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What missing instrumentation actually means
Instrumentation is the code and configuration that makes an application emit telemetry. OpenTelemetry defines the core signals as traces, metrics and logs; its documentation puts the requirement plainly: “For a system to be observable, it must be instrumented: that is, code from the system’s components must emit signals, such as traces, metrics, and logs.” See OpenTelemetry’s instrumentation documentation.
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A service can therefore be running, exporting CPU and memory measurements, and showing framework request spans while still hiding the business operation that explains a failure. A checkout authorization branch, a retry decision, a recommendation calculation or a background-job state transition may execute entirely inside application code that produces no domain-level signal. Google Cloud’s application-developer guidance notes that system-level CPU, memory and disk measurements do not provide much insight into application-level concerns by themselves: application observability guidance.
Automatic edges versus application logic
OpenTelemetry separates code-based instrumentation from zero-code instrumentation. Code-based APIs and SDKs let developers create spans, metrics and logs at meaningful points in their own code. Zero-code methods use agents, bytecode manipulation, monkey patching or eBPF injection to collect telemetry from supported runtimes, libraries and infrastructure without editing each application function. They commonly observe incoming requests, database calls and message-queue interactions, but they do not normally know the meaning of a domain decision. Both approaches can run together; zero-code coverage is a foundation, not proof that the application is fully instrumented. See OpenTelemetry’s zero-code overview.
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| Approach | Usually reveals | Typical blind spot | Best use |
|---|---|---|---|
| Zero-code instrumentation | Framework requests, supported database clients, messaging libraries and runtime edges | Application-specific branches, business rules and state transitions | Fast baseline coverage with little or no source change |
| Code-based instrumentation | Domain operations, important decisions, custom measures and explicit error context | Requires design, maintenance and review of sensitive attributes | Explain why the application behaved a certain way |
| Combined approach | Infrastructure and library edges plus business semantics | Gaps can still remain at unpropagated or unsampled boundaries | Most complete view when configuration and context are correct |
Start with journeys, not files
Searching every source file for a missing span creates noise. Begin with the journeys whose failure would matter to users or operators. Examples include signing in, searching, checking out, completing a background job or running a model-assisted workflow. For each journey, document the entry point, expected service boundaries, asynchronous hand-offs and important dependencies.
Build a journey coverage matrix
Write down the evidence needed to diagnose each journey before looking at the telemetry you already have. A useful matrix has one row per journey and columns for the following signals:
- An entry trace that identifies the service, operation and request outcome.
- Child spans for important database, cache, HTTP and message-queue calls.
- Business-operation spans for decisions or stages that cannot be inferred from generic framework work.
- Latency and error measurements, including counts for meaningful outcomes such as rejected authorization or expired inventory.
- Structured logs or domain events correlated to the trace and scrubbed of secrets and personal data.
- Context propagation across service, thread, process and queue boundaries.
Mark each item as required, useful or unnecessary for that journey. This turns “fully instrumented” into a testable statement: a journey is covered when the required evidence is emitted, delivered and correlated under its normal, slow and failing paths.
A step-by-step method for finding gaps
1. Choose critical journeys and failure cases
List the paths that deserve diagnostic evidence. Include at least one normal request, a validation or authorization failure, a dependency failure and a slow path where the system waits or retries. For asynchronous work, include both producer submission and consumer completion. Record which service or process should own each stage.
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2. Define expected evidence and naming
For every stage, specify the span or metric name, the parent relationship, the outcome and the attributes an operator will need. Follow OpenTelemetry semantic conventions where they apply, and establish project conventions for custom names. Keep attributes bounded and low-cardinality: do not put raw request bodies, tokens, passwords or unrestricted user input into telemetry.
3. Generate representative traffic
Use a test or staging environment whenever practical. Exercise successful, invalid, slow and dependency-error cases, then record the identifiers returned by the application. Include enough traffic to expose sampling rules, retries and concurrency effects. A single happy-path request cannot prove that an error branch or queue consumer is covered.
4. Follow the trace through the backend
Open the trace and verify that service identity, parent-child relationships and trace context survive every boundary. Check whether the expected database, HTTP and messaging spans appear under the right parent. For logs and metrics, confirm that correlation fields and resource attributes point to the same service and deployment.
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Classify each discrepancy rather than immediately adding a span:
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- A missing service segment may indicate absent instrumentation, a deployment that did not receive the agent, or broken context propagation.
