The optimization of MySQL in ERP systems requires four aspects: structural design, parameter adjustment, regular maintenance and avoiding performance traps. 1. Reasonably design the database structure, use appropriate normalization, establish indexes and avoid frequent query of large fields; 2. Adjust configuration parameters such as innodb_buffer_pool_size, max_connections, etc. to adapt to business load; 3. Regularly analyze and optimize tables, enable slow query logs, and use monitoring tools to continuously track performance; 4. Avoid using functions in the WHERE clause, reduce SELECT *, adopt batch operations, and control transaction granularity, thereby improving overall system efficiency.

ERP systems rely very strongly on databases, and MySQL, as one of the commonly used open source databases, carries the storage and query of a large amount of business data. However, in order to make it run stably and efficiently in ERP, it is not enough to rely on the default configuration alone, and targeted optimization is required.

1. Reasonably design the database structure

ERP systems usually involve multiple modules, such as finance, inventory, procurement, sales, etc., and the correlation between data tables is complex. If the table structure is unreasonable, it will lead to problems such as slow query, frequent table locking, and data redundancy.

• Use standardized design but avoid over-the-top : proper normalization can reduce data redundancy, but over-standardization can increase the overhead of associated queries.
• Indexing commonly used query fields : especially primary keys, foreign keys and fields that are often used for filtering, such as order status, customer number, etc.
• Avoid frequent query of large fields : fields such as TEXT and BLOB should not be placed in the main table with frequent query, and can be disassembled into the extended table.

For example, if each query is included in the order table, the product details description (TEXT type) will slow down the overall performance and the description information should be disassembled separately.

2. Adjust MySQL configuration parameters

The default configuration is suitable for common scenarios, but in data-intensive systems like ERP, it needs to be adjusted according to server hardware and business load.

The key parameters are recommended as follows:

• innodb_buffer_pool_size : It is recommended to set it to 50%~70% of physical memory, which is used to cache data and indexes to reduce disk access.
• max_connections : Adjust according to the number of concurrent users to avoid excessive connections causing resource competition.
• query_cache_type and query_cache_size : MySQL 8.0 has removed the query cache, but if it is version 5.7, it can be enabled appropriately, but be careful that tables that are updated frequently are not suitable for use.

In addition, log-related parameters such as innodb_log_file_size and sync_binlog will also affect the write performance. It is recommended to tune according to the business write volume.

3. Regular maintenance and monitoring

After the ERP system is running for a long time, the data continues to grow, and problems such as index fragmentation, slow query, and deadlock will gradually be exposed.

• Regularly analyze and optimize tables : Use ANALYZE TABLE and OPTIMIZE TABLE to re-index and defragment.
• Turn on slow query logs : locate SQL with long execution time and perform targeted optimization.
• Use monitoring tools : such as SHOW ENGINE INNODB STATUS , which comes with MySQL or third-party tools such as Prometheus Grafana, which monitors connection count, QPS, lock waiting and other situations in real time.

For example, if you find that a query often appears in the slow query log, you can consider adding indexes or rewriting SQL instead of blindly increasing hardware resources.

4. Avoid common performance traps

In ERP, some performance problems are actually caused by design or usage.

• Avoid function operations on fields in WHERE clauses : For example, WHERE DATE(create_time) = '2024-01-01' , which will cause the index to fail.
• **Reduce SELECT ***: Only take the required fields to reduce the amount of data transmission.
• Batch operations replace single operations : for example, when inserting or updating large amounts of data, use batch statements to reduce network round trips and transaction overhead.

Also, pay attention to the granularity of the transaction to avoid operating too much data in a transaction, causing the lock to wait or even deadlock.

Basically that's it. The performance of MySQL in ERP depends largely on pre-design and post-maintenance, and is not complicated but easily overlooked.

The above is the detailed content of Optimizing MySQL for Enterprise Resource Planning (ERP) Systems. For more information, please follow other related articles on the PHP Chinese website!