At the heart of every production service lies a database. However, an architecture that relies on a single database instance becomes a critical Single Point of Failure, where an unexpected hardware failure, network issue, or maintenance task can bring down the entire service. To address these risks and maximize service reliability, ensuring database high availability (HA) is not an option—it’s a necessity.
PostgreSQL offers a powerful and reliable Streaming Replication feature to meet these demands. This feature allows you to replicate data from a primary server to one or more standby servers in near real-time. This minimizes service interruptions by enabling a quick switch to a standby server if the primary fails. Simultaneously, it allows for Read Scaling by distributing read queries to standby servers, improving overall database performance. This post provides an in-depth guide on how to build a production-level PostgreSQL streaming replication setup that can be applied directly in a real-world environment.
As Microservices Architecture (MSA) has become commonplace, Kubernetes has established itself as the standard for container orchestration. In a Kubernetes environment where numerous containers are dynamically created and destroyed, tracking distributed application logs and troubleshooting issues is nearly impossible with traditional methods. Accessing each pod and checking logs with the kubectl logs command is merely a temporary fix, with clear limitations for real-time incident response and root cause analysis.
To solve these problems, building a Centralized Logging System has become not an option, but a necessity. A centralized logging system collects all logs generated across the entire cluster into a single location, refining and storing them so that developers and operators can easily search and visualize them. This post provides an in-depth guide to building a production-level centralized logging system for Kubernetes using the EFK (Elasticsearch, Fluentd, Kibana) stack, centered around Fluentd, a powerful log collector and a graduated project of the CNCF (Cloud Native Computing Foundation).
When operating a Kubernetes cluster using Amazon EKS (Elastic Kubernetes Service), one of the most critical challenges is routing external traffic to services inside the cluster reliably and efficiently. While Kubernetes provides NodePort or LoadBalancer type services, they have clear limitations in meeting the complex demands of a production environment. For example, every time a LoadBalancer service is deployed, a new ELB (Elastic Load Balancer) is created, increasing costs. It’s also difficult to apply fine-grained L7 routing rules (path-based, host-based routing).
To solve these problems, Kubernetes offers an object called Ingress. An Ingress is a collection of rules that allow inbound connections to reach cluster services, and the component that actually fulfills these rules is the Ingress Controller. In the AWS environment, the AWS Load Balancer Controller integrates most seamlessly with EKS, allowing you to natively leverage AWS’s Application Load Balancer (ALB) or Network Load Balancer (NLB). Using this controller, you can expose multiple services through a single ALB and declare powerful features like SSL/TLS certificate management, advanced traffic routing, and WAF integration in a Kubernetes-native way.
This post, aimed at experienced engineers, will cover the entire process of building a stable and cost-effective ingress system by integrating Amazon EKS with the AWS Load Balancer Controller in a production environment, from A to Z. We’ll go beyond simple controller installation to delve into IAM role setup, advanced configurations using essential annotations, and detailed solutions and best practices for problems you might encounter in the field.
The core of successful web service operation goes beyond simply implementing features; it lies in continuously observing the service’s state while it’s ‘alive’ and predicting potential issues. Identifying potential bottlenecks before users experience outages and analyzing resource usage trends to scale infrastructure efficiently are essential skills for any experienced engineer. However, managing the state of numerous servers and applications fragmentally in a distributed microservices architecture is nearly impossible.
To solve these problems, the combination of Prometheus and Grafana has become the de facto standard in modern DevOps environments. Prometheus collects metrics from systems and applications based on a powerful time-series database (TSDB), and Grafana visualizes this collected data into beautiful and intuitive dashboards. This combination allows us to gain powerful ‘observability,’ enabling us to grasp the state of a distributed system at a glance from a central point, detect signs of anomalies early, and respond swiftly. This post will provide an in-depth guide on the entire process of building a production-ready Prometheus and Grafana monitoring stack using Docker, and instrumenting and visualizing key business metrics of an application.
Any experienced developer has likely used Nginx as a web server or a simple reverse proxy. However, it’s hard to say you’re fully leveraging Nginx’s potential with just a single proxy_pass directive. In a production environment where traffic is growing and service stability is crucial, you need to use Nginx more sophisticatedly to maximize performance, availability, and deployment efficiency.
This article goes beyond simple port forwarding to delve into advanced Nginx reverse proxy techniques for solving problems you might face in a real production environment. We will explore in detail, with practical configurations and code, everything from high-performance caching strategies that dramatically improve response times for repetitive requests, to load balancing and health checks that prevent a single server failure from bringing down the entire service, and even building a zero-downtime deployment (Blue-Green) architecture that completes deployments without users noticing.