Most developers prefer to stick to the microservices architecture when developing agile and scalable applications. It helps them to break monolithic applications to small, independent services. As a result, they can experience better scalability and flexibility. Introducing new changes and managing deployments become less complex for the developers as well. Kubernetes offers much-needed help for developers who stick to microservices architecture.
Kubernetes is a powerful container orchestration platform. It simplifies the process of managing deployments and scaling new features. On top of that, Kubernetes offers resilience in microservices through auto-scaling, self-healing, and zero-downtime deployments. Let’s learn more about the different strategies available when deploying microservices on Kubernetes.
Why Use Kubernetes for Microservices?
Kubernetes is the go-to (go, you see what I did there?) platform for running microservices in production. That’s because it offers some useful features to handle complexities in distributed systems. In other words, it can orchestrate, scale, and manage containerized applications effectively. This helps the developers to focus more on building business logic, instead of dealing with underlying infrastructure. Some of the key features offered by Kubernetes include:
- Auto-scaling
- Self-healing
- Service discovery and load balancing
- Zero-downtime
Let’s explore what they are in detail.
Auto-scaling Microservices on Kubernetes
Auto-scaling plays a major role in microservices architecture. It manages fluctuating workloads without any manual intervention. Kubernetes offers two different strategies for auto-scaling:
- Horizontal Pod Autoscaler (HPA)
It adjusts the total number of running pod replicas based on metrics such as memory and CPU usage. It allows microservices to scale out when the demand for applications increases. In other words, it optimizes the utilization of resources, while reducing costs.
- Vertical Pod Autoscaler (VPA)
It adjusts the total number of requests as well as limits for containers within a pod. VPAs are beneficial when the resources of microservices need change over time. It helps Kubernetes to adjust CPU and memory allocations without the need to manually re-deploy the service.
Self-Healing Microservices with Kubernetes
Microservices can encounter numerous issues when running applications. Slow response times and crashes are to name a few. However, the self-healing capabilities of Kubernetes can help the services to recover automatically. There is no need to have any manual intervention at all. This can eventually enhance the overall reliability of applications.
Liveness and Readiness Probes
Kubernetes uses liveness and readiness probes to determine the health of a microservice. These help Kubernetes to detect issues early and take corrective action:
- Liveness Probes
These checks determine if the container is running as expected. When there is a failure in a liveness probe, Kubernetes will go ahead and perform a restart action on the container. It will ensure that the microservice that experiences issues gets replaced promptly.
- Readiness Probes
These checks determine if a microservice is capable of handling traffic. When there is a failure in the readiness probe, Kubernetes will temporarily remove the pod from the load balancer. This can ensure that the traffic gets routed only to the healthy pods. It will prevent partial and unresponsive services from impacting user experience.
Achieving Zero-downtime Deployments with Kubernetes
One of the most prominent benefits that Kubernetes offers is zero-downtime deployments. It ensures that new updates or fixes can go live, without disrupting service availability. The most common strategy to do this is through rolling updates.
Rolling Updates
Kubernetes can perform rolling updates by gradually replacing the old version of a microservice pod. During this process, Kubernetes will make sure that a specific number of pods are available at all times to handle traffic. This can effectively minimize disruption.
Readiness probes play a major role here. They help Kubernetes to prevent traffic from being routed to new pods until they are available for deployment. This can ensure a smooth transition between versions. On top of that, you can overcome downtime during the deployment process.
Enhancing Resilience with Service Mesh
A service mesh introduces an additional layer of infrastructure. It helps to manage communication between microservices. Kubernetes will continue to provide basic service discovery such as load balancing. In the meantime, a service mesh can enhance these capabilities. It brings advanced traffic management, security, and observability.
Istio and Linkerd are some of the most popular service mesh solutions that you can find there. Here are some of the prominent features that you can find in them:
- Traffic routing
Service mesh allows intelligent traffic routing. In other words, you can ensure optimum traffic routing based on policies. It will enable canary deployments as well as blue-green deployments.
- Fault tolerance
Circuit breaking and retries will enhance overall fault tolerance. This can improve the resilience of an application in case of service failures.
- Observability
Service mesh can enhance observability through detailed metrics and tracing. It will also enable logging, which facilitates better monitoring and troubleshooting.
As you can see, it is beneficial to integrate a service mesh with Kubernetes. It will help the software development teams to secure a better level of resilience. It can also ensure that microservices can withstand failures. In the long run, it will help the application to remain responsive under varying conditions.
Monitoring and Observability in Kubernetes
To ensure the long-term resilience of microservices, effective monitoring and observability are essential. Kubernetes integrates well with monitoring tools to help with it. A few of the well-known monitoring tools include Prometheus, Grafana, and ELK Stack. They provide real-time insights into the health and performance of microservices.
It is also important to track metrics such as CPU usage, memory consumption, pod health, and latency. Then it is possible to identify issues before they escalate. Additionally, distributed tracing tools like Jaeger and Zipkin can provide detailed insights. Such detailed insights can benefit inter-service communication. It can eventually enable faster debugging and performance optimization, ensuring that microservices remain performant and resilient under various loads.
Final Words
As you can see, Kubernetes offers much-needed support for building resilient microservices. It provides a platform that can handle scaling, healing, and availability. On top of that, Kubernetes offers many robust features such as zero-downtime deployments and rolling updates. By combining these capabilities with a service mesh, any organization can create highly resilient and scalable microservice architectures.