In today’s software development landscape, ensuring system reliability, performance, and scalability is crucial. With the increase in complexity of modern systems, traditional monitoring methods are no longer sufficient. Observability was introduced as a critical practice that allows engineers to gain deeper insights into their systems and diagnose issues effectively. This is the first post of a series about observability in software engineering. In this first post, we will explore what observability means, its historical evolution, its importance in modern architectures, and why observable systems outperform those without observability.

What is observability

Observability is a measure of how well the internal states of a system can be inferred from its external outputs. In software engineering, this translates to the ability to understand the health and performance of a system through the collection of data such as logs, metrics, and traces. Observability enables teams to ask open-ended questions about their system and get answers without having to write new code to capture specific behaviors.

In contrast to traditional monitoring methods, observability is not just about checking predefined metrics or responding to known issues. Observability is also about gaining comprehensive visibility into the system, allowing engineers to detect, understand, and fix unknown issues, often before they escalate into critical failures.

History of observability

Observability has its roots in control theory, dating back to the 1960s, where it was originally used in the context of measuring physical systems. Observability has gained prominence in software engineering relatively recently, particularly with the rise of distributed and cloud-native architectures.

In the early days of software development, most systems were monolithic: self-contained applications running on dedicated hardware. Monitoring these systems was simpler, involving basic checks for CPU usage, memory, disk space, and logs for error reporting. However, as architectures evolved to be more distributed, especially with the domination of microservices and cloud computing in the 2010s, these traditional monitoring approaches proved insufficient.

Observability in microservice architecture

Microservice architectures, by their very nature, break applications into many small, independent services that communicate with each other over the network. While this offers advantages such as flexibility, scalability, and faster development cycles, it also presents new challenges for understanding how the system behaves as a whole. In a microservices ecosystem, failures can propagate quickly. For example, a single misbehaving service can affect downstream services, leading to cascading failures. An example would be an order checkout system with a GUI application, an order management service, a product management service, a payment service and a billing service. An error in handling user’s transaction in payment service could ramify to other parts of the system. Without observability, it is difficult to pinpoint the root cause of an issue when services are distributed across many nodes or data centers.

Observability is vital in microservices architectures because of these reasons:

  • Inter-service dependencies: Services usually depends on each other, either directly or indirectly. Observability helps track these dependencies and trace where failures occur.
  • Dynamic scaling and elasticity: Cloud-native applications often scale dynamically. Observability ensures that engineers can monitor system behavior as services are spun up or down, detecting performance issues in near real-time.
  • Decentralized ownership: In modern development practices, different teams may own different services. Observability provides a single source of truth across all teams, helping them collaborate more effectively when diagnosing issues.

Systems with good observability implementation will outshine its counterparts without observability:

  • Faster incident response: With comprehensive visibility into the system’s behavior, engineers can detect and resolve issues faster. Logs, metrics, and traces provide a complete picture of what went wrong and why.
  • Proactive issue detection: Observability allows engineers to identify potential problems before they affect users. By analyzing trends and anomalies in metrics and traces, teams can intervene before a minor issue escalates into a major incident.
  • SLI enablement: Observable systems make Service Level Indicator (SLI) more transparent. Using latency, throughput, and error rates, SLIs can be defined clearly, creating opportunities for engineering team to make optimizations when necessary.
  • Better collaboration across teams: In large organizations, teams often work on different parts of the system. Observability provides a shared understanding of system health, making it easier for teams to collaborate and resolve issues together.
  • Business metrics: Not limited to system health and performance, observability can also provide business insights such as transaction counts, user behavior trends, etc., which are valuable feedback for business to make timely decisions.

Conclusion

Observability is a critical component of modern software systems, especially in microservice-based architectures. By collecting and analyzing logs, metrics, and traces, observability empowers engineers to understand their systems deeply and respond quickly to incidents. It also offers business and operational benefits by providing insights into system performance and user behavior.

In the next posts of this series, we’ll have a chance to dive deeper into tooling and techniques to implement observability with Grafana and OTEL. Click here to reach the next part of the series.