What is horizontal scaling in database engineering?

Horizontal scaling is often used in conjunction with other technologies, such as cloud computing and load balancing, to create highly scalable and reliable database systems. Horizontal scaling is not a silver bullet, and it is important to carefully consider the specific needs of your database before implementing it. For example, horizontal scaling can be more complex to manage than vertical scaling, and it may not be suitable for all types of databases. Overall, horizontal scaling is a powerful technique for scaling databases to handle high workloads and volumes of data. However, it is important to carefully consider the benefits and challenges before implementing it.

Horizontal scaling in database engineering is a strategy that involves increasing the performance and capacity of a system by adding more hardware or nodes to the existing infrastructure. Unlike vertical scaling, which focuses on enhancing the power of an individual machine, horizontal scaling distributes the workload across multiple machines. This approach promotes flexibility, improved performance, and enhanced reliability, making it a popular choice for accommodating growing data volumes and user demands.

Horizontal scaling is beneficially when you want to balance load or create fault tolerance system so that even though one node fails other nodes will still work on client request.Implementing this strategy requires good amount of testing and application configuration changes so that you can reap in full benefits of database horizontal scaling

Each strategy should be taken into consideration while designing your databases.Each database provide different type of horizontal scaling usecase so should plan it accordingly.Every strategy which is mentioned like sharding,replication,partioning will have individual sub category and it should be taken into consideration while designing your database solution.

Additionally, once you have sharded your database, re-sharding or splitting into more shards will involve a lot of data movement unless you use the right sharding mechanism. This is a critical consideration if your application starts growing faster than what was initially anticipated. It is always better to keep a good head-room in every shard and monitor the growth of data in every shard.

There are many techniques. Let me name some few important ones. 1. Sharding: Distributes data across multiple servers by splitting it into smaller, manageable "shards". 2. Replication: Creates multiple copies of the database on different servers, enhancing availability and load distribution. 3. Load Balancing: Optimizes resource use by distributing incoming database requests evenly over multiple servers. 4. Caching: Uses distributed memory storage to reduce direct database queries by storing frequently accessed data. 5. Database Decoupling: Splits a monolithic database into specialized databases to cater to specific workloads. 6. Consistent Hashing: Ensures minimal data movement in distributed systems when servers are added or removed.