Database Sharding: Real-Life Applications of Database Sharding

• Database sharding is the best way to distribute a single dataset into multiple.
• It holds many benefits, with some complexities as well.
• The Real-Life Application of Database Sharding in Couchbase Hash-Based Sharding.

Database sharding is a method of distributing a single dataset into multiples. There are many advantages to sharding, from high availability to storage capacity. However, it also holds some complexities, such as infrastructure cost, administration complexity, etc. 

What Is Database Sharding?

It is a method of distributing a single dataset across multiple databases that can be stored on multiple machines. It helps to split larger datasets into smaller chunks. These are stored in multiple data nodes that increase the total storage capacity of the system. A sharded database can handle more requests than a single machine can. It is a form of scaling known as horizontal scaling or scale-out as additional nodes are brought on to share the load. This allows for near-limitless scalability to handle big data and intense workloads. 

The advantages of sharding include handling increased load to a nearly unlimited degree by providing increased write throughput/read, high availability, and storage capacity. Both read and write operation capacity is increased by distributing the dataset across multiple shards. The overall storage capacity is also increased by increasing the number of shards. It provides high availability in two ways; every piece of data is replicated and the database as a whole still remains partially functional. There is an alternative term used for sharding; partitioning, however, refers to the grouping of data in a single database instance only. 

The disadvantages of sharding include overhead in query result compilation, increased infrastructure costs, and complexity of administration. Each sharded database should have a separate machine to understand how to route a querying operation to the appropriate shard. The database server itself requires upkeep and maintenance with a single unsharded database. Sharding requires computer power and additional machines on a single database server. 

There are different sharding methods; algorithm/harshed sharding, geography-based sharding, entity/relationship-based sharding, and ranged/dynamic sharding. There are certain factors required for sharded solutions; data model and API offered by the system, developer skills and learnability, online documentation, and customer support, right tools and frameworks, cloud-based solutions, etc. There are two types of sharding; horizontal and vertical. Horizontal is effective when queries tend to return a subset of rows, whereas vertical is effective when queries tend to return only a subset of columns. 

Real-Life Applications of Database Sharding

There is no right or wrong design. Every sharding implementation comes with its own pros, cons, and complexity. A real-life example of database sharding is Couchbase hash sharding. Couchbase is a document database that stores data in binary or JSON format. Users can define a bucket that logically organizes a set of documents. 

Conclusion

Sharding is a great solution for applications with larger data requirements and high-volume read/write workloads. But it does come with additional complexity. It is very important to consider whether the benefits outweigh the costs or whether there is a simpler solution before beginning the implementation of database sharding.