Distributed caching is an important aspect of cloud-based applications, be it for on-premises, public or hybrid cloud environments. It facilitates incremental scaling, allowing the cache to grow and incorporate the data growth. In this post, we will explore distributed caching on cloud and why it is useful for environments with high data volume and load. This post will cover:
Traditional caching servers are usually deployed with limited storage and CPU speed. Often these caching infrastructures reside on data centers that are on premises. I am referring to a non-distributed caching server. Traditional distributed caching comes with numerous challenges like:
Caching is a technique to store the state of data outside of the main storage and store it in high-speed memory to improve performance. In a microservices environment, all apps are deployed with their multiple instances across various servers/containers on the hybrid cloud. A single caching source is needed in a multicluster Kubernetes environment on cloud to persist data centrally and replicate it on its own caching cluster. It will serve as a single point of storage to cache data in a distributed environment.
Redis: It’s one of the most popular distributed caching services, and it supports different data structures. It’s an open source, in-memory data store used by millions of developers as a database, cache, streaming engine and message broker. It also has an enterprise version. It can be deployed in containers on private, public and hybrid clouds, etc., and it provides consistent and faster data synchronization between different data centers.