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Table of Contents
Read and write separation: alleviate the pressure of query
Library and table sub-table: breaking through the performance limit of single table
Cache and asynchronous processing: Reduce direct database pressure
Other optimization methods: Details determine success or failure
Home Database Mysql Tutorial Approaches to Scaling MySQL Database for High Load

Approaches to Scaling MySQL Database for High Load

Jul 08, 2025 am 12:54 AM
mysql database

MySQL stand-alone bottlenecks can be solved through read and write separation, library separation, cache and asynchronous processing and other optimization methods. 1. Read and write separation is implemented through master-slave replication. The master library processes write requests, and the slave library processes read requests, and combines connection pools to improve efficiency, but attention should be paid to the asynchronous replication delay problem; 2. The sub-base sub-table includes vertical split (split by field) and horizontal split (split by rules), which is suitable for large data scenarios and requires middleware to handle complex queries; 3. Caching can reduce database pressure, use Redis or Memcached to cache hotspot data, and combines message queues to asynchronously process non-real-time write operations; 4. Other optimizations include slow query analysis, parameter tuning, connection pool management and SQL optimization, and detailed processing is crucial to performance improvement.

Approaches to Scaling MySQL Database for High Load

MySQL is the underlying core of many applications. When business growth and visits increase, stand-alone MySQL will soon become a bottleneck. To support high concurrency and large data volume scenarios, we must make adjustments based on architecture and strategy. The most direct way is not to change the database, but to choose the appropriate expansion method based on the business characteristics.

Approaches to Scaling MySQL Database for High Load

Read and write separation: alleviate the pressure of query

In most systems, read operations are much more than write operations . At this time, you can use "master-slave copy" to achieve read and write separation. The master library processes write requests, and multiple slave libraries are responsible for reading requests, which can effectively reduce the pressure on a single node.

Approaches to Scaling MySQL Database for High Load
  • The master library synchronizes data changes to the slave library
  • The application layer or middleware automatically determines whether it is read or written, and distributes it to different nodes
  • Can be used in conjunction pools to improve efficiency

It should be noted that MySQL replication is asynchronous by default and may have delays. If some businesses have high consistency requirements (such as payment), semi-synchronous replication or introduction of cache guarantees must be considered.


Library and table sub-table: breaking through the performance limit of single table

When the data volume of a table reaches tens of millions or even hundreds of millions, the response speed will slow down even if the index is added. At this time, vertical splitting and horizontal splitting need to be considered.

Approaches to Scaling MySQL Database for High Load
  • Vertical split : Split the large table into multiple small tables according to the fields, and pick up the uncommon fields or large fields separately
  • Horizontal splitting : Spread the data of a table into multiple physical tables according to rules, such as taking the module by user ID

For example, if the data volume of an order table is too large, it can be distributed to multiple subtables according to the user ID hash. In this way, only one subset is checked for each query, and the performance will be much better.

However, after splitting the table, cross-table query and aggregation statistics become complicated, and they need to be processed by middleware (such as MyCat, ShardingSphere) or the business layer itself.


Cache and asynchronous processing: Reduce direct database pressure

Many times, the reason why the database cannot withstand the pressure is that a large number of repeated requests are hit on it. Adding the cache layer is a very effective way at this time.

  • Use Redis or Memcached to cache hotspot data, such as user information, configuration items, etc.
  • Write operations can be processed asynchronously using message queues to avoid instantaneous peak writing

For example, the product details page has a lot of visits, and if you check the database every time, the pressure will be great. You can check the cache first, and then return to the source database, and update the cache.

In addition, for some non-real-time write operations, such as logging and integral changes, they can be thrown into the message queue and processed slowly to give the database a breath.


Other optimization methods: Details determine success or failure

In addition to architectural adjustments, daily maintenance and optimization must not be missing:

  • Regularly analyze slow query logs, optimize SQL and indexes
  • Reasonably set parameters such as connection number and buffer pool size
  • Use a connection pool to manage database connections to avoid frequent disconnections
  • Archive large tables and migrate historical data to separate storage

Sometimes a small SQL change can bring dozens of times the performance improvement. For example, replace SELECT * with a specified field, avoid full table scanning, reduce the number of JOINs, etc.


These methods can be used in combination according to business needs and may not be all used from the beginning. First solve the most obvious bottleneck, and then gradually upgrade the architecture. Basically all this is not complicated but easy to ignore.

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