What it is: Observability is the degree to which the functioning of an application is visible. In essence, an observable system is one that can be monitored and measured with a high degree of precision, with real-time, relevant, well-organized data. While the term is generic, it is commonly applied to cloud-native and container-based applications, which can be massively distributed and therefore hard to monitor. Systems can be observable by design or made observable with software tools.
What it does: Observability helps operations staff and system administrators identify the symptoms and causes of issues. An unobservable application is a black box; when it breaks down, it might be apparent what broke it, but not why, making it harder to fix quickly, and harder still to prevent in the future. The resulting diagnostic information can be fed back to developers and architects for resolution.
Why it matters: Applications are becoming more complex. Revolutions in application design like serverless computing and microservices mean that software infrastructure is more segmented and distributed. Without actively cultivating observability and using tools that can deal with complex application architectures, operations staff can find themselves increasingly mired in complex and hard-to-diagnose failure modes.
What to do about it: Observability challenges are an inevitable consequence of adopting more forward-looking application architectures and cloud-based models. Prepare for this by prioritizing observability during application design, for example by embracing principles of cohesion within containers, or reducing dependencies between them. If it’s an ongoing challenge in your current applications, consider adopting commercial observability tools.
- Makes it easier to assess performance
- Allows for preventative maintenance
- Speeds problem diagnosis and reduces time to resolution
- Aids communications between operations and development teams
A common mistake with the adoption of more agile practices and distributed architectures is that they require less control or governance so they can result in more sprawling, less manageable systems. Observability goes some way to address this, but a better starting point is to recognize the potential complexity from the outset and to build systems and services accordingly. In other words, you can’t just run amok with interesting cloud tools. Requiring systems to be transparent slows upfront development, and also requires a change in developer culture but pays off in the longer term.
A slew of tools on the market help address observability issues. Generally, they range from more stripped-back monitoring tools to AI-based tools offering automatically collected and centralized data across complex applications. Uber’s open-source version of their in-house application tracing solution, Jaeger, is well-regarded. LightStep is another popular choice, with clients including Lyft and Medium.