Observability is a term that has gained a lot of traction in recent years, particularly in the realm of software engineering and DevOps. At its core, observability refers to the ability to gain insight into the internal workings of a system by observing its external outputs. This allows engineers to diagnose and troubleshoot issues with the system, as well as to monitor its performance and behaviour.

However, despite its importance, there are a number of myths and misconceptions about observability that can lead to misunderstandings and misunderstandings about what it is and how it works. In this article, we will take a closer look at some of these myths and dispel them, as well as discuss the three pillars of observability that are essential for effective monitoring and troubleshooting.

Myth #1: Observability is the same thing as monitoring

One of the most common misconceptions about observability is that it is the same thing as monitoring. While monitoring certainly plays a role in observability, it is not the whole picture. Monitoring refers to the process of collecting data about a system, such as metrics and logs, and using this data to track the system’s performance and behaviour. Observability, on the other hand, goes beyond just collecting data and involves using this data to gain insight into the internal workings of the system.

Myth #2: Observability is only relevant for large, complex systems

Another myth about observability is that it is only relevant to large, complex systems. In reality, observability is important for any system, no matter how simple or small it may be. Even a simple web application with a handful of microservices can benefit from observability, as it can help engineers to diagnose and fix issues with the system quickly.

Myth #3: Observability is only for production systems.

Some people think that observability is only relevant for systems that are running in production and that it is not necessary for development or testing environments. However, observability is just as important in these environments, as it allows developers and testers to understand how their code is behaving and to identify and fix issues before they are deployed to production.

Myth #4: Observability is only about metrics and logs

While metrics and logs are undoubtedly important for observability, they are not the only aspects of a system that need to be monitored. In order to gain a complete understanding of a system, engineers also need to be able to observe the system’s behaviour, as well as its internal state. This requires a combination of different data types, including metrics, logs, and traces.