In database management, sharding is one of the topics that developers can’t get a good grasp of unless you have implemented it yourself. Sharding is a database technique that splits large databases into smaller, more easily managed parts called shards. The data shards are faster as compared to the very large database. A shard is a small part of a whole. In practice, sharding means breaking a database into numerous much smaller databases that don’t share anything in common and can be spread across many servers.
Sharding is used mostly to refer to database partitioning that makes a large database faster and more manageable. Sharding is based on an idea that when the size of a database increases, the number of transactions per unit of time made on the same database increase linearly while the response time used to query the database increase exponentially.
Why should large database developers consider sharding?
The cost of creating a large database and maintaining it on one place increases exponentially. Reason being the database requires high end computers to work efficiently. When sharding is implemented, the data shards are distributed across a number of less expensive servers. These data shards also have little restrictions in hardware and software requirements. Data shards are cheap and easier to manage making the database more efficient almost in all ways.
In some scenarios, data sharding can be a series of complex processes. For example, sharding a database that has less structured data can be very complex. The resulting data shards become difficult to maintain decreasing the efficiency of such a database.
Benefits of sharding
Several reasons make sharding important, some which have been mentioned earlier. Let’s take a better look:
- Increases search performance – In a sharded database, rows of the database are held separately meaning it has smaller index sizes which results in faster and more improved search capabilities.
- Database reliability – The fundamental goal of sharding is reliability. Data on multiple servers results to high availability instead of depending on one server which can be prone to server downtime.
- Speed – Sharding enables a person to enjoy the computing power of more than one server at a time. This results to improved performance and overall speed enhancement.
- Harmonization – Managing data scattered and stored across different serves is more efficient as compared to managing one very large database.
Sharding might not make a lot of sense in every case but if a data model fits a sharding model, it provides a lot of gains.