MySQL Data Sharding

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

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.

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.

SingleStoreDB

SingleStoreDB is a real-time, distributed SQL database that unifies transactions and analytics in a single engine to drive low-latency access to large datasets, simplifying the development of fast, modern enterprise applications. SingleStoreDB provides support for large scale databases with analytics and takes care of most configuration, and also supports various distributions for deployment.

SingleStore is MySQL wire compatible and offers the familiar syntax of SQL, but is based on modern underlying technology that allows infinitely higher speed and scale versus MySQL. This is one of the many reasons SingleStore is the #1, top-rated relational database on TrustRadius.

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