Large digital enterprises often struggle to understand how their systems work, how healthy they are, and how they change over time. This is especially hard when they use modern designs. Traditional ways of observing systems usually only collect data from one area, focusing on numbers. But this does not give them the whole picture. They miss important things about how different parts of the system interact and how people interpret the data. So, organizations have a hard time understanding how their systems are doing and how they are changing.
We need a unified theory of observability to address the limitations of current practices while still being practical and straightforward to implement. It should go beyond different areas of the system, like service monitoring, user experience, and system changes. It should combine quantitative measurements with qualitative understanding while connecting with context. This unified approach should consider the different perspectives that come from complex system interactions. It should give us a holistic view, so everyone can understand, agree on, and work together. Our theory is based on a universal pattern that we see in all system observations. We have taken inspiration from semiotics and systems theory, and we have adapted these principles to software systems.
Every system observation follows a universal pattern that goes beyond specific domains or implementations. Every meaningful observation can be broken down into a few basic elements. When we observe systems, there is always a source (the one telling us something) and a subject (the thing being told about). This duality is super important – no observation exists in a vacuum. A service monitoring another service, a user experiencing a system, or a component tracking internal state changes all follow this pattern. The relationship between the source and the subject gives us the context we need to understand what is happening. To communicate effectively, we need a language, or signs, which are the building blocks of meaning. They are like labels we put on observations.
Systems use vocabularies to express observations, which can be specific to a particular domain but follow common patterns. Signs provide the language, while signals represent actual observations. Signals combine signs with context. This distinction lets us move from abstract classifications to concrete observations while still understanding what is happening. Every observation has an orientation: the source’s point of view and the subject’s experience. This dual perspective is super important for how systems interact. For example, in a service call, the caller’s perspective is all about starting and finishing the call, while the recipient’s perspective is all about handling and processing the request. These perspectives give us different insights into how systems work.
This basic idea of sources, subjects, signs, and signals is the foundation for deeper system understanding. By recognizing and working with this pattern, we can create structured ways of talking about system behaviors, keep track of context, understand how things interact, and build a shared understanding.
This approach works for simple observations and complex behaviors, and it always stays the same. For example, a microservices architecture could track service calls, see when things fail, and figure out how signals are connected to user impact, all in one place. This pattern shows up in different areas, making it easier to do more advanced observability. Looking at how it shows up reveals that this pattern is universal and practical.
In service management, the pattern reveals itself through the interactions between services in a distributed system. Here, our vocabulary of signs might include basic service phenomena like START, CALL, FAIL, and SUCCEED. These signs combine to tell stories about service health and interaction patterns.
Consider a simple service interaction within a cloud environment: Service A, a frontend application, initiates a call to Service B, a backend API service. This common interaction offers valuable insights into the behaviors of both services and the dependencies between them. In this scenario, Service A, as the source, emits a CALL signal directed at Service B, identifying it as the subject. Upon receiving this signal, Service B, now acting as the source, responds by emitting a CALLED signal about Service A, which becomes its subject. The interaction ultimately concludes with either a SUCCESS or FAIL signal, capturing the outcome of the exchange.
This sequence demonstrates the framework in action: sources telling us about subjects through signs, manifesting as signals with dual perspectives. The power becomes clear when we observe patterns over time – a service repeatedly failing calls might indicate deeper systemic issues that simple metric collections would miss.
When we apply our pattern to understanding change management, we get a fresh take on things. With this new perspective, we can use a set of signs to describe different types and qualities of change. This makes it easier to understand and analyze change. For example, temporal signs can show us the different stages of change. An INITIATE signal is like the start of a change, getting things moving. The PROPAGATE sign shows how change spreads through a system. A STABILIZE signal is when the change is settled and the system finds a new balance. Finally, a REVERT signal is when the change is undone, and the system goes back to how it was before. This set helps us understand change better, so we can predict, manage, and adapt to changes in complex systems.
This approach captures both deliberate changes like deployments and unforeseen changes like performance degradation. For intentional changes, a deployment system communicates about a service using deployment-related signs. Conversely, a monitoring system provides insights into the system’s behavior through performance-related signs. This consistency in modeling facilitates seamless interpretation of changes in diverse scenarios, offering a comprehensive understanding of system dynamics. The dual perspective is crucial – the initiator of a change often has a different view of its impact than the recipients. This difference helps us understand how changes propagate and impact different parts of our system.
User experience can also be understood with our approach. In this case, the sources could be users or user sessions, and the subjects could be system features or specific interactions. The signs tell us different things about how users are engaging with our stuff. For example, an ENGAGE sign means a user is actively interacting with a feature or task. A HESITATE sign means a user is unsure about something, which could be a problem. An ABANDON sign means a user stopped working on a task, which could be a problem. And a COMPLETE sign means a user finished a task, which is good! This way of looking at things helps us understand how users behave, leading to more user-friendliness.
While each domain manifestation provides valuable insights on its own, the true power of our general theory emerges when we observe how these domains interact and influence each other. This cross-domain understanding reveals patterns and relationships that would remain hidden when viewing each domain in isolation. Consider the interaction between service behavior and change management domains.
For example, imagine a deployment change within a large-scale e-commerce system. When a new feature is rolled out—a change management event—it triggers interactions among dozens of microservices in the service behavior domain. Observing these interactions can uncover patterns, such as certain services experiencing slower response times or errors propagating along service dependencies. This interplay not only sheds light on the immediate impact of the change but also reveals its long-term effects on overall system resilience.
