This morning’s tech news title read: “Observability is the future of monitoring!”
It caught me off guard, I must admit. In all those years spent in software development and DevOps, observability always complimented monitoring, NEVER replacing it.
They’re two sides of the same coin, each with its unique value in the DevOps realm. But has the time come that a simple buzzword turns into reality, bringing controversy to the surface?
Let’s find out!
A different kind of observability: A new breed in DevOps
Over the years, working in the DevOps industry, three new trends caught my eye. If you’re not careful enough and stick to the old DevOps methods, you’ll easily get blindsided and run through by them.
- Rise of applied observability.
- Increased role of AI in observability.
- Progressive shift from predefined metrics to active debugging.
1. The rise of applied observability
I knew it was coming sooner or later, but didn’t know it was this trendy. Gartner awarded applied observability the silver medal for the top ten strategic observability 2023 trends.
Sure, applied observation is a game-changer, but it’s not a silver bullet. I mean, it’s a tool, not a one-size-fits-all solution!
Applied observability provides a wealth of data. It offers a deep dive into the system’s internal states, giving us a detailed picture of what’s happening within our applications and infrastructure. However, this strength can also be its Achilles’ heel.
Why? Because, without the right context and understanding, this leads to:
- Information overload — The sheer volume of data is overwhelming, causing you to get lost in the noise. This results in important issues being overlooked or misdiagnosed.
- Misinterpretations — Poor correlations, lack of information, or too much data without a deep understanding of the system’s architecture and behavior can lead to misinterpretations. Even high-performance Agile teams are prone to this and draw incorrect conclusions from the data, leading to misguided decisions and actions.
Don’t let the latest trend fool you. To avoid these DevOps pitfalls, applied observability must be paired with:
- Right knowledge — Without a deep understanding of your system, making sense of data turns into a nightmare!
- Context — Data is just chunks of zeros and ones if it isn’t added to the system’s behavior context.
- Data analysis — Without effectively analyzing large volumes of data, your team is sitting ducks.
The role of AI in observability
ChatGPT and its upcoming Code Interpreter have changed the DevOps world forever, whether you like it or not. But instead of screaming “AI will take our jobs”, here’s another fresh look at it — How can we, both you and I, be more efficient in noticing how, where, what, and why our apps’ operation malfunctions?
It’s a fact: Our DevOps systems grew more complex and the volume of data generated escalated, turning manual analysis into an uphill task. But with AI, you can drop the manual approach when analyzing enormous data sets and leverage an automated and lightning-fast alternative.
Based on experience, AI brings the following benefits to observability:
- Automated data analysis.
- Real-time insights.
- Predictive capabilities.
- Learning from past incidents.
- Reduced MTTR (Mean Time to Resolution) .
Progressive shift to active debugging
Relying on predefined metrics is DevOps’ status quo. And it has been like that for ages. But the old ways no longer work!
Predefined metrics tell us what happened, but active debugging allows us to understand why it happened. It’s about exploring our system in real time, identifying and resolving issues we didn’t even know existed. Thanks to that approach, your DevOps are more reliable and achieve higher performance.
Think of it as having a super-advanced metal detector for your application. Not only does it guide you to an unknown bug location, but it also tells you how it happened as well as what caused it, how to fix it, and how to make your application even better.
The indispensable role of monitoring in DevOps
Like many others, when I first started in DevOps I focused on setting up as many metrics and alerts as possible. The more, the better, I thought. But over time, I realized that this approach often led to alert fatigue and overlooked issues. It was like finding a needle in a haystack.
I learned that effective monitoring isn’t about quantity, but quality.
Quality DevOps monitoring revolves around three key principles:
- Measure what matters — Identify the key performance indicators (KPIs) that truly reflect the health and performance of your systems. These KPIs should align with your application, infrastructure, and business needs.
- Alert with intent — Set meaningful thresholds and alerts based on a deep understanding of your system behavior and business impact. I’ve seen many lead developers screaming “wolf, wolf” when there’s no wolf around!
- Interpret with context — Monitoring data should be interpreted in the context of your system and operational environment, not the other way around.
The anatomy of monitoring in DevOps
DevOps monitoring is like the nervous system of your application or system. It sounds like an odd analogy but bear with me: monitoring is about collecting metrics, logs, and traces, just like how our nervous system works.
- Think of metrics as the main pulse, telling you the quantitative measures of your system’s health.
- The logs are the journal. They record events for future troubleshooting.
- Traces are like highways, showing data transactions where to flow through the system.
The dark side of monitoring is a recent trend that keeps happening. Overemphasis on data collection at the expense of interpretation. Don’t let that sway you away from DevOps monitoring ways.
“Understanding data is more meaningful than collecting it” — the secret many pro-DevOps developers keep for themselves.
The interplay between monitoring and observability: A fresh perspective
The buzzword stays strong: “Observability will replace monitoring”!
If that rings true, it would be like saying watching a movie replaces reading the script.
Monitoring is the script — it outlines the expected sequence of events and known paths. It tells developers when a line is missed or when a scene doesn’t play out as planned.
Observability, on the other hand, is like watching a movie. It provides context, understanding why a line was missed, or a scene didn’t unfold as planned. It allows us to ask challenging questions and explore beyond the initial script.
And that is the real beauty of observability and monitoring in DevOps! Two sides of the same coin, always complementing each other!
DevOps monitoring and observability: Lessons from the trenches
From my experience, the most effective approach to monitoring and observability involves a balance of tools and human insight. AI can augment your capabilities, but it doesn’t replace the need for experienced professionals who understand the system and interpret the data.
While data collection is a critical part of monitoring, it’s equally critical to focus on data interpretation. It’s about understanding the story the data tells about our systems and using that understanding to maintain system health and performance.
Only then will you become the true master of DevOps observability and monitoring!