Sharding is suitable for scenarios where the data volume is extremely large and needs to be scaled horizontally, reducing the load by splitting the database; partitioning is suitable for optimizing single-table query performance and dividing physical blocks according to rules. Sharding is split according to user ID, region or time and requires middleware support. It is suitable for scenarios with high write pressure and acceptable complexity. Partitions include RANGE, LIST, HASH and other types, which improve query efficiency and are transparent to applications, but cannot solve the write bottleneck; if the data volume is large and the expansion is required for sharding, if the query efficiency decreases significantly, partitioning is preferred; pay attention to key selection, partition number control, shard expansion strategy and monitoring and maintenance when implementing.
MySQL is the core database of many applications, but as the amount of data and visits increases, stand-alone performance often becomes a bottleneck. To solve this problem, sharding and partitioning are two commonly used strategies. They are not mutually exclusive, but they apply differently. This article will talk about how to use them to extend MySQL.

What is sharding? What scenario is suitable for?
The essence of sharding is to split a large database into multiple small databases, each small database only processes part of the data. For example, you can divide the data into different database instances by user ID, so that each instance has a lighter load.
Common practices:

- Divide data by user ID, region, time and other dimensions
- Each shard is deployed independently without interfering with each other
- When querying, decide which fragment to check according to the rules
Suitable for scenarios:
- The data volume is so large that it can't hold on to the stand-alone machine
- High writing pressure, obvious bottleneck in single-machine writing performance
- Can accept certain complexity management costs
Note:

- After sharding, cross-slicing JOIN and transaction processing will become complicated
- Once the sharding strategy is established, the cost of later adjustment is high
- Need to support routing logic by middleware (such as Vitess, MyCat) or application layer
What's going on with partitioning? What are the benefits?
Partitioning is to divide data into multiple physical blocks according to certain rules within the same table . For example, partition by time and save a piece of data for each month alone, you can only scan the required part when querying.
The partition types supported by MySQL include:
- RANGE: partition by range, such as by time or numerical interval
- LIST: Partition by specified value list
- HASH: Distribute data evenly according to the hashing algorithm
- KEY: Similar to HASH, but using MySQL internal functions
Advantages:
- Improve query performance, especially when there is partitioning and cropping
- More convenient for data archiving and cleaning (such as directly deleting a partition)
- Transparent to the application layer, no need to change the query logic
limitation:
- The partition is on the same instance and cannot solve the write bottleneck
- Not all queries can benefit, and the partition key must be used well.
- Too many partitions will affect management efficiency and performance
How to choose shard vs partition?
There is no standard answer to this question, the key depends on your business needs.
Prioritize partitioning:
- The data volume is not particularly large, but the query efficiency is significantly reduced
- There are clear partition keys, such as time, region, etc.
- I hope to make minimal changes to the application layer
Priority to sharding:
- The data volume is extremely large, and the stand-alone machine cannot hold on
- High write pressure, need to expand horizontally
- Can accept the complexity brought by sharding, such as middleware management and cross-chip query restrictions
It can also be used in combination: for example, partitioning within a shard can not only scale horizontally, but also optimize query efficiency within a single shard.
Some precautions when implementing
1. The selection of shard keys and partition keys is the key
- If you choose well, the performance will be significantly improved; if you choose poorly, the complexity will be increased
- Try to select the commonly used fields in the query as keys
2. Too many partitions
- Too many partitions will increase management overhead and will also affect performance
- It is generally recommended to control it within a few dozen
3. The sharding strategy should consider the expansion issue
- The number of early shards is too small, so it is troublesome to expand later
- You can reserve some sharding space, or use strategies such as consistency hashing.
4. Monitoring and maintenance must not be missing
- Partitions and shards need regular maintenance, such as rebuilding indexes, migrating data, etc.
- There must be a monitoring mechanism to promptly detect hot spots or performance bottlenecks
Basically that's it. Sharding and partitioning are both effective means to extend MySQL. The key is to choose the right strategy based on business characteristics. It is not that the more complicated the better, but that the more appropriate the better.
The above is the detailed content of Scaling MySQL with Sharding and Partitioning Techniques. For more information, please follow other related articles on the PHP Chinese website!

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