Database Management – Differences between Database Sharding and Partition

E-commerce websites and applications like Amazon see an exponential increase in traffic during the holiday season. The sudden surge in requests will require your application to access the database concurrently to accommodate every attempt to read or save information. Your database will become a bottleneck when volumes become too large to handle. It will slow down the application and impact customer experience. 

Your database must scale as dynamically as the incoming traffic for seamless performance. The database architecture type you choose decides the scalability of your application. Most organizations rely on two popular database architecture techniques – sharding and partitioning. These are both methods of dividing a database to enhance application performance through improved availability. 

This article will explore sharding and partitioning techniques and how database sharding helps achieve horizontal scalability.

What is database partitioning?

Also known as Vertical Partitioning, it is simply a process of breaking down huge tables into smaller, individual sections so that queries have less data to scan. By managing these smaller tables separately, you can improve application performance and database availability.

Database partitioning is used when queries must go through all the columns containing extensive text or BLOB (Binary Large Objects) to refine SQL Server throughput time. Partitioning can also be used to protect sensitive information. You can store secrets in a partition and then restrict access to it.

What is database sharding?

Also known as Horizontal Partition, Sharding is a method of database division that involves replicating schema to break down large datasets into smaller chunks to be distributed across multiple instances for easy access. These smaller data blocks are known as shards and are associated with a shard key. This key identifier helps the query understand which server stores the required data. 

Each division of the database comprises the same schema and columns. However, its data will be unique and independent of data stored in other partitions. To simplify horizontal partition, let’s consider a corporate office. It will comprise professionals from different backgrounds, skills, and experience levels. You can implement sharding by separating them based on experience. This means freshers will be stored in instance A, resources with 1-5 years of experience will be stored in instance B, and so on. 

Difference between sharding and partitioning

Both sharding and partitioning are database architecture models that aim to simplify large datasets by dividing them into smaller sections. However, they both follow different routes to achieve it. 

Sharding a database will create data segments spread across multiple servers or computers. But with vertical partitioning, you split large tables into smaller ones stored in the same database instance. Sharding makes scaling database capacity easy by distributing it across multiple machines.

How does sharding facilitate horizontal scalability?

Typically, databases store information in the form of columns and rows for easy query management. With database sharding, you split a single table into multiple smaller tables. Each table now will contain unique rows but with the same columns. It means every shard comprises a unique dataset with the same schema. These shards are stored individually across multiple locations called nodes. Let’s take a small example. Consider the below dataset of employees of an organization:

This table can be split into multiple tables and stored on different servers like below:

Server 1

Server 2

Each data partition is known as a shard, associated with a shard key that forms the basis of dividing a dataset.

Implementation of database sharding

You can implement database sharding through several factors, as discussed below.

• Key-based or Hashed: It is a mathematical approach to sharding where a shard key is assigned to every row using a hash function, which produces a hash value. The tables are populated using hash value as a shard key.
• Range-based: In this, the data is split based on a range of values, and this range is then assigned a shard key.
• Directory-based: To divide the database using the directory, a lookup table is created using a shard key to track which shard holds which data.

Why should you consider database sharding?

Reduce response time: Sharding allows requests to run parallelly across different shards, reducing retrieval time significantly. Since shard databases have fewer rows, users can run multiple queries with better response time.

Easy scalability: In database sharding, you create numerous smaller shards that consume fewer computing resources than a large database. This makes it easy to scale databases without storage issues.

Avoid downtimes: The entire database is spread across multiple computers. One failed system will not shut down the whole application since other shards remain operational. It enables high availability of the application.

Sharding or partitioning: which one to use?

Dividing a database becomes essential as your application becomes popular to avoid experience issues. Although sharding and partitioning are helpful methods to improve data management efficiency and database scalability, they are not interchangeable. It means they both have unique use cases in managing your database. Before picking a database architecture method, you must consider data distribution and manageability questions. Sharding can be a great way of handling increasing user requests without performance issues. However, its implementation can be complex due to its distributed architecture.