024)
);

Connection management issues can impact overall database performance. Improper connection pool configuration leads to frequent new connection creation. Long-running uncommitted transactions hold lock resources. Applications may not properly close database connections.

Optimize connection management configuration:

sql SET GLOBAL max_connections = 500;
SET GLOBAL wait_timeout = 60;
SET GLOBAL interactive_timeout = 60;

Inaccurate statistics can cause the optimizer to select an incorrect execution plan. Outdated statistics can cause the optimizer to underestimate or overestimate data volume. Automatic statistics collection may fail due to frequent data changes.

Methods for updating statistics:
sql ANALYZE TABLE table_name;
OPTIMIZE TABLE table_name;

System architecture issues require consideration of the overall design. A lack of caching strategies leads to all requests directly accessing the database. Batch processing tasks compete for resources with online services. Network latency and bandwidth limitations impact the performance of distributed architectures.

Example configuration for implementing a caching strategy:

sql -- Use query caching (before MySQL 8.0)
SET GLOBAL query_cache_size = 67108864;
SET GLOBAL query_cache_type = 1;

MySQL performance optimization is an ongoing process requiring regular monitoring and adjustments. Establish a comprehensive monitoring system, including database performance metrics, system resource usage, and business-level response times. Develop regular maintenance plans, including index rebuilding, statistics updates, and defragmentation.

Performance optimization should be based on real load testing, not theoretical speculation. Use professional testing tools to simulate production environment load and verify the optimization effect. Record detailed steps and effect evaluations for each optimization, and build a knowledge base for future reference.

Solving database performance issues requires a systematic approach and patient debugging. Through scientific monitoring, accurate problem localization, and targeted optimization measures, the access speed of MySQL databases can be significantly improved, providing stable and efficient data support for business systems.