- An unexplained gap between parent and child spans may be an asynchronous boundary, a sampling decision or an exporter failure.
- A dependency that appears only on some code paths may be legitimate conditional behavior or an uninstrumented client.
- A trace that shows framework work but cannot distinguish business stages usually needs deliberate code-based instrumentation.
- An error with no useful cause may need exception recording and carefully selected attributes at the application boundary.
6. Prove that telemetry delivery works
Before editing application code, verify SDK or agent initialization, service naming, endpoint configuration, credentials, network access, exporter and collector logs, sampling, filters and backend ingestion. Telemetry can exist locally and still disappear before storage. If the backend receives no signals at all, treat that as a pipeline problem until a controlled test demonstrates otherwise.
7. Cover supported edges automatically, then add domain spans
Use zero-code coverage for supported frameworks, clients and runtime edges. Add code-based spans, metrics or logs around the operations that carry domain meaning: authorization decisions, inventory reservations, payment-state transitions, retry policies, feature-flag choices and job state changes. Keep the custom layer small and intentional; instrument decisions that change diagnosis, not every helper function.
8. Re-run the journeys after each change
Generate the same normal, error and slow traffic again. Confirm that the expected signals reach the backend, that parent-child relationships are intact and that attributes contain no sensitive data. Microsoft’s Application Insights guidance recommends running the application, generating traffic or log messages and checking the Azure portal; it notes that data can take a few minutes to appear: Enable OpenTelemetry in Application Insights.
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| What you observe | Checks to perform | Likely next action |
|---|---|---|
| No telemetry at all | SDK or agent startup, service identity, endpoint, credentials, network route, collector and exporter errors | Repair initialization or export, then repeat a controlled request |
| Requests appear but internal work is opaque | Whether the missing work is application logic rather than a supported library call | Add a code-based span or metric around the business operation |
| A trace stops at a service or queue boundary | Propagation headers, producer and consumer instrumentation, async context handling and sampling | Fix propagation or instrument both sides of the boundary |
| Errors have no actionable context | Exception recording, status setting and safe attributes at the application boundary | Record the exception and outcome without secrets or personal data |
| Only some requests are visible | Head or tail sampling, filters, traffic mix and exporter limits | Adjust or account for sampling before declaring code coverage missing |
| Services are grouped incorrectly | Resource attributes and service identity, including cloud role naming | Correct identity fields; Microsoft notes that multiple services sending to one Application Insights resource need Cloud Role Names for a useful Application Map |
Use AI as a bounded review assistant
An AI assistant is most useful when the question is constrained by a journey and backed by evidence. Provide the relevant source files, language and framework versions, expected journey map, current telemetry examples, sampling configuration and your naming and privacy conventions. Do not provide production secrets or unrestricted customer data.
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What to ask for
- Identify operations on the supplied journey that have no visible span, metric or correlated log.
- For every suspected gap, cite the function and code path that supports the finding.
- Separate confirmed observations from inferences and list what runtime evidence would confirm each inference.
- Suggest the smallest OpenTelemetry change that exposes the operation, including parent context and semantic attributes.
- Flag duplicate instrumentation, high-cardinality fields, sensitive data and possible overhead or maintenance concerns without asserting unverified performance numbers.
- Propose queries or trace checks that an engineer can run after the change.
A practical review prompt
Review these files for the checkout journey. The expected stages are request entry, cart load, inventory reservation, authorization decision, payment submission, order persistence, and confirmation publishing. Identify likely instrumentation gaps, show the code path for each, distinguish evidence from inference, propose minimal OpenTelemetry spans or metrics, and flag sensitive or high-cardinality attributes. Do not claim complete coverage. End with a verification checklist for normal, rejected, timed-out, retried, and asynchronous cases.Validate every AI suggestion
Review the proposed code as you would any change from a junior engineer. Confirm that the span starts and ends around the intended operation, inherits the correct context, records failures without swallowing them, and uses stable names. Deploy to a controlled environment, generate representative traffic and inspect the backend. “AI suggested a likely gap” is a valid interim finding; “the application is missing instrumentation” requires code-path and telemetry evidence.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Implement business-level instrumentation safely
Choose meaningful boundaries
Instrument operations that answer an incident question: why was access denied, which inventory rule rejected the order, where did a retry begin, or why did a job remain pending? A span around a domain operation should have a stable name and a small set of attributes such as outcome, version, bounded category or dependency status. Avoid identifiers that create a unique time series or expose confidential values.
