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To track production errors, connect application instrumentation to an error-monitoring backend, verify that events arrive with readable stack traces and release context, then configure sampling, privacy controls, and alerts around your service’s needs. Installing an SDK is only the first step: a production-ready setup also needs a way to identify which service and deployment produced an error and who should act on it.
Choose an instrumentation approach
Most teams start with either a vendor’s error-monitoring SDK or OpenTelemetry instrumentation connected to a telemetry backend. The right choice depends on your language and framework, how much automatic instrumentation is available, and whether you want a vendor-managed workflow or more control over export and backend selection.
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| Approach | What to evaluate |
|---|---|
| Vendor SDK | Platform coverage, issue grouping, stack and source mapping, alert integrations, filtering and retention controls. Configuration and field names vary by vendor and SDK version. |
| OpenTelemetry | Supported libraries, manual instrumentation needs, exporter and backend options, trace context across services, sampling controls, and the operational work of running or managing the telemetry pipeline. |
The sources cited here do not establish a neutral provider ranking or current prices. Compare the options against your own stack and expected event volume rather than assuming one is universally better.
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OpenTelemetry’s JavaScript zero-code instrumentation guide describes installing @opentelemetry/api and @opentelemetry/auto-instrumentations-node, then loading the registration module when starting the application. Its example configures OTLP export and sets OTEL_SERVICE_NAME; resource detectors can be selected using OTEL_NODE_RESOURCE_DETECTORS.
Automatic instrumentation can cover many popular libraries, but do not assume it covers every dependency or custom code path. Check the supported instrumentation list against the libraries your application actually uses. For a vendor SDK, use the current platform-specific guide: the vendor’s error-monitoring page, for example, includes platform initialization examples and advises configuring the SDK early.
Connect events to the running service and release
Configure an exporter or SDK endpoint that sends events to the intended project or backend. Include service identity and deployment context so responders can distinguish one service, environment, or release from another. Exact attribute names depend on the instrumentation and backend; follow their current documentation rather than copying a field name from an unrelated stack.
Initialize instrumentation early enough in the process startup sequence to capture errors that occur during initialization. Confirm the registration code or SDK setup actually runs in the deployed application, not just in a local script or test process.
Verify ingestion and make stack traces useful
- Use a safe environment. Trigger a known test exception or event where it will not affect real users.
- Confirm delivery. Check that the event appears in the intended project and environment, and that it identifies the expected service.
- Check code context. Inspect the stack trace and confirm it corresponds to the code version that generated the event. Sentry’s Developer Quick Reference Guide recommends uploading source maps or platform debug files such as ProGuard, dSYM, or PDB files for useful stack traces.
- Inspect investigation context. Check whether relevant tags and breadcrumbs help explain what happened before the error. Add only context that is useful and appropriate to collect.
If the event does not appear, first verify that instrumentation is initialized, the endpoint and credentials are correct, and the running process loads the registration module. Then check exporter or SDK logs and any network or backend ingestion errors.
Set sampling to fit the workload
Sampling reduces telemetry volume and overhead, but it also means some data is not retained. OpenTelemetry’s sampling guidance describes 1,000 or more traces per second as one circumstance in which teams may consider sampling—not a mandatory threshold or general performance benchmark. It also notes that rates of 1% or lower occur in some high-volume systems; this is contextual guidance, not a recommended default for every service.
| Sampling method | Decision point | Trade-off |
|---|---|---|
| Head sampling | Early in a trace, before its full outcome is known | Simple and efficient, but cannot select a trace based on an error or latency discovered later. It cannot guarantee retention of every error-containing trace on its own. |
| Tail sampling | After considering most or all spans in a trace | Can retain traces selected for errors or high latency, but is more resource-intensive and operationally complex. Monitor sampler capacity and the risk of losing useful data if it cannot keep up. |
Choose a policy based on what you need to investigate, the volume your backend can handle, and the cost of missing an event. Review the resulting data after rollout; a percentage that works for one service may be unsuitable for another.
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Make production configuration safe
Telemetry can contain exception text, request details, user identifiers, and custom attributes. Decide what your application is allowed to send, then apply filtering and retention settings in the SDK, collector, or backend as appropriate. Review those choices against your organization’s security and privacy requirements and the jurisdiction where the application operates; exact controls and legal obligations depend on the selected technology and context.
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For OpenTelemetry’s JavaScript zero-code module, the guide recommends OTEL_LOG_LEVEL=info in production. It warns that debug logs are extremely verbose, go to the console, and may negatively affect application performance. Review resource detection and avoid collecting identifiers or attributes you do not need.
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Route alerts into an actionable response
An alert should have an owner and a defined response, not merely announce that an event occurred. Decide which production conditions warrant notification, who receives it, and what action they can take. Base thresholds and time windows on service impact and observed baseline behavior rather than copying an unrelated example.
Sentry’s quick-reference guide describes issue and metric alerts, issue grouping and assignment, and integrations with collaboration, issue-tracking, and escalation tools. The operational questions apply regardless of backend: who owns a recurring issue, who is notified during a production spike, and how does a resolved regression connect to the deployment that introduced it?
Roll out in stages
- Instrument one service or a limited deployment first.
- Verify delivery, service identity, release context, and readable stack traces with a known event.
- Review event volume, console or exporter logs, and the usefulness of tags and breadcrumbs.
- Apply sampling and privacy filters, then confirm that the resulting events still support the investigations your team needs.
- Enable alerts with a named owner and a response path; expand rollout once the signal is useful and the operational load is understood.
OpenTelemetry’s Tracing SDK specification provides additional detail on trace SDK behavior. For setup, however, the implementation guide for your language, framework, SDK version, and backend is the source for exact configuration.
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