Best Practices for Observability: Monitoring, Logging, and Tracing in Distributed Systems

Building distributed systems that are reliable and scalable isn’t just about making them run; it’s about understanding every nuance of how they operate under varying conditions. This understanding doesn’t come from hope or guesswork; it comes from observability—a deliberate approach to knowing what’s happening in your systems and why. After years of working with cloud infrastructure and Kubernetes environments, I’ve realized that true observability requires more than just plugging in a monitoring tool and calling it a day. It’s about the thoughtful integration of monitoring, logging, and tracing to provide clarity across the entire system.

Monitoring often becomes the starting point for teams stepping into observability, and for good reason. Tracking metrics like CPU usage, memory consumption, and request latencies helps maintain the baseline performance of any application. But the challenge with monitoring lies in its limitations; it’s great for showing trends and alerting you to anomalies, but it’s rarely enough to diagnose the root cause of an issue. Dashboards filled with clean, green indicators can mask deeper problems, and when those problems do surface, relying solely on monitoring often leads to dead ends. Combining monitoring with logging and tracing ensures you’re not just reacting to symptoms but digging into the actual causes.

Logs often take a backseat in system design until something goes wrong, and by then, it’s often too late to implement them properly. Logs are meant to tell a story, but they only do so when crafted with purpose and structure. I’ve worked with teams drowning in endless streams of log data, only to realize the logs were full of noise—vague error messages without context or timestamps. These kinds of logs create frustration rather than solving problems. When structured correctly, logs provide a timeline of events that highlight what your system was doing before, during, and after an issue arose. For instance, instead of a generic “error occurred,” a well-designed log entry might specify which service threw the error, what the input was, and how it deviated from expected behavior. Logging isn’t an afterthought or something to revisit when issues escalate; it’s part of your system’s foundation.

Tracing might be the least understood piece of observability, but in many ways, it’s the most revealing. Distributed tracing maps the journey of a request across your services, exposing bottlenecks and inefficiencies. Without tracing, debugging performance issues in a microservices architecture can feel like being blindfolded while searching for a needle in a haystack. I’ve seen teams spend weeks trying to isolate latency problems because they lacked visibility into where time was being spent within their systems. Tools like Jaeger and OpenTelemetry turn this into a far more manageable task by giving you granular insight into where things slow down—whether that’s in a database query, a network call, or somewhere unexpected. Tracing isn’t just about diagnosing slowness; it’s about understanding the relationships between components and identifying areas for improvement.

The real strength of observability comes when monitoring, logging, and tracing work in harmony. Separately, each provides valuable insights, but together they offer a comprehensive view of system behavior. In my experience at Dojah, a company where real-time operations are critical to business outcomes, integrating these three elements has been a defining factor in turning vague, high-level alerts into actionable insights. Monitoring gives us the high-altitude perspective, logging fills in the granular details, and tracing provides the connections between them. This synergy doesn’t just help identify and fix issues faster; it transforms how we think about system reliability and performance.

One of the biggest mistakes I’ve seen is treating observability as a problem to solve only when systems grow large and complex. By then, the gaps in visibility are harder to close, and the cost of retrofitting observability into a system can be high. When observability is baked into the architecture from the start, it becomes easier to scale systems while maintaining confidence in their behavior. A distributed system that can’t be understood is a system waiting to fail, and no amount of reactive effort will change that.

The beauty of a unified observability strategy is that it turns operational challenges into opportunities for system refinement. When you’re no longer scrambling to answer basic questions about what went wrong, you can shift your focus toward preventing issues and optimizing performance. Observability isn’t just about putting out fires; it’s about understanding the conditions that allow them to start and addressing those proactively. It’s a mindset that prioritizes clarity and action over assumptions.

In a world of growing system complexity, building and maintaining observability is no longer optional for teams that care about reliability and scalability. Monitoring provides awareness, logging adds context, and tracing connects the dots. Together, they create a feedback loop that not only identifies problems but also drives better architectural decisions. Observability done well doesn’t just support the systems you build; it enables them to evolve gracefully as the demands on them grow. For anyone serious about distributed systems, observability isn’t a checkbox to tick—it’s a cornerstone of success.