Record failures with context
At the application boundary, set the operation outcome and record the exception or error according to the SDK for your language. Include safe facts that help reproduce the decision, such as a validation category or dependency status, rather than a full payload. Logs should carry the trace and span correlation fields so an operator can move from a trace to the relevant event.
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Preserve context across asynchronous work
Propagate trace context when a request publishes a message and extract it when a consumer starts processing. For queues, instrument both enqueue and consume operations, then verify that retries and dead-letter handling do not create misleading parentage. Similar checks apply to thread pools, scheduled jobs and process boundaries.
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- Powerful Pass-Through Charging: Supports up to 85W pass-through charging so you can power up your laptop while you use the hub. Note: Pass-through charging requires a charger (not included). Note: To achieve full power for iPad, we recommend using a 45W wall charger.
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Microsoft OpenTelemetry distribution details to check
Microsoft Learn’s current Application Insights guidance lists Microsoft OpenTelemetry Distro packages for Python (microsoft-opentelemetry), Node.js (@microsoft/opentelemetry) and .NET (Microsoft.OpenTelemetry); Java uses agent or native-image instructions. Supported language versions and migration details can change, so consult the live guide for the runtime you deploy: Application Insights OpenTelemetry setup.
Do not initialize both the Microsoft OpenTelemetry Distro and the Azure Monitor OpenTelemetry Distro in the same application. Duplicate initialization can produce duplicate or conflicting telemetry. Whichever distribution you use, verify startup logs, exporter configuration, service identity and the actual signals in the portal before adding custom spans.
What “fully instrumented” can and cannot mean
No single trace view proves that every possible path emits every useful signal. Confidence comes from a maintained inventory of critical journeys, expected evidence, representative tests and backend checks. Sampling means the absence of a particular request in storage is not necessarily a code gap; filters can intentionally remove data. Conversely, a trace with framework spans is not evidence that domain logic is covered.
Discovering all telemetry an application could emit is itself a recognized challenge. An OpenTelemetry specification proposal by Laurent Querel of F5 describes Application Telemetry Schema concepts intended to support tooling such as CI/CD compatibility and privacy checks. The document presents a vision; its names and formats remain under discussion, so it is not a mature standard or a universal automatic inventory: Application Telemetry Schema vision and roadmap.
Keep instrumentation coverage from regressing
- Store the journey matrix beside the service code and update it when a critical flow changes.
- Add a smoke test that exercises each required path and asserts the presence of key spans, outcomes and propagation links in a test collector or backend.
- Review sampling and filters as configuration changes, not only as code changes.
- Check instrumentation after framework, SDK, agent and collector upgrades.
- Use code review to reject secrets, raw payloads and unbounded attributes.
- Track exporter and collector health separately from application correctness.
- Give on-call engineers saved queries for the domain spans that explain the most common incidents.
How to compare observability approaches and backends
Keep instrumentation and export choices separable where feasible. Google Cloud recommends vendor-neutral open-source instrumentation even for organizations that plan to send telemetry only to Google Cloud, citing portability, standardized collection and interoperable libraries: Google Cloud observability guidance.
| Comparison area | Questions to answer for your workload |
|---|---|
| Instrumentation depth | Can the approach cover your language, libraries, asynchronous boundaries and domain operations? |
| OpenTelemetry support | Which SDKs, agents, receivers and semantic conventions are supported and maintained? |
| Correlation and workflow | Can an engineer move from a trace to logs, metrics, profiles or deployment data? |
| Query and alerting | Can you express journey-level failures, latency and outcome alerts without excessive custom work? |
| Retention and access | Do retention periods, tenancy, audit controls and role-based access fit the data classification? |
| Deployment constraints | Can agents, collectors or eBPF run in your hosts, containers, serverless platform and regulated environments? |
| Cost model | How are ingestion, storage, retention, querying and export charged for your actual volume and sampling policy? |
There is no universal winner. Select the combination that covers the journeys you operate, preserves the context you need, protects sensitive data and remains maintainable for your team.
Further reading
The preview of Learning OpenTelemetry discusses instrumentation strategies, telemetry collection and Collector deployment. Verify the current edition and availability before relying on it: book preview PDF.
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