In this article we will go over deploying microservices into a Kubernetes cloud. Part 1.
Introduction Welcome to the first post in our series exploring the pros and cons of deploying microservices into a Kubernetes cloud.
Objective In this series, we aim to deploy a microservice architecture where all services are containerized using Docker and deployed to a managed Kubernetes cloud.
Key Features:
- Auto-Scaling: Kubernetes’ auto-scaling features allow us to manage varying loads effectively by automatically adjusting the number of pods based on CPU/memory utilization or custom metrics.
- CI/CD Integration: A robust CI/CD process will be integrated into our deployment pipeline, * automating the build, test, and deployment processes for faster and more reliable releases.
- Monitoring and Logging: We will leverage Prometheus, Grafana, and Loki for monitoring and logging our services, enabling swift detection and diagnosis of issues.
- Configuration Management: ConfigMaps and Secrets will be used to manage configuration data separately from application code.
Further reading
Once you’re comfortable with the high-level deployment goals, continue with the step-by-step guide in Kubernetes Cluster Setup on AWS EKS to provision a production-ready cluster and core addons.
FAQ
- What does this Kubernetes deployments series aim to teach?
- The series introduces a microservice architecture deployed to a managed Kubernetes cluster, covering autoscaling, CI/CD pipelines, monitoring, logging, and configuration management so you can move from simple Dockerized services to a production-ready environment.
- How does this high-level post relate to the AWS EKS setup guide?
- This article explains the goals and concepts, while the "Kubernetes Cluster Setup on AWS EKS" post walks through creating a concrete cluster with eksctl, ingress, cert-manager, messaging, and PostgreSQL so you can actually run those microservices.
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