The safest useful default is parent-based sampling with a bounded ratio or rate limit for routine traffic, explicit retention rules for errors and slow requests, selective stack-trace enrichment, and redaction performed before telemetry leaves the application. Keep dependency spans and their logs aligned, leave URL-query redaction enabled, sanitize headers, and export only the fields your diagnosis requires. There is no universal safe sampling percentage or retention period: validate the policy against your traffic, jurisdiction and data-access controls.
What a privacy-conscious dependency profile should capture
Dependency profiling is valuable when it explains where a request spent time outside your application: database calls, HTTP clients, queues, caches and other remote services. A useful profile preserves enough context to connect a slow or failed request to the dependency span that caused it, while avoiding payloads and attributes that identify a person or reveal a secret.
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That means optimizing for diagnostic signal per captured event, not for the largest possible trace volume. A complete record of every request can create unnecessary storage, processing and access risk. An aggressively sparse profile can hide the very dependency failures you need to investigate. The right design separates ordinary traffic from exceptional traffic, then applies privacy controls to both.
- Routine requests: use parent-based behavior and a bounded ratio or rate limit so distributed traces remain coherent without unlimited volume.
- Exceptional requests: retain errors, unusually slow operations and security-relevant cases through explicit rules or a tail decision where the backend supports one.
- Stack detail: add stack traces only to selected spans or inferred spans rather than every event.
- Content: redact credentials, identifiers, request bodies and user-controlled attributes before export; collect only fields that have a defined diagnostic purpose.
Use parent-based sampling as the starting point
Distributed tracing works best when related services agree on whether a trace is sampled. OpenTelemetry documents parent-based, always-on, always-off, trace-ID-ratio, remote, links-based and rule-based samplers. Parent-based sampling should normally be the first layer: a child service follows the parent decision instead of creating a fragmented trace in which one service is visible and the next is missing.
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Put a bounded sampler underneath that parent-based behavior for ordinary traffic. A trace-ID ratio sampler gives a predictable proportion; a rate limit caps events when traffic surges. There is no universally safe percentage, and the correct value depends on request volume, dependency cardinality, storage limits and the sensitivity of the data. Start with a value you can review, then measure whether representative dependency paths still appear.
Why parent-based decisions matter for dependencies
Suppose an incoming request is sampled and fans out to three services. Parent-based behavior lets those services keep the same decision, so the database and HTTP client spans can be interpreted with the server span. If each service samples independently, a missing child span can look like a missing dependency call even when the call occurred. Parent-based sampling does not mean every request is retained; it means the requests you do retain remain internally consistent.
Keep rare but important traces out of the ordinary budget
Errors, high-latency requests and security-relevant cases should not rely solely on a random ratio. Add explicit rules where your collector or backend supports them, or use a tail-sampling decision that can inspect the completed trace. This lets normal traffic stay bounded while preserving the evidence most likely to explain an incident. Define the conditions precisely and review them for sensitive attributes: a rule based on a user identifier, for example, can itself become a privacy problem if that identifier is exported.
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That dependency-and-log alignment is important. A sampled server request without its dependency spans is rarely enough to explain latency. Conversely, dependency spans without the request’s logs can remove the error message or application state needed to interpret them. The documented behavior keeps the dependency spans and logs attached to the sampled requests.
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Trace-based log sampling has a boundary
Trace-based log sampling drops logs that belong to unsampled traces. Logs without trace context are unaffected. If your application emits important security or audit events without a trace context, they will not be reduced by trace-based sampling; they need their own retention, access and redaction policy. Do not assume that changing trace sampling controls every log stream.
Before adopting the Java default unchanged, check the agent version and deployment profile, observe the actual request rate, and inspect whether the resulting dependency coverage represents your important traffic. A five-request-per-second ceiling can be generous for a small service and sparse for a high-volume system; neither conclusion can be made from the number alone.
Choose a profiling interval that balances accuracy and overhead
Stack-trace enrichment and inferred-span profiling add detail beyond ordinary span timing. The OpenTelemetry Java inferred-spans extension documents a 50 ms profiling sampling interval by default. Lower intervals can improve duration accuracy, but they also increase overhead and the number of inferred spans. A shorter interval is therefore not automatically better.
