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The safest useful profile combines parent-based sampling with a bounded ratio or rate limit for routine requests, then keeps errors, high-latency operations and security-relevant traces through explicit rules or tail-based decisions. Preserve the dependency spans and logs attached to a sampled trace, enrich stack traces only where they answer a diagnostic question, and redact sensitive fields locally before telemetry leaves the environment. There is no universally safe sampling percentage or retention period: settings must be measured, reviewed and documented for each workload and jurisdiction.
What “useful dependency data” should contain
Dependency profiling is valuable when it explains where a request spent time and which downstream systems shaped its result. A practical trace usually needs the service span, child spans for database, cache, queue and HTTP calls, duration, outcome, correlation identifiers and enough operation metadata to distinguish one dependency from another. It does not require a complete record of every request or the original request body.
Privacy protection changes the question from “How much telemetry can we collect?” to “Which observations change an engineering decision?” Keep data that helps you find slow calls, failures, retries, saturation and unusual access patterns. Remove or transform values that identify a person, authenticate a request or reproduce its content. Sampling, enrichment and redaction are separate controls: a low sampling rate does not make an unredacted payload safe, and redaction does not solve the storage and access cost of collecting everything.
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Build the sampling policy around trace relationships
Use parent-based behavior first
OpenTelemetry documents parent-based, always-on, always-off, trace-ID-ratio, remote, links-based and rule-based samplers. Parent-based behavior should be the starting point for distributed systems because it follows the upstream sampling decision. A child service that independently samples can produce a trace with missing segments, making a dependency appear healthy simply because its span was discarded.
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In a parent-based policy, an inbound sampled decision is propagated to downstream services. For a root request, apply your chosen ratio or rate limit. For an inbound unsampled decision, keep the child unsampled unless an explicitly documented exception applies. Record the policy and propagate the standard trace context so every participating component evaluates the same parent decision.
Bound ordinary traffic with a ratio or rate limit
Routine traffic is where volume grows fastest. A trace-ID ratio sampler gives a predictable fraction of root traces; a rate-limited sampler gives a predictable approximate throughput. Choose between them according to the question you need to answer:
| Approach | Best use | Important limitation |
|---|---|---|
| Parent-based plus trace-ID ratio | Stable statistical coverage across a high-volume service | A fixed ratio can still produce bursts when traffic changes, and it does not by itself prioritize failures. |
| Parent-based plus rate limit | Keeping exporter and storage volume within an operational budget | Rare events may be missed unless exception rules or tail decisions preserve them. |
| Rule-based or tail decision | Retaining errors, high latency, selected operations or security-relevant cases | Requires a backend or collector that can evaluate the complete trace, plus memory and processing capacity. |
| Always on | Short diagnostic windows, controlled test environments or very low traffic | Can create unnecessary cost and privacy exposure in production. |
Do not advertise a ratio as a compliance guarantee. The sources provide mechanisms and defaults, not a universal safe percentage. Establish a starting point from traffic volume, dependency cardinality, exporter capacity and the sensitivity of attributes, then validate it with representative load and exported payload inspection.
Preserve exceptional traces deliberately
Ordinary sampling should not decide the fate of every event. Add explicit rules for failed requests, timeouts, high-duration requests and operations that your incident or security teams must investigate. Where tail sampling is available, make the decision after spans arrive so a slow child dependency or an error status can cause the whole trace to be retained. Keep these exceptions narrow and documented; an “keep everything unusual” rule can become an unbounded collection rule during an outage.
How much should you sample dependencies?
Start from the trace, not from an isolated dependency. If a root trace is selected, retain its relevant child spans so the resulting dependency graph is coherent. Sampling only database spans, for example, can show query timings without revealing which endpoint, tenant-free operation or retry path caused them. Conversely, collecting every child span when the parent is dropped defeats the volume control and can create orphan data.
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- Measure the unsampled shape. In a bounded test or short controlled window, estimate requests per second, average spans per request, log volume, attribute cardinality and exporter payload size.
- Select a parent-based root policy. Use a ratio when proportional coverage matters, or a rate limit when a predictable ceiling matters.
- Define retention exceptions. Match errors, latency thresholds and security cases to explicit rules or tail decisions. State whether retries and partial failures qualify.
- Check downstream coherence. Confirm that propagation keeps sampled children and that unsampled parents do not create unexpected dependency-only traces.
