Microservices Kubernetes

Microservices in Kubernetes: A Practical Guide

What Are the Benefits of Kubernetes for Microservices?

Microservices is a common approach used to design software systems, where an application is composed of multiple small, loosely coupled, and independently deployable services. Each microservice focuses on a specific business functionality and communicates with other services via APIs or messaging systems. This approach promotes modularity, scalability, and flexibility, making it easier to develop, maintain, and scale complex applications.

Kubernetes is a container orchestration platform that provides several benefits for managing microservices-based applications, including:

  • Scalability: Kubernetes makes it easy to scale applications horizontally by managing the deployment of additional replicas of microservices as needed, based on traffic and resource utilization.
  • High availability: Kubernetes ensures high availability of services by distributing replicas across multiple nodes, and automatically restarting failed containers, minimizing downtime.
  • Load balancing: Kubernetes handles load balancing among replicas of a microservice, distributing traffic evenly and improving overall performance.
  • Rolling updates and rollbacks: Kubernetes enables zero-downtime deployments by gradually updating application versions, and can automatically roll back to a previous version in case of issues.
  • Self-healing: Kubernetes monitors the health of containers and automatically replaces unhealthy instances, ensuring the stability and reliability of the application.
  • Service discovery and routing: Kubernetes provides built-in service discovery and routing mechanisms, making it easy for microservices to communicate with each other.
  • Portability: Kubernetes runs on various cloud providers and on-premises environments, making it easier to migrate or deploy applications across different infrastructures.
  • Extensibility: Kubernetes supports custom resources and operators, allowing you to extend its functionality and tailor it to your specific needs.

This is part of a series of articles about Kubernetes security.

In this article:

How to Deploy Microservices on Kubernetes

Containerizing Microservices

The first step in deploying microservices on Kubernetes is to containerize each microservice. This involves packaging them as container images, which requires creating a Dockerfile for every microservice. The Dockerfile specifies the runtime environment, dependencies, and any necessary configurations. By containerizing microservices, you ensure that they are isolated, portable, and can be easily managed by the Kubernetes platform.

Creating Kubernetes Resources for Microservices Deployment

To deploy your microservices on Kubernetes, you need to define several Kubernetes resources, such as Deployments, Services, and ConfigMaps. These resources will describe how the microservices should be deployed, exposed, and configured within the cluster.

  • Defining Deployments: The Deployment resource outlines the number of microservice instances to run and the process for updating them over time. It specifies the container image, necessary environment variables, and any required volumes.
  • Defining Services: The Service resource is responsible for exposing a microservice to other services within the cluster or externally. You’ll need to specify the microservice’s port, protocol, and the Service type (ClusterIP, NodePort, LoadBalancer, or ExternalName).
  • Defining ConfigMaps: The ConfigMap resource stores configuration data that can be accessed by one or more microservices. Create a ConfigMap for each microservice or group of microservices that requires access to shared configuration data.

Deploying and Managing Microservices on Kubernetes

Once the Kubernetes resources are defined, deploy the microservices using the kubectl apply command. Kubernetes will create the necessary Pods, Deployments, Services, and ConfigMaps.

  • Configuring networking: If the microservices need to communicate with each other, you’ll have to configure networking. This may involve creating a service mesh using tools like Istio or Linkerd.
  • Monitoring and scaling: Monitor the performance and health of your microservices and scale them as needed. Kubernetes provides tools such as Metrics Server and Horizontal Pod Autoscaler to help with this task. By closely monitoring your microservices, you can ensure optimal performance, identify potential issues, and automatically scale resources based on demand.

Example of a Kubernetes Microservices Workflow: Azure Kubernetes Service

Azure Kubernetes Service (AKS) is a managed Kubernetes service offered by Microsoft Azure, which simplifies the deployment, scaling, and management of Kubernetes clusters. Here is the general process for deploying a microservices application on Azure:

  1. Create an AKS cluster: Set up an AKS cluster by following the Azure documentation. It will include the required infrastructure such as virtual machines, networking, and storage.
  2. Containerize microservices: Develop and containerize your microservices using Docker. Each microservice should be in a separate container with its dependencies, making it easier to deploy and scale.
  3. Store container images: Push the Docker images to a container registry like Azure Container Registry (ACR). ACR stores and manages your container images, ensuring secure and fast access to them.
  4. Deploy microservices: Create Kubernetes deployment manifests (YAML files) for each microservice, specifying the desired number of replicas, resource limits, and other configurations. Deploy the microservices to the AKS cluster using tools like kubectl or Helm.
  5. Configure service discovery: Use Kubernetes services to expose microservices within the cluster. Each service gets a stable IP address and DNS name, making it easier for microservices to communicate with each other.
  6. Ingress and load balancing: Set up an ingress resource and an ingress controller like NGINX or Azure’s Application Gateway Ingress Controller (AGIC) to manage external traffic to your microservices. This provides load balancing and SSL/TLS termination.
  7. Autoscaling: Configure the horizontal pod autoscaler (HPA) to automatically scale the number of replicas based on CPU utilization or custom metrics.
  8. Monitoring and logging: Integrate Azure Monitor for containers to collect metrics, logs, and events from the AKS cluster, providing insights into the performance and health of your microservices.
  9. CI/CD pipeline: Set up a CI/CD pipeline using tools like Azure DevOps or GitHub Actions to automate the building, testing, and deployment of your microservices.
  10. Secure communication: Implement network policies to control traffic between microservices, and use Azure Key Vault to manage secrets and certificates for secure communication.

This example outlines the basic components of a microservices architecture on AKS. Depending on your specific requirements, you may need to customize or extend this architecture with additional components, such as persistent storage or advanced monitoring solutions.

Learn more in our detailed guide to AKS security

Kubernetes Security and Observability with Calico

Tigera’s commercial solutions provide Kubernetes security and observability for multi-cluster, multi-cloud, and hybrid-cloud deployments. Both Calico Enterprise and Calico Cloud provide the following features for security and observability:


  • Security policy preview, staging, and recommendation – Easily make self-service security policy changes to a cluster without the risk of overriding an existing policy. Calico can auto-generate a recommended policy based on ingress and egress traffic between existing services, and can deploy your policies in a “staged” mode before the policy rule is enforced.
  • Compliance reporting and alerts – Continuously monitor and enforce compliance controls, easily create custom reports for audit.
  • Intrusion detection & prevention (IDS/IPS) – Detect and mitigate Advanced Persistent Threats (APTs) using machine learning and a rule-based engine that enables active monitoring.
  • Microsegmentation across Host/VMs/Containers – Deploy a scalable, unified microsegmentation model for hosts, VMs, containers, pods, and services that works across all your environments.
  • Data-in-transit encryption – Protect sensitive data and meet compliance requirements with high-performance encryption for data-in-transit.


  • Dynamic Service Graph Get a detailed runtime visualization of your Kubernetes environment to easily understand microservice behavior and interaction.
  • Application-Layer Observability Gain visibility into service-to-service communication within your Kubernetes environment, without the operational complexity and performance overhead of service mesh.
  • Dynamic Packet Capture Generate pcap files on nodes associated with pods targeted for packet capture, to debug microservices and application interaction.
  • DNS Dashboard Quickly confirm or eliminate DNS as the root cause for microservice and application connectivity issues in Kubernetes.
  • Flow visualizer – Get a 360-degree view of a namespace or workload, including analytics around how security policies are being evaluated in real time and a volumetric representation of flows.

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