When to use the documented 50 ms default
The default is a reasonable baseline when you need inferred call relationships but have not yet measured the cost in your service. Establish the baseline in a representative environment, then compare CPU, allocation, exported span count and latency before changing it. The relevant question is whether the additional timing precision changes a diagnosis or operational decision.
When a lower interval is justified
Lower the interval only for a bounded diagnostic need, such as distinguishing very short dependency operations whose durations are otherwise blurred. Apply it to selected services, inferred spans or an investigation window rather than globally. Document who approved the change and when it will be removed. The extension’s guidance makes the trade-off explicit: duration accuracy improves while overhead and inferred-span volume rise.
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Limit stack traces to useful spans
Do not attach deep stacks to every routine span by default. Select dependency spans, inferred spans or error paths where a stack identifies the call site that needs repair. This reduces both processing cost and the chance that local variable values or framework attributes are captured unintentionally. A stack trace can still expose class names, file paths or user-controlled strings in exception messages, so it belongs inside the same redaction review as other telemetry.
Redact before export, not after storage
OpenTelemetry warns that telemetry collection can inadvertently capture sensitive or personal information subject to privacy and compliance requirements. The practical implication is architectural: apply redaction processors and allowlists inside the application or collector before data reaches an exporter. Deleting a field after it has been written to a remote backend leaves copies in queues, indexes, backups or access logs.
Build an allowlist for attributes
Start with attributes that have a documented diagnostic purpose, such as operation name, status, duration and a non-sensitive dependency identifier. Exclude broad “capture everything” settings. Field-based rules should remove known credential fields and identifiers; pattern-based rules should catch values that appear in free-form attributes or exception text.
- Credentials, API keys, bearer tokens, cookies and connection strings.
- Direct identifiers and stable identifiers that can be linked back to a person.
- Request and response bodies unless a specific incident requires a narrowly scoped, separately protected capture.
- User-controlled attributes, including free-text query values and exception messages, unless normalized and reviewed.
Redaction is not a substitute for minimizing collection. If a field is not needed to answer an operational question, do not capture it and hope a pattern rule removes it later.
Keep URL queries and headers protected by default
The .NET instrumentation documentation exposes an experimental setting that disables ASP.NET Core URL-query redaction. Leave that opt-out disabled unless a narrowly defined diagnostic need has been reviewed and approved. Query strings commonly contain search terms, identifiers, reset tokens and other user-controlled values; retaining them by default can turn an ordinary dependency span into a personal-data record.
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Sanitize captured headers as well. Authorization headers, cookies, proxy credentials and custom correlation headers can contain secrets or identifiers. Use an explicit allowlist for headers that are genuinely needed for diagnosis. Avoid raw request and response bodies unless the incident cannot be investigated otherwise; if body capture is temporarily approved, limit its scope, duration and access and apply the same pre-export controls.
Review redaction with real payloads
Configuration inspection alone is insufficient. Generate representative requests containing a token-like value, a personal identifier, a long query string and an error message with user input. Inspect the payload at the exporter boundary and in the destination system. Confirm that redaction occurs before transmission, that nested attributes are covered and that logs without trace context follow their own policy.
Make privacy controls part of deployment policy
Sampling and redaction settings are operational policy, not one-time instrumentation tweaks. Review them for each environment and jurisdiction. Development traffic may be synthetic, while production traffic can contain regulated data; a configuration that is acceptable in one environment may not be acceptable in another.
- Sampler: record whether parent-based, ratio, rate-limit, rule-based or tail decisions are used.
- Exceptional retention: define how errors, high latency and security-relevant traces are selected and who can change those rules.
- Redaction: maintain field and pattern rules, plus an allowlist of permitted attributes and headers.
- Exporter: document destinations, transport protection and whether processing occurs locally or remotely.
- Access: restrict who can view traces and logs, and audit administrative changes.
- Retention: set a period appropriate to the environment and jurisdiction; the cited guidance does not provide a universal duration.
- Exceptions: give every temporary increase in sampling, interval detail or content capture an owner, purpose, expiry and review record.
OpenTelemetry provides mechanisms and cautions, not a single compliance profile. Your organization’s legal basis, contracts and incident procedures determine the final retention and access choices.
