Observability in Microservices Architecture

Observability Is the capacity of a system's internal states to be predicted by its exterior outputs. It gives a high-level picture of the system's health with detailed insights into implicit failure modes. It is a series of procedures for gathering, examining, and quantifying various diagnostic data, including logs, metrics, traces, events, and more. You can learn more about performance bottlenecks, request failure reasons, etc., with an observable microservices system. For effectiveness, your system must be observable for debugging and diagnostics, especially with the recent increase in microservice usage or adoption.

Three Pillars of Observability in Microservices

For observability, your application must provide telemetry and requires knowledge of the three foundational elements: metrics, traces, and logs. The golden triangle of observability has three pillars, which can help you unleash the potential for creating superior apps. Despite serving fundamentally, different objectives, metrics, logs, and traces all work together to give you information about the functionality and behavior of your applications. When apps are observable, you can build them more effectively, and the best part is that you may start with any available data.

Observability Challenges in Microservices 

Organizations hope to enjoy numerous benefits from adopting microservices architectures, from improving component scalability to increasing developer efficiency. Despite the benefits and rapid adoption by large and small businesses, microservices come with hurdles and complexities. Companies like Twitter and Netflix have embraced microservices because they have discovered effective ways to manage complexity. The only good reason to employ microservices, according to Vijay Gill, Senior Vice President of Technical at Databricks, is to be able to grow your engineering group.


There are quite a few challenges regarding observability in Microservices.

When we see that some queries to our system are failing or taking too long, we can use observability tools to figure out what went wrong.

Observability Vs. Monitoring

Monitoring is the process of tracking performance and identifying problems and anomalies. It also describes the internal state, health, performance, and other significant features. Observability gives the required information to aid in monitoring. Consider observability as an insight you should have to know what needs monitoring. Once these insights are available, monitoring decides what is required once a system is observable.

When diving into a particularly virulent defect in a complex system, depending on the type of data in conjunction with logs can usually lead to a dead end. And at this point, observability comes into play. It resumes where monitoring ends. Monitoring and observability have recently been muddled, but there is a crucial distinction. Monitoring often focuses on the overall health of the system and business indicators. Observability is about offering more detailed insights into system behavior and greater context.

Here, the known knowns are facts; the known unknowns are hypotheses; the unknown knowns are assumptions, and the unknown unknowns are discoveries. The distinction between observability and monitoring becomes evident when viewed from this perspective. Monitoring involves the testing of hypotheses, whereas observability involves the exploration of discoveries. We track known unknowns because we know what to look for, but unknown unknowns are unpredictable; we cannot track them since we don't even know where to look for them! Instead, we ask our systems questions to comprehend and categorize these unknown unknowns.

Observability Is the capacity to interrogate systems with high fidelity in a data-rich manner after the event. On the other hand, monitoring is performed before the event and has a significantly low fidelity. Observability enables us to ask our systems arbitrary questions rather than predefined inquiries.

Monitoring is a subset of observability, which includes a wide range of data kinds. Distributed traces, application logs, system logs, audit logs, and application metrics, for example, are all critical observability signals. But when we boil it all down, everything is just experiences we want to see through different glasses. Some of this data, like logs and metrics, gives context for the event itself, while others, like traces, show links between events. We must have the means to collect and store all this context, to query and analyze it using these various lenses.


Observability is critical for modern businesses because it gives the necessary insights to ensure services are available to meet business demands. Many firms choose a do-it-yourself approach to observability, which requires a manual method. You can also create the complete application stack using the do-it-yourself (DIY) method. However, the disadvantage of this strategy is that it requires a significant amount of time; thus, not recommended. will undoubtedly assist you in achieving high observability for your microservices project. Contact us today to see how we can help you grow your business exponentially.

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