Lightweight Kubernetes at the Edge with K3s#

K3s is a production-grade Kubernetes distribution packaged as a single binary under 100 MB. It was built for environments where resources are constrained and operational simplicity matters: edge locations, IoT gateways, retail stores, factory floors, branch offices, and CI/CD pipelines where you need a real cluster but cannot justify the overhead of a full Kubernetes deployment.

K3s achieves its small footprint by replacing etcd with SQLite (by default), embedding containerd directly, removing in-tree cloud provider and storage plugins, and packaging everything into a single binary. Despite these changes, K3s is a fully conformant Kubernetes distribution – it passes the CNCF conformance tests and runs standard Kubernetes workloads without modification.

Debugging Workflow#

Start broad, narrow down. Most problems fall into five categories: service not running, resource exhaustion, full disk, network failure, or kernel issue. Work through them in order: service, resources, network, kernel logs.

Services: systemctl and journalctl#

When a service is misbehaving, start with its status:

systemctl status nginx

This shows whether the service is active, its PID, its last few log lines, and how long it has been running. If the service keeps restarting, the uptime will be suspiciously short.

Why Load Test#

Performance problems discovered in production are expensive. A service that handles 100 requests per second in dev might collapse at 500 in production because connection pools exhaust, garbage collection pauses compound, or a downstream service starts throttling. Load testing reveals these limits before users do.

Load testing answers specific questions: What is the maximum throughput before errors start? At what concurrency does latency degrade beyond acceptable limits? Can the system sustain expected traffic for hours without resource leaks? Will a traffic spike cause cascading failures?

The Fidelity-Speed Tradeoff#

Every local development environment sits on a spectrum between two extremes. On one end: running everything locally with no containers, maximum speed, minimum fidelity to production. On the other end: a full Kubernetes cluster with service mesh, maximum fidelity, minimum speed. Every tool in this space makes a different bet on where the sweet spot is.

The right choice depends on your answers to three questions. How many services does your application depend on? How different is your production environment from a single machine? How long can developers tolerate waiting for changes to take effect?

The Decision Landscape#

Log management is deceptively simple on the surface – applications write text, you store it, you search it later. In practice, every decision in the log pipeline involves tradeoffs between cost, query speed, retention depth, operational complexity, and correlation with other observability signals. This guide provides a framework for making those decisions based on your actual requirements rather than defaults or trends.

Structured Logging: The Foundation#

Before choosing any aggregation tool, standardize on structured logging. Unstructured logs are human-readable but machine-hostile. Structured logs are both.

How Kubernetes Captures Logs#

Containers write to stdout and stderr. The container runtime (containerd, CRI-O) captures these streams and writes them to files on the node. The kubelet manages these files at /var/log/pods/<namespace>_<pod-name>_<pod-uid>/<container-name>/ with symlinks from /var/log/containers/.

The format depends on the runtime. Containerd writes logs in a format with timestamp, stream tag, and the log line:

2026-02-22T10:15:32.123456789Z stdout F {"level":"info","msg":"request handled","status":200}
2026-02-22T10:15:32.456789012Z stderr F error: connection refused to database

kubectl logs reads these files. It only works while the pod exists – once a pod is deleted, its log files are eventually cleaned up. This is why centralized log collection is essential.

MCP Server Development#

This reference covers building MCP servers from scratch – the server lifecycle, defining tools with proper JSON Schema, exposing resources, choosing transports, handling errors, and testing the result. If you want to understand when to use MCP versus alternatives, see the companion article on MCP Server Patterns. This article focuses on how to build one.

Server Lifecycle#

An MCP server goes through four phases: initialization, capability negotiation, operation, and shutdown.

Message Queue Selection and Patterns#

Every microservice architecture eventually needs asynchronous communication. Synchronous HTTP calls between services create tight coupling, cascading failures, and latency chains. Message queues decouple producers from consumers, absorb traffic spikes, and enable event-driven workflows. The hard part is picking the right one.

Core Concepts That Apply Everywhere#

Before comparing specific systems, understand the delivery guarantees they can offer:

• At-most-once: The message might be lost, but it is never delivered twice. Fast, no overhead, acceptable for metrics or logs where occasional loss is tolerable.
• At-least-once: The message is guaranteed to arrive, but might arrive more than once. The consumer must handle duplicates (idempotency). This is the most common choice.
• Exactly-once: The message arrives exactly once. This is extremely hard to achieve in distributed systems. Kafka offers it within its ecosystem via transactional producers and consumers, but end-to-end exactly-once across system boundaries requires idempotent consumers anyway.

Ordering matters too. Some systems guarantee order within a partition or queue. Others provide no ordering at all. If your consumers process messages out of order, you need to handle that in application logic or choose a system that preserves order.

Minikube to Cloud Migration Guide#

Minikube is excellent for learning and local development. But almost everything that “just works” on minikube requires explicit configuration on a cloud cluster. Here are the 10 things that change.

1. Ingress Controller Becomes a Cloud Load Balancer#

On minikube: You enable the NGINX ingress addon with minikube addons enable ingress. Traffic reaches your services through minikube tunnel or minikube service.

On cloud: The ingress controller must be deployed explicitly, and it provisions a real cloud load balancer. On AWS, the AWS Load Balancer Controller creates ALBs or NLBs from Ingress resources. On GKE, the built-in GCE ingress controller creates Google Cloud Load Balancers. You pay per load balancer.

MongoDB Operational Patterns#

MongoDB operations center on three areas: keeping the cluster healthy (replica sets and sharding), protecting data (backups), and keeping queries fast (indexes and explain plans). This reference covers the practical commands and patterns for each.

Replica Set Setup#

A replica set is the minimum production deployment – three data-bearing members that elect a primary and maintain identical copies of the data.

Launching Members#

Each member runs mongod with the same --replSet name: