Observability is a term referenced in control theory by Hungarian-American engineer Rudolf E. Kálmán for linear dynamic systems introduced in 1960.
Observability generally involves understanding a complex system's internal state or condition based only on its external outputs. This is called an observable system. For a system to be observable, an external actor needs to observe its internal behavior without changing it. In the case of a software system, this means new code or a configuration change introduced in the software development process doesn't need to be shipped to answer further questions about the system performance.
The more observable a system is in real-time, the more quickly and accurately you can navigate from an identified performance problem, such as latency degradation, to its root cause without additional testing or coding. Advanced observability also improves application availability through end-to-end distributed tracing across serverless platforms, Kubernetes environments, microservices, and open-source solutions.
As a part of modern technology environments, observability represents a process reflected in software tools that detects issues by observing the inputs and outputs of the technology stack. Inputs include essential application and infrastructure stacks, while outputs include business transactions, user experiences, and application performance.
Observability platforms typically gather performance telemetry by integrating with existing instrumentation built into infrastructure and application infrastructure elements. Observability focuses on four main types of telemetry:
- Metrics. Metrics (sometimes called time-series metrics) are measures of application and system health over a given period of time, like how much memory or CPU capacity an application uses over five minutes or how much latency an application experiences during a spike in usage.
- Events. Events are a critical telemetry type for any observability solution. They’re valuable because they can be used to validate the occurrence of a particular action at a particular time and enable a fine-grained analysis in real time. Events contain a higher level of abstraction than the level of detail provided by logs. Logs record everything, whereas events are records of selected important things.
- Logs. Logs are time-stamped and complete records of various application events. Logs are used to create a granular millisecond-by-millisecond view of every event in a saved record, which developers can review for troubleshooting purposes.
- Traces. Traces record the transaction's lifecycle as it traverses the distributed systems. Tracing can be used for debugging and monitoring complex applications that contend for resources.
For cloud computing specifically, observability refers to software tools and practices that optimize aggregate, correlate, and analyze a stream of telemetry data from a distributed systems application and the hardware it runs on—to more effectively monitor, enable troubleshooting, and debug the application to meet customer experience expectations, provider service level agreements (SLAs), and other business requirements that software engineers in AIOps and DevOps teams are instrumenting.
In the networking world, observability is increasingly utilized by legacy NPM vendors in an attempt to “rebrand” network monitoring as an observability solution that promises more than traditional network monitoring. The reality is that observability has essentially evolved out of application performance management (APM) discipline, which relied on outputs from different areas to ascertain the overall state, so data collection and aggregation methods address the increasingly rapid, distributed, and dynamic nature of cloud-native application deployments.
Observability doesn't replace monitoring tools but can leverage network visibility to provide insight into the application infrastructure/architecture. It provides an additional perspective that can help AIOps teams perform detailed application debugging while considering potential network impacts. Observability can complement the network visibility provided by network monitoring so that application and network states can be correlated.
So observability utilizes APM to provide the current known state of the application and supporting infrastructure through the aggregation of application and system metadata, called telemetry, often related to application performance issues. It analyzes telemetry data relative to key performance indicators (KPIs). It also assembles the results for alerting via dashboards alert teams to abnormal conditions that should be addressed to resolve or prevent issues.
Observability tools can collect and analyze a broad range of data, including application health and performance, business metrics like conversion rates, and user experience mapping that impact business KPIs.