MySQL itself does not have built-in data sharding function, but can be implemented through architectural design and tools. Data sharding is to split large table data into multiple databases or tables according to rules to improve performance. Common implementation methods include: 1. Hashing slices by user ID, which are evenly distributed but troublesome in capacity expansion; 2. Shaving slices by range, which are suitable for time-class fields but are easy to hot spots; 3. Consistent hashing algorithm, which reduces the amount of expansion migration but is complex in implementation. After sharding, cross-slice query, data migration, distributed transactions and other problems need to be dealt with. Middleware such as MyCat, Vitess or application layer logic processing can be used, and shard keys should be selected reasonably, shard balance should be monitored, excessive sharding should be avoided, and backup strategies should be improved.

MySQL itself does not directly provide the function of data sharding, but through the coordination of some architectural designs and tools, sharding can be achieved. A common practice is to apply layer control sharding logic or use middleware proxy sharding operations .

What is data sharding?

Data sharding is to split the data of a large table into multiple databases or tables according to certain rules, and each shard stores part of the data. The advantage of this is that it can alleviate the pressure on a single library, improve query performance, and support a larger data volume.

MySQL itself is a stand-alone database and does not have an automatic sharding mechanism built in, but it can be implemented in the following ways.

How to implement data sharding in MySQL?

1. Hash shards by user ID

This is one of the most common sharding strategies. For example, modulo a certain value by deciding which shard the data falls on:

 shard_id = user_id % 4

This allows the user data to be evenly distributed among 4 shards. Each shard has an independent database instance or table.

Advantages: Even distribution, simple implementation disadvantages: Recalculate hashing when expanding capacity, making it troublesome to migrate data

2. Range Sharding

Applicable to fields with sequence characteristics such as time and order numbers. For example, by registration time:

• User registration time before 2020 is put on shard1
• 2020-2021 put on shard2
• And so on

Advantages: Suitable for time range query Disadvantages: It is easy to cause hot spots (the latest data is concentrated in a certain fragment)

3. Use a consistent hashing algorithm

In order to solve the problem of difficulty in scaling ordinary hash, consistent hashing can be used. It only affects neighboring nodes when nodes increase or decrease, reducing data migration.

Suitable for large-scale distributed systems, but with slightly more complexity.

Frequently Asked Questions and Coping Methods after Sharding

1. Cross-shash query efficiency is low

When the query conditions involve multiple shards, for example, to check the order information of all users, you have to access multiple shards and merge the results.

Solution:

• Try to avoid cross-slice queries and design shard keys in advance
• For statistical requirements, you can create a summary table separately or use a big data platform to process it

2. High data migration cost

As the business grows, it may be necessary to add new shards or adjust sharding strategies.

suggestion:

• In the early stage, sufficient number of shards will be reserved (for example, 64 or 128 virtual shards)
• Reduce migration costs with consistent hashing
• Plan migration scripts and rollback plans in advance

3. Distributed transactions are difficult to manage

MySQL natively supports local transactions, but cross-shash transactions require the introduction of two-stage commits or use of other frameworks.

Recommended plan:

• Use distributed transaction frameworks such as Seata and TCC
• Or use the final consistency design and asynchronous compensation updates

Common sharding tools and middleware

1. MyCat/Atlas/DBProxy

These are open source database middleware that can help you realize functions such as reading and writing separation, library and table separation. They act externally like a unified MySQL service, automatically routed to the correct shard internally.

2. Vitess (Open Source of Google)

More complex solutions, suitable for hyper-large deployment, and support advanced functions such as dynamic sharding and automatic balance.

3. Application layer custom logic

Many small and medium-sized projects will choose to deal with sharding logic at the code level, such as encapsulating sharding rules in ORM. Although the development cost is slightly higher, it is flexible.

Some practical suggestions for shard optimization

• It is important to choose the right shard key : usually the primary key or high-frequency query field is selected to avoid causing queries to be scattered.
• Maintain the size of shards : Regularly monitor the amount of data of each shard to prevent "hot and cold uneven".
• Don’t over-slice : Too many shards will increase the complexity of operation and maintenance. You can do horizontal splits first and then consider vertical splits in the early stage.
• The backup and recovery strategies must also be adapted to the shard structure : you cannot only back up the main library, and each shard must have a corresponding backup mechanism.

Basically that's it. MySQL sharding is not particularly complicated, but details are easy to ignore, especially the extension, maintenance, query and other problems encountered during actual operation, which need to be planned in advance.

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