Asking an IT engineer or SRE to define the purpose of observability is kind of like asking someone to explain the purpose of life: There are lots of different opinions out there, and no way of proving any of them right or wrong.
You could argue that observability is just a buzzword that refers to what used to be called monitoring. Conversely, you could take the position – as an increasing number of engineers do today – that observability is distinctly different from monitoring in that it leverages techniques like intelligent remediation to provide meaningful insights into performance problems and allow teams to respond to them as quickly as possible. In contrast, monitoring just helps you find issues.
Is one of these points of view more correct than the other? Or does the truth about the meaning and purpose of observability lie somewhere in between? Again, there’s no way to say definitively. But for the sake of gaining some perspective on what observability actually entails and why it’s important today, here’s a look at what makes observability unique within the realm of site reliability and IT operations.
What Observability Means
Most formal definitions of observability define the term as a means of using external outputs from a system in order to understand its internal state. That’s a definition borrowed from control theory, where observability emerged as a concept decades ago.
But most SREs and IT engineers aren’t control theorists. When they talk about observability, they’re thinking primarily of using multiple data sources – in most cases, logs, metrics, and traces, the so-called “three pillars of observability” – to understand what is happening within complex, distributed environments like those deployed using a microservices architecture.
In IT, Observability Means More Than Just Observing
But the definition of observability in the context of IT doesn’t end there. Whether it is explicitly stated or not, a key secondary component of observability is the ability to make actionable use of the insights that observability systems generate.
In other words, for IT and site reliability teams, observability is not typically limited merely to collecting external output data in order to understand a system’s internal state. It’s equally about putting that data to use through techniques like intelligent remediation.
This actionability-oriented part of the meaning of observability is typically absent from conventional scientific perspectives on the term. Thus, you could argue that, in the context of the IT industry, observability has expanded beyond its original meaning within the realm of control theory. For IT organizations, observability means both observing and doing.
Monitoring and Observability
So far, we’ve discussed how IT teams typically think about observability. But we still haven’t fully explained what the purpose of observability is, or how it varies from monitoring.
Again, this is a somewhat contentious topic, and you’ll find different discussions out on the Internet. Some define monitoring as a prerequisite for observability: “Monitoring enables you to detect errors in the system, while observability takes it a step ahead in helping you to understand why the problem occurred,” according to DZone. Other definitions focus on the idea that monitoring allows you to gain insight only into what you already know about a system, while observability lets you “ask new questions in order to debug a problem or gain insight.”
For my money, neither of these competing definitions (or, for that matter, any other attempts to explain the differences between monitoring and observability) is more right or wrong than the other. Both statements quoted above are true.
Instead of debating the nuances that distinguish observability from monitoring – or dismissing observability as a mere buzzword that, like “DevOps” or “digital transformation,” means everything and nothing – I tend to think that a healthier perspective is to treat observability as a metonym for modernization within the IT organization. Regardless of how IT operations teams actually update their tools or workflows when they expand from monitoring to observing their environments, what matters most is the change in mindset that takes place when observability becomes the ultimate goal.
Observability primes engineers to focus on deriving actual insights as opposed to merely finding problems. It emphasizes the importance of leveraging AI in order to perform complex root-cause analysis rather than only addressing surface-level issues. It opens the door to AI-driven intelligent remediation, which allows teams to resolve issues instantaneously rather than waiting on manual response.
In this sense, you could argue that observability is kind of like the cloud, to cite another oft-used buzzword. The cloud can mean a variety of different things and types of technologies – SaaS, IaaS, PaaS, hybrid cloud, multi-cloud, poly-cloud, and on and on. Arguably, the reason the cloud has become so important is not because of any specific type of technology that organizations implement when they use the cloud. There is no specific technology associated with the cloud, because the cloud comes in so many different forms. Rather, what makes the cloud valuable is that – no matter how, exactly, you approach the cloud – it paves the way toward innovation.
Observability is similar. However you choose to think about or implement it, the ultimate result of observability is to help teams modernize the way they approach software performance management.
Conclusion: The True Purpose of Observability
To summarize, observability can mean whatever you want it to mean, and it can involve whichever specific tools or methodologies you want it to involve.
But that doesn’t mean that observability has no common purpose. No matter how you approach observability, embracing it as a concept advances your team toward innovation and best practices. Fostering an innovation-oriented mindset is the real purpose of observability, no matter how exactly it is achieved.