What is observability?

Observability’s primary goal is reliability. An effective IT infrastructure that functions properly and reliably according to customer needs requires a measurement of its performance. Observability tools inform user behaviour, system availability, capacity and network speed to ensure that everything is performing optimally.

Organisations that are subject to compliance must have observability of their computing environments. Full visibility from observability through event logs allows organisations to detect potential intruders, security threats, attempts at brute force or possible DDoS attacks.

The ability to analyse events yields valuable information about behaviours, and how they are possibly affected by variables like application format, speed etc. All of this data can be analysed for actionable insights into network and application optimisation in order to generate revenue and attract new customers.

Observability is divided into three pillars: logs, metrics and traces.

This is the record of an event that occurred on a system. Logs are automatically generated, timestamped and written into a file that is unable to be modified. They offer a complete record of events, including metadata about the state of a system and when the event happened. They may be written in plaintext or structured in a specific format.

Metrics are numerical representations of data measured over time. While event logs gather information about specific events, metrics are measured values derived from overall system performance. They usually provide information about application SLIs.

A record of causally-related events as they occur on a network. The events don’t have to happen within a single application, but they must be part of the same request flow. Trace can be formatted as a list of event logs gathered from separate systems involved in the request fulfilment.

The three pillars of observability help bring together data sources that would otherwise be difficult to draw conclusions from alone. This is because, at its heart, observability depends on two things:

• High-context telemetry data with a great deal of runtime context.
• The ability to interact with that data iteratively to glean new insights without deploying code.

When these two factors are in place, businesses have the raw resources they need to improve systems and application observability.

Observability is only as effective as it is feasible; all of the contextualised telemetry data in the world won’t be of any use if teams lack the resources to make it actionable.

Context and topology refers to instrumenting in a way that allows for an understanding of relationships in a dynamic, multi-cloud environment with many interconnected components. Context metadata makes possible real-time topology maps and promotes understanding of causal dependencies through the stack, as well as across services, processes and hosts.

IT efforts are shifted away from manual configuration with automatic discovery, instrumentation and baselining of every system component. Continuous automation adds innovation projects that prioritise understandings of what matters. Observability is scalable, which allows constrained teams to do more with less.

An exhaustive fault-tree analysis, in conjunction with code-level visibility, provides the ability to identify the root cause of anomalies without relying on trial and error, guessing or correlation. Causation-based AI also detects anything unusual to discover what is unknown.

It’s advisable to extend observability to include external data sources. It can provide topology mapping, automated discovery and instrumentation, and actional answers that are needed for observability at scale.