Often misinterpreted as a synonym of monitoring, observability has been getting a lot of attention in recent years, courtesy of technological advancement. Engineers are constantly building greater, faster, and more efficient systems, with their complexity proportionate to their growth. This, in turn, dictates the need for equally advanced, complex, and efficient control methods to ensure maximum uptime and availability, and minimum predictable and preventable issues that could lead to catastrophic outages.
Primarily, observability is a concept that is highly relied upon for many DevOps, SRE, and security teams to be able to most effectively keep mission-critical business systems healthy and functional - and the requirements are pushing for higher levels of intelligence and visibility.
Observability measures how well the internal states of a system can be identified based on its external outputs. It is a concept introduced by Hungarian-American engineer Rudolf E. Kálmán as an element of control theory which deals with the control of continuously operating dynamical systems in engineered processes and machines.
Dynamical systems designed to estimate the states of other systems based on measurements of their outputs are called state observers. Systems meanwhile are considered observable if their current states can be inferred from output information.
Observability is achievable by meeting certain conditions and is mathematically calculated by constructing an observability matrix and checking its rank. While a dynamic system is rendered unobservable if it has an unobservable state, it can still be detectable.
Detectability, as a weaker notion than observability, describes a system’s condition when all of its unobservable states are stable.
Monitoring is a systematic and purposeful observation. It entails systematic gathering and analyzing data for tracking a system’s progress and potential changes in its output. After parsing the collected data in metrics, results are compared against predefined standards or thresholds to evaluate a system’s stability. When the data violates the preconfigured metrics, the monitoring system sends an alert or triggers a preconfigured action.
While monitoring and observation are semantic synonyms, their processes and purposes are entirely different and indicate that observability is a superset of monitoring. Monitoring evaluates a system’s performance based on a set of predefined measurements and alerts what is wrong and when. Observability meanwhile also answers why something is wrong as it determines the relationships between objects and actions.
New trends in observability are leading to novel approaches and improved efficiencies. One such innovator, Edge Delta, is reinventing the space with a distributed machine learning concept called federated learning, allowing DevOps, SRE, and security teams to accomplish the what, when, and why much more effectively than previously possible with traditional centralized systems.
In modern software engineering, in order for a state observer to answer the why, it needs to cover the following elements:
This is the collection of data of any proprietary, open-source, or integrated entity that emits it. Data not only needs to be collected but it also needs to be kept in a unified store, readily available for interoperable analysis, regardless of its source. Finally, the observability solution should be able to provide context for the data by automatically creating entities and connections within.
To achieve this, there are four types of data that a state observer needs to process.
Logs are chained sequences of records ordered by time, providing reliable data and context around events, enabling system administrators and engineers to recreate a scenario by measures as granular as milliseconds.
As measures of quantitative assessment at the core of monitoring, metrics are the initial type of data needed for observability. They are cost-efficient to collect and store and have sufficient substance for quick analysis and insights into a system’s overall health.
Individual calls in a distributed architecture give specific insights into the varying multitude of user journeys within a system. Traces, as a type of data, provide insight into these user journeys and enable administrators to detect bottlenecks and errors.
Events provide data on what took place for a certain service within a system to perform a unit of work. While often confused with logs, they are fundamentally different. Events are more abstract yet detailed records of select occurrences, while a log can be considered an indication of an event.
Merely collecting data from various entities into a single depot is not enough to define an observable system. Instead, data needs to be connected in a way that provides insight into the relationships between its source entities, as well as correlate with meta-data for deeper context. This helps with curated views of the most important data within a specific environment and a certain point in time. Finally, context-based curation is solid ground to upgrade a state observer with machine learning and AI to make it provide even more actionable information on even shorter notice, or trigger an automated response to certain behaviors.
As the last aspect of the ideal observability platform, programmability is the ability to build custom applications on top of it. These go beyond dashboards as they provide curated and interactive combinations of telemetry and external datasets (e.g. business KPIs) that, in turn, give real-time insights into how a system’s operation affects external processes.
Mathematically, these aspects are duals of the same problem. Controllability measures the possibility of changing the state of a system by using a control input. There are several variations of controllability such as state controllability, output controllability, controllability in the behavioral framework, or the weaker notion of stabilizability. None of these degrees of controllability can be established without system observability. In other words, if a system is not observable, it cannot be controlled or stabilized.
As DevOps, SRE, and security teams are trying to increase observability, the product and engineering counterparts are driving forward with many popular methodologies - multi-cloud, microservices architecture, containerization, Kubernetes, serverless. This is leading to an exponential growth of data that is requiring a re-think of the infrastructure and tools needed to successfully stay on pace. The organizations that are able to adapt are implementing new technologies designed to automate a lot of the analysis and alerting for these much larger data sets. The move from traditional logs, metrics, and tracing platforms to modern platforms like Edge Delta is quickly helping organizations to achieve observability without a large time or monetary investment upfront.