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What This Is All About

In hyperscale environments, where even the smallest blip can ripple across thousands of services and millions of users, postmortem culture is an operational necessity. Yet, outside of these big tech companies, many teams still view postmortems as a mere checkbox task. They write one, file it away, and move on.

However, real postmortem discipline is more than just writing about what happened. It’s about:

• Understanding the full context

• Identifying the actual root cause

• Codifying lessons learned

• Preventing the issue from ever happening again

Most importantly, it changes how we think, diagnose, and design. When done right, a postmortem becomes a cultural catalyst for improvement and resilience.

In this article, we'll take a close look at redacted cases to uncover real-world insights on tackling network availability risks on a large scale and achieving operational excellence through successful postmortems. Let's dive deep!

Real-World Case Study: The Invisible Packet Drops

Let me take you through a real (but redacted) incident that tested my postmortem discipline.

The Symptom: One Availability Zone (AZ) started experiencing service latency and timeouts. From the application side, it looked like congestion or CPU pressure. Our dashboards lit up with warnings, and pressure mounted to mitigate the issue.

Initial Diagnosis: Classic case, right? Services overloaded. So we:

• Migrated workloads

• Added server capacity

• Scaled out certain pods

The symptoms... moved. But they didn’t disappear. The issue wasn’t gone; it was just displaced.

Root Cause Discovery: We eventually discovered the truth:

• A firmware bug in a small subset of programmable switches

• Bug triggered only under specific ECMP load thresholds

• Result: Return-path packet drops at the ASIC level

It had nothing to do with CPU or congestion. We were chasing shadows while the real monster lurked deeper.

Lessons From the Trenches

The whole idea behind postmortems is to understand what happened and, most importantly, or maybe even the only thing that matters, is to leverage the failure to learn and improve your systems. You can't miss the opportunity to evolve after experiencing a service impact and a postmortem follow-up: you must learn from what happened and evolve by implementing the necessary fixes or strategies.

The following hard-learned lessons emerged from this redacted case:

1. Symptoms Aren’t Root Causes

If an issue seems to follow your mitigations without resolution, it’s a sign you’re patching symptoms, not causes.

Example: Moving workloads helped because traffic patterns shifted, not because we addressed the root.

Allow me to explain what this meant for me and what it should mean for you:

Too often, especially in high-pressure environments, we fall into the trap of action bias, doing something feels better than sitting still. We re-route traffic, restart services, or drain nodes. And sometimes it seems to work, at least temporarily.

But transient recovery isn't resolution. It's often symptom displacement, not system healing.

Let’s say you observed reduced latency after shifting workloads between AZs. It’s tempting to call it a fix. But unless you validated why the original traffic path was congested, whether due to queuing on a specific uplink, ECMP hash imbalance, or a misbehaving BGP community, you’re just moving the pain around, not removing it.

In large-scale infrastructures, this can lead to what I call operational whack-a-mole, where the same incident reappears under different names, in different locations, because the core issue was never addressed. And that’s dangerous. It burns out teams, creates noisy telemetry baselines, and erodes trust in your ability to deliver sustainable reliability.

The right approach is investigative: use observability data to correlate symptoms to root causes. Was the packet loss tied to queue drops or forwarding inconsistencies? Did the performance dip start after a route flap? Was a silent configuration drift the real trigger?

Root cause work is slower, but it’s exponential in value. When you solve the cause, the symptoms stop coming back. That’s the difference between “working harder” and “engineering smarter.”

And when you build systems to surface, validate, and isolate root causes consistently, you’re not just solving problems. You're preventing the next 10.

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