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To trace what happened inside an embedded device, instrument the software to emit useful telemetry, then connect each record to a device or process and an operation. A timestamp alone rarely tells the whole story: engineers need the event, its execution context, and the identity of the system that produced it. OpenTelemetry provides APIs, SDKs, conventions, and collection components for that work; it does not provide the storage or visualization backend. OpenTelemetry describes observability as understanding a system’s internal state by examining its outputs.
What runtime observability tells you
A field failure can leave an incomplete record: a log may show that something happened, but not which operation led to it or which device emitted it. Runtime observability is the practice of producing and connecting signals so engineers can investigate behavior while the software runs. It depends on instrumentation: code, or an appropriate zero-code mechanism, must generate telemetry before a team can inspect it.
OpenTelemetry is a vendor-neutral set of APIs, SDKs, semantic conventions, and collection components for generating, collecting, and exporting telemetry. It is not a database or dashboard. A deployment also needs a destination or backend to store and present the data.
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- Traces show the path of an operation through instrumented components. They help establish what happened before and during a request or task.
- Metrics record measurements over time. They can help reveal changes or thresholds associated with a failure, but their meaning depends on how the specific runtime defines and emits them.
- Logs capture events, often with details useful for explaining a specific moment.
- Baggage carries context across operations. Its use should be deliberate because any propagated information becomes part of the telemetry design.
The OpenTelemetry signals overview also discusses events and profiles as developing or proposed signals; its profiles documentation labels profiles Alpha. These maturity labels can change, so check current documentation before basing an implementation on them.
How do you correlate logs, metrics, and traces?
Correlation turns separate records into an audit trail. OpenTelemetry’s logging guidance identifies three useful forms of context: time, execution context such as trace and span IDs, and resource context describing the entity that produced the telemetry. When a log is associated with a trace and a stable producer identity, an engineer has a better chance of moving from a symptom to the operation and source behind it.
Older or otherwise uninstrumented logs may not contain trace context. Where your software controls log emission, add or enrich records with the relevant context. The OpenTelemetry logging specification explains correlation between logs and other telemetry.
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A Resource represents the observed entity through attributes, allowing users to pivot across signals from the same source. Choose identity attributes that make sense for the product and deployment—for example, the distinction between a device model, a deployed unit, and a software instance may matter to an investigation. The Resource documentation explains the model; it does not prescribe an embedded-device identity scheme. Balance diagnostic usefulness with privacy and the amount of context your system can retain.
What should an embedded team instrument?
Start with failure questions, not a wish to collect everything. Select events and operation boundaries that would help answer what failed, what led to it, and which producer was involved. Add runtime measurements only when their definitions are meaningful for the actual environment.
- Choose failure-relevant operations and events. Identify the tasks, requests, state transitions, or other boundaries that matter during an investigation.
- Define the producer identity. Select consistent Resource attributes that identify the telemetry source in a way useful to your deployment.
- Propagate execution context where the architecture permits. Carry trace context across operation boundaries and include trace/span and resource context in logs where possible.
- Choose measurements with explicit meaning. Document what each runtime metric measures and under what conditions it is emitted.
- Route telemetry through a collection pipeline. Decide how the device or software exports signals to a collector and how the collector forwards them to a backend.
- Verify the investigation path. Confirm that a real record can be followed from log to trace or metric and back to the producer in the selected backend.
- Evaluate the design on the target. Measure its effect on the specific device and transport, then make buffering, sampling, retention, and privacy choices against the system’s requirements.
These are engineering steps, not a platform-independent recipe. The appropriate instrumentation, export path, and operating limits depend on the target and its constraints.
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Code-based or zero-code instrumentation?
OpenTelemetry describes both code-based and zero-code instrumentation. The choice depends on whether application-level detail is needed and what can be changed in the environment.
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| Code-based instrumentation | Can provide deeper application insight. | When the team can instrument application code and needs context around its operations. |
| Zero-code instrumentation | Can expose libraries or environments without changing application code. | When code cannot be modified or as a way to get started. |
The instrumentation documentation describes these approaches. They are not interchangeable: zero-code coverage may not expose the application-specific boundaries and context needed to explain a device-level failure.
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General C++ support does not establish that a particular SDK, exporter, or instrumentation setup fits a particular microcontroller or RTOS. OpenTelemetry’s C++ language page lists traces, metrics, and logs as Stable, but those status labels are not proof of support on every embedded toolchain or device. The C++ documentation describes project-level component status.
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Automatic instrumentation also has a specific limitation: OpenTelemetry’s C++ instrumentation guidance says it is not supported when the source code of the library to be instrumented is unavailable. See the C++ instrumentation documentation.
Runtime metric names and meanings are not universally comparable. The runtime semantic conventions explain that runtime environments differ and do not define a universal set of top-level runtime metric instruments. Interpret measurements in the context of the runtime that produced them.
For a concrete target, validate the SDK build and export path against the device’s CPU, memory, energy, transport, and RTOS constraints. Also determine what happens during disconnection—whether telemetry is buffered, lost, or exported later—and what retention and privacy rules apply. The cited documentation does not provide universal budgets or establish feasibility for a specific target; those require platform-specific documentation or measurement.
Where does the Collector fit?
The OpenTelemetry Collector can receive, process, and forward telemetry to backends. It can form part of a pipeline between instrumented software and storage or visualization tools, but it does not remove the need to choose and configure those destinations. The Collector quick start explicitly says its local demonstration is not production-ready, so treat it as an introduction rather than deployment guidance.
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