- Recalculate after traffic changes. A setting that is safe at one request rate can overwhelm a collector after a release or seasonal surge.
- Review the exported payload. Sampling decisions and privacy decisions must be verified from what actually leaves the process, not only from configuration files.
Use separate policies for development, staging and production. Development may need a temporary higher rate to debug a new dependency, while production normally needs stricter volume and data controls. Record who can change each policy, what evidence is required for an exception and when it expires.
Keep dependency spans and logs aligned
Logs and traces answer different questions, but they become confusing when their sampling decisions disagree. Microsoft Azure Monitor documentation states that Java agent version 3.4.0 introduced rate-limited sampling as the default, capturing approximately five requests per second together with all dependencies and logs on those requests. That figure is a documented default for that agent behavior, not a universal target for every language, release or workload.
Trace-based log sampling drops logs that belong to unsampled traces. Logs without trace context are unaffected, so an application can still emit a large volume of standalone records even after trace sampling is reduced. Decide separately whether context-free logs should be rate-limited, filtered or retained for audit purposes. Check that correlation fields survive redaction so an investigator can still connect an approved log to its trace without exposing a user identifier.
When changing a sampler, test three cases: a sampled successful request with several dependencies, an unsampled successful request, and a failed or slow request that should be retained by an exception rule. Verify that the retained trace includes the expected dependency spans and associated logs, while unrelated context-free logs follow their own policy.
Use stack traces selectively and choose the interval deliberately
Stack traces can explain where an inferred dependency span originated, but collecting them on every span increases CPU work, payload size and the chance of capturing sensitive values in method arguments or exception text. Add stack-trace enrichment only to selected spans or inferred spans where call-site detail changes the diagnosis.
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The OpenTelemetry Java inferred-spans extension documents a 50 ms profiling sampling interval by default. Lowering the interval can improve duration accuracy because the profiler observes execution more frequently, but it also increases overhead and the number of inferred spans. A higher interval reduces overhead while making short-lived work easier to miss or less precisely timed. Treat 50 ms as a documented default, not as a promise that every runtime or extension has the same setting.
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- Keep the default while establishing a baseline for CPU, allocation, span count and duration error.
- Lower the interval only for a narrowly defined investigation of short or bursty calls, and bound the investigation window.
- Raise or disable enrichment on high-throughput paths where stack detail is not needed for a current question.
- Exclude sensitive code paths and avoid recording raw arguments, request bodies or credential-bearing exception text.
Compare the added diagnostic value with the measured overhead in a representative environment. A more detailed profile is not automatically a better profile if it changes application latency or fills storage with redundant frames.
Redact before telemetry leaves the process
OpenTelemetry warns that telemetry collection can inadvertently capture sensitive or personal information subject to privacy regulations and compliance requirements. Its .NET guidance describes redaction as crucial for compliance, security, privacy and risk reduction. Apply those controls before export, ideally in the instrumented process or a collector located inside the trust boundary, so an external exporter never receives the original value.
Use allowlists and targeted deny rules together
An allowlist for attributes is the stronger baseline: export only fields that have a defined diagnostic purpose. Add field-based rules for known sensitive keys and pattern-based rules for values that can appear under unexpected keys. Cover credentials, access tokens, cookies, authorization headers, payment or government identifiers, email addresses, phone numbers, request bodies and user-controlled attributes. Replace values with a stable hash, a category such as [redacted], or deletion according to the investigative need; do not assume hashing makes a low-cardinality identifier anonymous.
Order the controls so raw data cannot escape
- Capture only the fields required by the instrumentation.
- Apply field and pattern redaction immediately after capture.
- Apply an allowlist before serialization or export.
- Inspect representative exported payloads, including errors and unusual requests.
- Test that redaction also covers exception messages, span events, resource attributes and logs.
Keep redaction rules versioned with deployment policy. A new library can add an attribute name, and a new endpoint can place secrets in a field that was previously harmless. Treat rule changes as security-sensitive configuration, with review and rollback.
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Protect URL queries, headers and bodies by default
Query strings often contain search terms, account identifiers, reset tokens or signed parameters. The .NET instrumentation documentation exposes an explicit experimental setting that disables ASP.NET Core URL-query redaction. Leave that opt-out disabled unless a narrowly defined diagnostic need has been reviewed, approved and time-limited. If a query value is essential, capture a classified parameter or a redacted representation rather than the complete query string.
