Scaling refers to increasing a company’s computing resources to manage the exponential growth of work. Simply put, it is the ability of a database to expand the output as load increases. Under heavy loads, the database can slow or even collapse, crippling business operations
Scaling the database can be extremely challenging and demands a far more comprehensive understanding of the available options and how it all works.
When Should A Company Scale Its Database?
Once your company has used up all other options at hand to approach database performance issues, it is time to scale your database. In other words, you should tackle all of the possible performance optimization issues in your current situation before you take a leap to a scalable database cluster.
If you have fast-growing applications that strain your database, then it is time to think ahead and come out with a plan for a scalable database. In other words, despite your present situation, the time to prepare a database scaling plan is now, before finding yourself under complete emergency or outage conditions.
Deciding to move to a fully scalable database is a crucial call. In terms of system and financial resources, it can be an ambitious project. But most importantly, you need time to draw up a well-thought strategy that meets your needs.
Database scalability allows databases to expand to a larger size in order to support more transactions while data increases. There are two effective ways to turn your database into an agile and robust framework: Vertical database scalability and horizontal scaling.
Vertical Database Scalability and Horizontal Scaling
Vertical scaling allows upgrading the capacity of the existing database server. It also adds extra physical resources, such as storage, memory, and CPU.
On the other hand, horizontal database scaling allows increasing the capacity of the database by distributing the load across more, smaller database servers. Companies can, therefore, reduce costs by using fewer resources and hardware components to accommodate variable workloads. Having data hosted across various databases and freeing up the resources rises I/O concurrency and decreases the load on existing nodes.
While both these kinds of scalability have unique advantages, they also have some limitations. Vertical scaling does not deliver quickly and has difficulty in handling highly variable workloads. On its part, horizontal scaling demands replication for data synch, and the applications connected to the cluster to be updated to direct traffic appropriately to the various servers.