Learn more about MySQL data sharding — including what it is, specific sharding techniques, pros and cons of using sharding, and more.

MySQL Data Sharding: What Is it?

As data increases in MySQL, it’s not uncommon for schema performance to deteriorate. This deterioration is caused by:
  1. Increase in Throughput. As data volume increases, the size of indices also grows — and at a certain point, simple queries have unprecedented return times.
  2. Data Redundancy. One traditional optimization technique is to repeat data instead of using foreign keys, reducing return time for queries. But, it’s a poor practice that diminishes the purpose of relational databases.
  3. Bottlenecks. As data volume increases bottlenecks occur in batched tasks, backups or any intensive task associated with the database. This  can result in an overall downgrade in performance and efficiency of the system.
  4. Storage Congestion. Single server optimization exponentially increases database size, which considerably impacts database performance.
Sharding can be used to overcome these challenges. Data sharding is a technique where data is split into mutually exclusive segments, which is achieved by splitting tables into horizontal chunks. In a distributed environment, these chunks can be placed on partitions — and then nodes — which would balance the throughput.

MySQL Data Sharding: What Are the Sharding Techniques?

Before we discuss practical steps for sharding, let’s briefly take a look at different types of sharding: 
  1. Hash Sharding. Hash functions are used for distribution of data partitions, and placement of data in those partitions.
  2. Range Sharding. In range sharding, a particular length is defined for a partition, which consists of a range of keys. Partitions do not need to be equal in length.
  3. Geo Sharding. In this data split, stored procedures are used to format data into its required form, and then distributed among partitions.
In MySQL, sharding can be achieved with the following steps:
  1. Key Selection. This step can be deployed using either hash or range techniques (depending on the use case). Security-intensive applications often use hash functions.
  2. Schema Modifications. Depending on the Key section, the schema needs to be modified. This can be accomplished with ALTER commands.
  3. Distribution on Nodes. A scheme needs to be created at the application layer, which places data in the correct partition and retrieves it when required.

MySQL Data Sharding: What Are the Pros & Cons?

The follow are pros for MySQL data sharding:
  1. Sharding reduces throughput considerably when applied properly.
  2. Sharding can help reduce your storage footprint.
  3. Sharding allows node balancing — and if shards are optimally placed, users can have access to relevant data and the ability to handle complex queries.
There are some instances where MySQL data sharding is not the best approach, and only presents further challenges:
  1. Establishing an analytics interface over a sharded database is very difficult due to limitations on JOINS and Aggregations.
  2. Sharded databases in MySQL are mostly ACID (Atomicity, Consistency, Isolation and Durability) compliant.
  3. MySQL does not provide automated sharding — sharding is normally implemented at the application layer. That means development teams are responsible for the entirety of sharding and maintenance. As such, MySQL is not suited in situations where sharding is required.


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