Observability (software)

Observability and monitoring are sometimes used interchangeably.[9] As tooling, commercial offerings and practices evolved in complexity, "monitoring" was re-branded as observability in order to differentiate new tools from the old.

The terms are commonly contrasted in that systems are monitored using predefined sets of telemetry,[6] and monitored systems may be observable.[10]

Majors et al. suggest that engineering teams that only have monitoring tools end up relying on expert foreknowledge (seniority), whereas teams that have observability tools rely on exploratory analysis (curiosity).[3]

A metric is a point in time measurement (scalar) that represents some system state. Examples of common metrics include:

• number of HTTP requests per second;
• total number of query failures;
• database size in bytes;
• time in seconds since last garbage collection.

Monitoring tools are typically configured to emit alerts when certain metric values exceed set thresholds. Thresholds are set based on knowledge about normal operating conditions and experience.

Metrics are typically tagged to facilitate grouping and searchability.

Application developers choose what kind of metrics to instrument their software with, before it is released. As a result, when a previously unknown issue is encountered, it is impossible to add new metrics without shipping new code. Furthermore, their cardinality can quickly make the storage size of telemetry data prohibitively expensive. Since metrics are cardinality-limited, they are often used to represent aggregate values (for example: average page load time, or 5-second average of the request rate). Without external context, it is impossible to correlate between events (such as user requests) and distinct metric values.

Logs, or log lines, are generally free-form, unstructured text blobs that are intended to be human readable. Modern logging is structured, to enable machine parsability.[3] Similar to metrics, an application developer must "instrument" the application upfront, and ship new code if different logging information is required.

Logs typically include a timestamp and severity level. An event (such as a user request) may be fragmented across multiple log lines and interweave with logs from concurrent events.

A cloud native application is typically made up of distributed services which together fulfill a single request. A distributed trace is an interrelated series of discrete events (also called spans) that track the progression of a single user request.[3] A trace shows the causal and temporal relationships between the services that interoperate to fulfill a request.

Instrumenting an application with traces means sending span information to a tracing backend. The tracing backend correlates the received spans to generate presentable traces. To be able to follow a request as it traverses multiple services, spans are labeled with unique identifiers that enable constructing a parent-child relationship between spans. Span information is typically shared in the HTTP headers of outbound requests.[3][13][14]

Continuous profiling is another telemetry type used to precisely determine how an application consumes resources.[15]

To be able to observe an application, telemetry about the application's behavior needs to be collected or exported. Instrumentation means generating telemetry alongside the normal operation of the application.[3] Telemetry is then collected by an independent backend for later analysis.

In fast-changing systems, instrumentation itself is often the best possible documentation, since it combines intention (what are the dimensions that an engineer named and decided to collect?) with the real-time, up-to-date information of live status in production.[3]

Instrumentation can be automatic, or custom. Automatic instrumentation offers blanket coverage and immediate value; custom instrumentation brings higher value but requires more intimate involvement with the instrumented application.

Instrumentation can be native - done in-code (modifying the code of the instrumented application) - or out-of-code (e.g. sidecar, eBPF).

Verifying new features in production by shipping them together with custom instrumentation is a practice called "observability-driven development".[3]