Devcontainer Sandbox Templates#

Devcontainers provide disposable, reproducible development environments that run in a container. You define the tools, extensions, and configuration in a .devcontainer/ directory, and any compatible host – GitHub Codespaces, Gitpod, VS Code with Docker, or the devcontainer CLI – builds and launches the environment from that definition.

For infrastructure validation, devcontainers solve a specific problem: giving every developer and every CI run the exact same set of tools at the exact same versions, without requiring them to install anything on their local machine. A Kubernetes devcontainer includes kind, kubectl, helm, and kustomize at pinned versions. A Terraform devcontainer includes terraform, tflint, checkov, and cloud CLIs. The environment is ready to use the moment it starts.

Docker Compose Validation Stacks#

Docker Compose validates multi-service architectures without Kubernetes overhead. It answers the question: do these services actually work together? Containers start, connect, and communicate – or they fail, giving you fast feedback before you push to a cluster.

This article provides complete Compose stacks for four common validation scenarios. Each includes the full docker-compose.yml, health check scripts, and teardown procedures. The pattern for using them is always the same: clone the template, customize for your services, bring it up, validate, capture results, bring it down.

Rate Limiting Implementation Patterns#

Rate limiting controls how many requests a client can make within a time period. It protects services from overload, ensures fair usage across clients, prevents abuse, and provides a mechanism for graceful degradation under load. Every production API needs rate limiting at some layer.

Algorithm Comparison#

Fixed Window#

The simplest algorithm. Divide time into fixed windows (e.g., 1-minute intervals) and count requests per window. When the count exceeds the limit, reject requests until the next window starts.

Running Redis on Kubernetes#

Redis on Kubernetes ranges from dead simple (single pod for caching) to operationally complex (Redis Cluster with persistence). The right choice depends on whether you need data durability, high availability, or just a fast throwaway cache.

Single-Instance Redis with Persistence#

For development or small workloads, a single Redis Deployment with a PVC is enough:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: redis
spec:
  replicas: 1
  selector:
    matchLabels:
      app: redis
  template:
    metadata:
      labels:
        app: redis
    spec:
      containers:
      - name: redis
        image: redis:7-alpine
        command: ["redis-server", "--appendonly", "yes", "--maxmemory", "256mb", "--maxmemory-policy", "allkeys-lru"]
        ports:
        - containerPort: 6379
        volumeMounts:
        - name: redis-data
          mountPath: /data
        resources:
          requests:
            cpu: 100m
            memory: 300Mi
          limits:
            cpu: 500m
            memory: 350Mi
      volumes:
      - name: redis-data
        persistentVolumeClaim:
          claimName: redis-data
---
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: redis-data
spec:
  accessModes: ["ReadWriteOnce"]
  resources:
    requests:
      storage: 5Gi
---
apiVersion: v1
kind: Service
metadata:
  name: redis
spec:
  selector:
    app: redis
  ports:
  - port: 6379
    targetPort: 6379

Set the memory limit in Redis (--maxmemory) lower than the container memory limit. If Redis uses 350Mi and the container limit is 350Mi, the kernel OOM-kills the process during background save operations when Redis forks and temporarily doubles its memory usage. A safe ratio: set maxmemory to 60-75% of the container memory limit.

Redis Deep Dive: Data Structures, Persistence, Performance, and Operational Patterns#

Redis is an in-memory data store, but calling it a “cache” undersells what it can do. It is a data structure server that happens to be extraordinarily fast. Understanding its data structures, persistence model, and operational characteristics determines whether Redis becomes a reliable part of your architecture or a source of mysterious production incidents.

Data Structures Beyond Key-Value#

Redis supports far more than simple string key-value pairs. Each data structure has specific use cases where it outperforms alternatives.

Redis on Kubernetes: Deployment Patterns, Operators, and Production Configuration#

Running Redis on Kubernetes requires more thought than deploying a stateless application. Redis is stateful, memory-sensitive, and its clustering model makes assumptions about network identity that conflict with Kubernetes defaults. This guide covers the deployment options from simplest to most complex, the configuration details that matter in production, and the mistakes that cause outages.

Deployment Options#

There are three main approaches to deploying Redis on Kubernetes, each with different tradeoffs.