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Compare profiling implementations on the settings that matter
| Decision area | Privacy-conscious target | What to verify |
|---|---|---|
| Dependency completeness under sampling | Parent-based decisions with bounded ratio or rate limiting | Child services retain the same sampled traces and dependency spans are present |
| Error and latency retention | Explicit rules or tail decisions for failures, slow requests and security-relevant cases | Exceptional traces are retained without making all routine traffic always-on |
| Stack-trace depth and interval | Selective enrichment; 50 ms is the documented Java inferred-spans default | Lower intervals improve duration accuracy but increase overhead and inferred spans |
| Pre-export redaction | Field- and pattern-based processors plus attribute allowlists | Secrets, identifiers, bodies and user-controlled fields are removed before export |
| Query and header sanitization | URL-query redaction enabled; headers explicitly allowlisted | No experimental opt-out is enabled without a reviewed exception |
| Processing location | Redaction as close to collection as practical | Unredacted data is not queued or sent to an exporter first |
| Retention and access | Environment- and jurisdiction-specific policy | Duration, roles, auditability and exception expiry are documented |
A practical implementation sequence
- Inventory the dependency questions. List the failures, latency investigations and capacity decisions the profile must answer. Mark fields that are not needed for those questions as prohibited rather than merely optional.
- Enable parent-based sampling. Put a ratio sampler or rate limit beneath it for ordinary traffic. Record the selected value and the expected volume; do not present it as a generally safe percentage.
- Add exceptional rules. Route errors, high-latency traces and approved security cases through explicit rules or a supported tail-sampling decision. Check that the rule itself does not export sensitive identifiers.
- Align dependencies and logs. Confirm that sampled requests carry their dependency spans and related logs. Check separately for logs that lack trace context, because trace-based log sampling does not affect those entries.
- Constrain stack detail. Begin with the documented 50 ms Java inferred-spans interval where applicable and attach stacks only to selected spans. Change the interval only after measuring overhead and diagnostic benefit.
- Install redaction and allowlists. Remove credentials, identifiers, bodies and user-controlled attributes with field and pattern rules before export. Allow only attributes and headers with a documented purpose.
- Verify query protection. Keep ASP.NET Core URL-query redaction enabled. Test that authorization, cookie and other sensitive headers are absent or sanitized.
- Inspect exported payloads. Use representative synthetic values and review the exporter boundary and destination. Check traces, logs, inferred spans, retries and exception attributes.
- Document and review. Record sampler behavior, interval, processors, exporter destination, retention, access roles and temporary exceptions. Revalidate after agent upgrades because defaults vary by language, distribution and release.
Troubleshoot common failure modes
“The trace has a server span but no database or HTTP dependency.”
Check whether child services are honoring the parent decision and whether dependency instrumentation is enabled for that service. Independent sampling can fragment traces; a parent-based layer helps keep sampled traces coherent. Also verify that a rate limit is not being applied separately at each component.
“Logs disappeared after reducing trace sampling.”
Trace-based log sampling drops logs belonging to unsampled traces. Determine whether the missing entries had trace context. Context-free logs are unaffected and require a separate policy, so reducing trace volume will not reduce that stream.
“Short operations are missing or durations look imprecise.”
If inferred spans are enabled, the 50 ms interval may be too coarse for the diagnostic question. Lower it only for the affected scope and measure the resulting overhead and span volume. Do not compensate by enabling deep stacks everywhere.
“A secret appeared in telemetry.”
Stop the export or isolate the destination according to your incident procedure, identify the attribute and path that carried it, then add a field or pattern rule and an allowlist restriction. Inspect queues, retries and stored copies, not just the primary backend. Document the exception and test with a representative secret-like value before restoring export.
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“A query string contains personal data.”
Confirm that the URL-query redaction opt-out is not enabled and that the instrumentation version applies the expected default. Add an explicit sanitizer for any custom URL attribute and test nested or normalized URL fields as well as the standard server span.
How to know the profile is good enough
Evaluate the policy with three questions: Can an operator connect a retained request to its dependency calls? Are failures and slow traces retained at a useful rate? Can you demonstrate that secrets and unnecessary personal data are removed before export? Review these answers after traffic-pattern changes, instrumentation upgrades and incidents.
A profile that passes those checks is more defensible than one chosen from a generic percentage. Sampling remains bounded, exceptional evidence remains available, and privacy controls are enforced at the point where telemetry is created rather than after it has spread.
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