Learning to Read the Water (and the Logs)
I’ve been fishing since I was a kid. Long days on lakes and rivers taught me to slow down, watch for movement, and notice the subtle signals that tell you where the fish are — a swirl, a flick, a change in current.
Years later, I realized that observability in cloud systems isn’t so different. In both cases, you’re not reacting to loud alarms. You’re watching for patterns, listening for signals, and using those clues to adjust course.
As a platform and DevOps engineer, I’ve spent a good chunk of my life building observability stacks in production environments. Whether at Target, Icario, or running my own aquaculture business, observability has been the key to everything — from catching bugs before they explode to tuning system performance under heavy load.
This post is a mix of hands-on advice and philosophy. If you’re looking to upgrade your observability game — and maybe see logs the way an angler sees water — this one’s for you.
Observability vs. Monitoring: Know the Difference
Let’s start with a quick reminder: monitoring tells you what’s broken, observability tells you why.
- Monitoring: “CPU spiked above 90%.”
- Observability: “That spike was caused by a bad query deployed in version 1.3.8 during the lunch hour.”
Monitoring is about setting thresholds. Observability is about correlating signals across your system to form a narrative.
If you’re still only watching dashboards for red and green lights, you’re fishing with a stick and a string. It works — but you’re missing everything happening beneath the surface.
The Three Pillars (and Why They’re Not Enough)
We all know the “three pillars” of observability: logs, metrics, and traces. Here’s the fishing version:
- Metrics = water temperature. High-level signal, good for knowing if fish might be active.
- Logs = ripples and splashes. Tells you what just happened.
- Traces = underwater camera. Shows you where the fish went and how.
But real-world observability goes beyond those. You also need:
- Events: Deployment timestamps, feature toggles, config changes.
- Context: Metadata on users, requests, or devices.
- Topology: The system map. What talks to what. What fails when X goes down.
You want a lake map, sonar, weather radar, and a guide whispering, “Try casting near that fallen log.”
Build Your Stack Like an Ecosystem
Here’s a breakdown of my preferred observability stack in Kubernetes-based environments:
- Metrics: Prometheus (collected at pod/node level), Grafana for dashboards.
- Logs: Loki (if you’re all-in on Grafana), or ElasticSearch with Fluent Bit.
- Tracing: OpenTelemetry for instrumenting services, Jaeger or Tempo for visualization.
- Alerting: Alertmanager + Slack or PagerDuty integrations.
- Dashboards: Grafana templated views by service, workload, or region.
But don’t stop at setup. Think about how everything ties together — like an ecosystem. Your observability system should flow like a watershed: one source feeds the next, leading to clarity.
Fishing for Anomalies
In both SRE and fishing, anomalies are gold.
Maybe you see a spike in memory usage every Thursday at 3 p.m. Or a drop in traffic from one region after a new deploy. The untrained eye might ignore it — but if you’ve spent time watching the system, you know that something’s off.
The trick is to set up your observability to surface these anomalies before they become incidents:
- Use anomaly detection in your metrics platform (Prometheus + PromQL or tools like Grafana Machine Learning).
- Enrich logs with contextual data — request IDs, user IDs, build versions.
- Correlate deploy events with error rates or latency changes.
- Use tracing to surface why a request suddenly takes 400ms instead of 100ms.
I once caught a bug in an ML inference pipeline by noticing a tiny increase in response times for edge requests. It turned out to be a memory leak triggered only by a specific data structure. Without trace-level data, we’d still be scratching our heads.
Don’t Just Observe — Respond
Fishing teaches you to respond gradually. You don’t jerk the line at every nibble. You watch, feel, and adjust.
Observability should work the same way. The best systems don’t just tell you what’s happening — they help you decide what to do.
- Integrate automated rollback triggers on high error rates (e.g., with Argo Rollouts).
- Tie alerts to runbooks or ChatOps tools for fast action.
- Use SLOs and error budgets to guide decision-making, not panic.
Good observability isn’t just about clarity. It’s about confident response. When an incident hits, your signals should whisper: “Here’s where the problem is. Here’s what changed. Here’s what to do next.”
Tuning for Signal, Not Noise
Overalerting is like casting a line into a school of baitfish hoping for a bass. You get hits — but they don’t matter.
- Tune your alert rules. Aim for low noise, high confidence.
- Use severity tiers — not every 500 needs to page someone.
- Add for: clauses in Prometheus rules to avoid flapping.
Observability should make your team smarter, not more anxious. Every alert should teach something. Every dashboard should answer a question.
Fish Smarter, Not Harder
Building observability isn’t just a checklist — it’s a craft. It’s how we connect with our systems and understand what they’re really telling us.
Whether I’m debugging a degraded API or watching the ripples on a still lake, the question is the same:
What is this system trying to tell me — and how fast can I act on it?
You don’t need to be an expert fisherman to catch fish. But if you learn to read the water — just like learning to read your infrastructure — you’ll catch more, waste less time, and respond with calm confidence when something bites.
And honestly? That’s what engineering is all about.