Kubernetes is considered to be a good choice as a platform for deploying the Microservices. You can develop, build, test, deploy, and scale the components of a microservices-based application independently. Kubernetes has containers that enable the portability of the application components and easily get integrated into a larger application.
The applications you build on microservices can be scaled in various ways. You can scale them to support development with the assistance of larger development teams and can be scaled to perform better. There are several advanced ways to tackle performance issues. In this blog, we will cover some easy techniques to scale the microservices by leveraging Kubernetes:
- Vertically scaling the entire cluster
- Horizontally scaling the entire cluster
- Horizontally scaling individual microservices
- Elastically scaling the entire cluster
Let’s check each of these techniques one by one.
Vertically scaling the cluster
As your application grows, a point might come where the cluster doesn’t have enough compute, memory or storage to run your application. As you go on adding new microservices or replicating the existing microservices for redundancy. Just max out the nodes in your cluster. Then, monitor this through your Kubernetes dashboard. Now, increase the total amount of resources that are available for the cluster. Either use vertical or horizontal scaling when scaling microservices on a Kubernetes cluster.
As we grow our application, we might come to a point where our cluster generally doesn’t have enough compute, memory or storage to run our application. As we add new microservices (or replicate existing microservices for redundancy), we will eventually max out the nodes in our cluster. (We can monitor this through our cloud vendor or the Kubernetes dashboard.) Scale up the cluster by increasing the size of virtual machines (VMs) in the node pool.
Horizontally scaling the cluster
Apart from scaling the structure vertically, you can also scale it horizontally. Don’t change the size of the VMs, it can be of the same size, just that you can add more VMs. As you add more VMs to the cluster, you can easily spread the load of your application across different computers. Horizontal scaling is less expensive as compared to vertical scaling.
Horizontally scaling an individual microservice
As an individual microservices become overloaded which can be monitored in the Kubernetes dashboard. So, when a microservice becomes a performance bottleneck, horizontally scale it to distribute the load properly over multiple instances. You can also scale individual microservices for performance and horizontally scale the microservices for redundancy. You can also create a more fault-tolerant application. With multiple instances, there are always others available to pick up the load in case any single instance fails. This further allows the failed microservice instance to restart and start working again.
Elastic scaling for the cluster
Now, talking about elastic scaling it is a technique where you can automatically and dynamically scale your cluster. This will help your cluster to meet different levels of demand. During the low demand, Kubernetes automatically deallocates the resources that aren’t required anymore. And, when the demand rises, new resources are allocated to meet the high workload demands. This saves up the cost because you only end up paying for the needful resources required to handle the application’s workload.
You can also use the elastic scaling at the cluster level to grow the clusters automatically that are close to their resource limits. In conclusion, elastic scaling works by default, but there are several ways in which you can customize it based on your requirements.
When you use microservices it gives you granular control over your application’s performance. You can easily measure your microservices’ performance to look for the ones that aren’t performing properly, are either overworked, or are overloaded due to high demand. In such cases using the Kubernetes dashboard to figure out the CPU and memory storage for your microservices is highly recommended.