The one thing you need to know about successfully scaling MongoDB? The entire stack matters. We could write several books on optimizing your clusters at scale, but we’ll start with a couple of blogs first. In this blog, we’ll cover issues with scaling your network and scaling your app.
In a previous blog post, we provided an introduction to massive scaling with MongoDB. Scaling tends to be reactive in nature leading to issues such as degraded application performance or, even worse, total application downtime. These issues ultimately lead to a negative customer experiences that impact your business.
Today’s users, customers, clients, and buyers are notoriously impatient. They demand new experiences that engage and inspire; and they wanted them delivered at light speed. Because of these demands and expectations, applications and data stores have evolved in order to provide these types of “I want it now” experiences.
There are lots of definitions for scaling. Scaling could be defined as removing the scales from a fish. However, with databases, scaling refers to having the ability to expand to meet additional needs around storage/disk, RAM/memory, CPUs/compute cycles, networking, or other resources.
It’s no secret that NoSQL databases have gained tremendous ground over relational databases in the last decade. A primary driver for this is the huge amount of data generated today from sources we never would have dreamed of two decades ago.