Q is for quality, R is for resolution, S is for signal, and finally, T is for topology. Welcome to the fifth installment in a series of posts alphabetically listing and defining important Observability terms. If you have just jumped in, then please consider starting from the beginning to get a better understanding of my selection.
The quality of data and information are at the core of Observability in practice. With regard to data, the quality dimensions most organizations focus on include relevance, accuracy, precision, timeliness, coherence, consistency, comparability, completeness, comprehension, periodicity, and representativeness. Information quality builds on the data quality dimensions with significance in context (in situ), generalizations, integration, resolution (scale), structure, temporal relevance, operational, and communication. Data quality reflects more the intrinsics of measurement and collection. In contrast, information is the derived value pertaining to a goal, be it situational awareness and understanding, causality and control, or descriptive and representation. Quality is intellectual.
Alternatives: Queue, Query, Qualia, QoS
All too often, when the Observability community talks of high resolution, it pertains chiefly to data as opposed to information. Unfortunately, a finer observability data measurement scale is often associated with more noise. Within mature organizations and teams, thinking about resolution shifts from raw data payloads and events to the ability to scale up and down the resolution of a perspective, representation, aggregation, as well as space and time dimensions. Moving forward as an engineering discipline, the emphasis must center around how best to automatically and adaptively scale the resolution of information communicated to the task at hand and reflective of the diverse operational and environmental contexts. Much of today’s measurement is relatively coarse grain in nature and middle of the road. In the future, there will be two paths taken by tooling – one offering high fidelity playback of software execution, the other utilizing signaling theory and social cognition for high-level contextual reasoning about systems, services, and segments in the service supply chain. The middle ground of tracing, metrics, and logging will be the land occasionally visited by users in transitioning between service management operations and deep diagnostics modes of awareness, acknowledgment, acquisition, and action.
Alternatives: Remedy, Reactive, Request, Response, Resilience, Reliability, Reservation, Robustness, Recording
Purposive communication rests on signals. Signals are the observable features of a participant (service or agent) in communication that is displayed to increase the chance that a receiver will assign a particular state of affairs to the producer, environment, or situation. A signal coveys meaning with immediacy without loss or translation costs, whereas an event or message requires introspection of properties and structures and then interpretation. With regard to Observability, a signal is a classification of some phenomenon concerning the operation or outcome of a particular execution or call. Comprehension of signals is the source of information. Information is formed from the sequencing of signals and the state inference that is implied or learned over time. Signals synthesize information and elicit action. In humans as well as animals, signals evolved to alter the behavior of others through the shared, but not necessarily perfect, understanding of the meaning of such – systems of services must do likewise now.
Alternatives: State, Sensor, System, Significance, Simulation, Scalability, Stability, Scale, Scope, Site, Stack, Span, Synthetics, Sink
Topological maps, concerned with shape (properties), connection (relationships), and relative position (boundaries), are of fundamental importance to the comprehension of distributed systems and modern architectures such as microservices. Topology is the study of places. In Observability, a place (entity or enclosure) is some spatial and temporal context of some permanence. However, what constitutes permanence is a moving target these days with accelerating change. When it comes to network topology, and I refer to network in the broadest sense possible, what is connected to what is more important than how far things are apart. Topology is less concerned with distance, and in most cases, so too must engineering. It is the degree of connection in networks, networks that are everywhere within today’s software systems, at the physical network layer up into the call graph of a service workflow spanning multiple services nodes of execution. Topologies reduce complex systems to an underlying architecture of nodes and links – a simplification that offers easier interpretability of high-level structural change.
Alternatives: Trace, Testing, Topic, Transaction, Transience