Sanitize captured request and response headers. Keep only headers with a documented operational purpose, and remove authorization material, cookies, proxy credentials and other secrets. Avoid raw request and response bodies unless they are essential to a specific diagnostic workflow; if they must be captured temporarily, restrict the routes, fields, access group and retention window, and verify the redaction with test payloads containing fake secrets.
Make privacy controls a deployment policy
Sampling and redaction are not one-time agent settings. Review the following together for each environment and jurisdiction:
- Sampler behavior: parent handling, ratio or rate limit, exception rules and tail-decision capacity.
- Data scope: dependency span types, stack-trace targets, attributes, events, headers and body capture.
- Processing location: which transformations happen in-process, in a local collector or in a remote backend.
- Exporter destination: region, contractual controls, encryption and tenant separation.
- Access: role-based permissions, audit logs and emergency access procedures.
- Retention: separate periods for ordinary traces, incident captures and security records; the sources do not establish a universal retention period.
- Change control: owner, approval path, review date and expiry for diagnostic exceptions.
Local processing can reduce exposure because secrets are removed before transmission, while remote processing can centralize policy and tail decisions. The right split depends on trust boundaries and available collector capacity. Document what data is impossible to recover after redaction so investigators know the limits before an incident.
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Compare profiling implementations with the same questions
| Evaluation axis | Questions to ask | Evidence to collect |
|---|---|---|
| Dependency completeness | Does a sampled parent retain its child dependencies, and are unsampled children suppressed? | End-to-end traces containing database, cache, queue and HTTP calls. |
| Error and latency retention | Can explicit rules or tail decisions keep failures and slow traces? | Test traces that cross each status and duration threshold. |
| Stack-trace controls | Which spans can be enriched, and what interval and depth controls exist? | CPU, allocation, span-count and timing comparisons at the chosen interval. |
| Pre-export privacy | Are allowlists, field rules and pattern rules applied before serialization? | Exporter payloads containing seeded fake secrets and identifiers. |
| Query and header sanitization | Are URL queries redacted by default, and can sensitive headers be excluded? | Requests with tokens, cookies and user-controlled parameters. |
| Processing location | Where is the first trusted redaction and sampling decision made? | Network captures or collector logs showing no raw sensitive value crosses the boundary. |
| Retention and access | Can policies differ by environment, jurisdiction and incident type? | Configuration, role review and deletion evidence. |
Failure modes and corrective actions
Dependency graphs have gaps
Check parent propagation, sampler inheritance and instrumentation coverage before increasing the sampling rate. Independent child sampling is a common cause of orphan spans; fixing coherence usually produces more useful data than collecting more traces.
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Storage spikes during incidents
Inspect whether an exception rule retained every retry or whether a rate limit was bypassed by an always-on component. Bound incident rules, cap diagnostic windows and ensure tail-sampling buffers have capacity.
Logs remain expensive after trace sampling
Find records without trace context, because trace-based log sampling does not affect them. Give standalone logs an explicit volume and field policy rather than assuming the trace sampler controls them.
Profiles contain secrets
Stop export from the affected pipeline, preserve only the minimum evidence needed for incident handling, add field and pattern rules, and retest with seeded values. Review exception messages, span events and resource attributes, not only the obvious URL field.
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Restore the documented default, narrow stack enrichment to selected spans and repeat the measurement under representative load. Duration accuracy is useful only when the added overhead does not distort the system being observed.
A compact operating checklist
- Set a parent-based root policy with a tested ratio or rate limit.
- Define explicit retention for errors, high latency and security-relevant operations.
- Verify that sampled traces include their dependency spans and associated logs.
- Keep the 50 ms inferred-span interval unless measurements justify a bounded change.
- Use selective stack traces, not blanket enrichment.
- Redact and allowlist before export; test payloads with fake secrets.
- Leave URL-query redaction enabled and sanitize headers and bodies.
- Review exporter region, access controls and retention for each jurisdiction.
- Document every exception, owner, approval and expiry date.
- Revalidate after agent, library, traffic or schema changes.
The result is not the maximum amount of telemetry. It is a coherent, bounded set of dependency evidence that preserves the traces most likely to explain failures and latency while removing data that the diagnostic question does not require.
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