A deployment can often initiate a cascade of behaviors within the service domain, exposing critical patterns and questions. For instance, how do different types of changes affect service resilience? In a payment processing system, even a minor database schema update might result in slower transaction times across dependent services. Such observations highlight both immediate disruptions and cascading effects on user-facing systems.
Additionally, these interactions can help identify which services are most sensitive to environmental changes, how changes propagate through service dependencies, and when shifts in one domain serve as predictors of problems in another. These insights are essential for improving system design and resilience.
For example, imagine a subtle configuration change that appears minor in the change management domain. By observing its manifestation in the service behavior domain, we might notice a gradual degradation in service performance that would be challenging to attribute to the change when viewing either domain alone. The different perspectives from both domains combine to tell a more complete story about system behavior.
The approach enables the emergence of collective intelligence. When multiple domains observe and record information about the same system phenomena, they collectively generate a detailed and interconnected view. For example, signals from the service domain might provide technical insights, such as “Service A is experiencing increased latency.” Meanwhile, signals from the change domain could offer critical context: “A configuration change was deployed to the region.” Finally, signals from the user experience domain might reveal the broader impact: “Users are experiencing longer load times.” Together, these observations weave a comprehensive narrative, enabling better diagnosis, response, and system improvement. These multiple perspectives combine to create a more nuanced and complete understanding of system behavior. The key is that the framework’s model remains the same across domains, allowing us to correlate and combine observations meaningfully.
By correlating observations across domains, we can uncover patterns that enable a predictive understanding of system dynamics. These patterns provide early warnings and deeper insights into potential issues, facilitating proactive responses. For example, specific patterns in service interactions might signal impending performance problems, allowing teams to address them before they escalate. Similarly, user behavior trends could reveal emerging issues, such as increased abandonment rates, before they reach critical levels.
Sequencing might also indicate which services are becoming more or less stable over time, offering valuable foresight into system resilience. By identifying these cross-domain patterns, organizations can move from reactive troubleshooting to predictive and preventive strategies. This predictive capability emerges naturally from the cross-domain application of our framework. By maintaining a consistent structure in how we record and interpret observations, we can begin to see relationships and patterns that span multiple domains and time scales.
A key advantage of our framework lies in its potential to reduce fragmentation in system visualization and interaction. Today we face a highly fragmented tooling landscape, with separate visualization tools for service metrics, independent dashboards for change management, and distinct interfaces for user experience analytics.
This disjointed approach introduces several challenges. Engineers are forced to context-switch between various tools, each requiring a different mental model. Every tool comes with its own interaction patterns and learning curve, further complicating usability. Correlating information across these tools often becomes a manual and error-prone process, adding inefficiency and increasing the risk of mistakes. Moreover, the cognitive load of maintaining proficiency across multiple systems places a significant burden on teams, hampering productivity.
Our framework naturally leads to a more cohesive visualization approach. Engineers could interact seamlessly across these layers, tracing an issue from a change event to its service impact and further to user experience, all within the same tool. Because all domains share the same model, we can create visualization interfaces that maintain consistency while accommodating domain-specific needs. Rather than relying on separate tools, we can adopt a layered visualization approach that integrates and unifies insights across domains.
At the base layer, common patterns provide a consistent foundation for understanding system behavior. This includes standardized ways to visualize source-subject relationships, signal flows, and sequences, along with unified interaction patterns for temporal navigation. A shared visual grammar ties these elements together, enabling a clear representation of relationships and correlations. Building on this, domain-specific layers add tailored insights while maintaining the common visual language. Service monitoring visualizations can reveal interactions and health metrics across services. Change management views can illustrate how changes propagate and their impact on the system. User experience visualizations can depict user journeys and interactions, offering a window into end-user impact. Finally, cross-domain integration enables a holistic view by overlaying insights.
Seamless transitions between perspectives and levels of detail further enhance usability. Our approach reduces cognitive overhead for teams managing complex systems. Engineers no longer need to navigate disparate tools or learn multiple interaction paradigms, empowering them to work more effectively and efficiently.
Our theory of system observability unlocks exciting possibilities. It enables advanced pattern recognition across domains, allowing teams to identify emerging issues before they escalate. Predictive analytics and automated classification streamline observability processes. Learning normal behavior patterns and detecting anomalies foster a deeper understanding of system dynamics, leading to proactive optimization and maintenance.
On the organizational front, unified tooling reduces training requirements and enhances collaboration among technical teams. Integrated insights improve incident response and ensure institutional memory is preserved.
Real-time cross-domain correlation provides a richer understanding of system behavior, enabling more sophisticated root cause analysis and predictive maintenance strategies. Proactive optimization ensures systems are resilient and continually improving. Aligning technological advancements with organizational goals drives better outcomes across teams. Unified insights and tooling lead to improved efficiency, collaboration, and knowledge retention, supporting the continuous evolution of systems and teams.
This framework provides a strong base for future improvements in how we understand our systems. By building better tools and methods based on these ideas, we can expect some exciting results. We will see more integrated ways of looking at systems, connecting different areas that are currently separate. This will help us predict and prevent problems more effectively, dealing with issues before they become major incidents. Incident response will be faster and more successful thanks to a better understanding of how systems work. And, importantly, we will be able to design better, more resilient systems because we will have a broader perspective.
Navigating from raw data to a deep understanding of systems is not easy. But by identifying common patterns across all types of system observations, we can create more effective, unified, and insightful ways to grasp the complex systems we build and maintain. This theory provides a solid foundation for understanding and managing increasingly complex systems. As our systems continue to evolve and grow in complexity, the need for such unified, principled approaches to observability will only become more pressing.