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Head-based sampling decides whether to keep a trace early, usually when an SDK starts a span; tail-based sampling decides downstream, after it has seen all or most of the trace. Head sampling is simpler and cheaper to operate, while tail sampling can preserve traces because they contain an error, took too long, or match an important attribute. The choice is about when a decision is made and what information is available—not about one universally correct sampling percentage.
What is the difference between head-based and tail-based sampling?
OpenTelemetry describes head sampling as a technique that “make[s] a sampling decision as early as possible.” In practice, an SDK commonly makes that decision when a span starts. A tail sampler waits until spans have arrived downstream and can consider the trace’s accumulated outcomes and attributes.
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| Dimension | Head-based sampling | Tail-based sampling |
|---|---|---|
| Decision point | Early, typically at span creation in an SDK | Downstream, after all or most spans for a trace arrive |
| Information available | Trace ID, parent sampling decision, and data known when the span starts | Outcomes and attributes accumulated across the trace |
| Typical selection | Deterministic or ratio-based sample | Errors, slow traces, selected attributes, or different rates by class |
| Main advantage | Relatively simple and efficient; can reduce volume early | Can retain traces based on what happened during the full request |
| Main cost | Cannot reliably select on a later error or total trace latency | Needs stateful processing, resource planning, monitoring, and reliable trace routing |
Head sampling is useful when a representative subset is enough and reducing data before it travels farther is important. Its limitation is informational: at the start of a request, the SDK cannot know whether a later operation will fail or make the entire trace unusually slow. Tail sampling can use those later facts, but has to hold trace data while making its decision.
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A root span’s sampler can make a deterministic or ratio-based decision, often using the trace ID and a target probability. The decision is recorded in the span context and propagated. With parent-based behavior, child spans follow the parent’s sampled state rather than making unrelated decisions that could fragment a distributed trace.
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The Go SDK documentation describes samplers including AlwaysSample, NeverSample, TraceIDRatioBased, and ParentBased. It says the default tracer provider uses ParentBased with AlwaysSample, and suggests considering ParentBased with TraceIDRatioBased in production. These are Go-specific details; check the documentation for the language SDK and version you actually deploy rather than assuming defaults or environment-variable behavior are identical across languages.
For probability sampling, OpenTelemetry’s specification describes consistent decisions based on shared randomness and a rejection threshold. It distinguishes parent/child sampling inside SDKs from downstream decisions in the collection path. It also describes how threshold information can be carried in TraceState; sampling stages that change an effective threshold must update the encoded threshold to preserve its statistical meaning. Treat this as specification context, not a guarantee that every installed SDK or Collector release implements every detail identically.
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How tail sampling works in the Collector
The OpenTelemetry Collector’s Tail Sampling Processor can apply policies after spans arrive, such as retaining error traces, traces over a latency threshold, or traces with particular attributes. Because a decision depends on data arriving over time, the processor maintains trace state and waits for enough spans—or for a configured decision window—before deciding. The Collector component catalog lists the processor as a contrib and Kubernetes distribution component with beta trace support. Component availability and stability can change, so verify the component and its status in the exact Collector distribution and release you run.
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What the OpenTelemetry demo illustrates
The OpenTelemetry demo configuration shows policy combinations, not production defaults. Its example uses a 10-second decision_wait, num_traces: 100000, and expected_new_traces_per_sec: 1000. It sets sampling at 100% for services marked critical, 50% for high criticality, 10% for medium, and 1% for low; those rates and a 5,000 ms slow-trace threshold belong to that demo configuration and are not universal recommendations. In the example, error traces are eligible regardless of criticality, while the slow-trace policy applies to critical and high-criticality services.
When should I use tail sampling?
Use tail sampling when the reason to keep a trace becomes clear only after spans finish. Examples include an error anywhere in the request, total latency above a service-specific limit, or attributes that identify a valuable transaction class. It is most useful when preserving those uncommon but important cases matters more than the extra state, compute, and operational complexity.
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Choose head sampling when the goal is straightforward volume reduction and a representative sample is sufficient. Consider using no sampling if trace volume is low or regulations prohibit dropping data and there is no safe unsampled-data route. OpenTelemetry’s concepts documentation names 1,000 or more traces per second as one condition in which sampling may be worth considering, and says 1% or lower can be representative in some high-volume systems. These are decision cues, not a universal cutoff or a guarantee for a particular workload.
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A combined approach can limit volume early and then apply context-aware selection downstream, but it cannot recover traces already rejected at the head. If a head sampler discards a trace, a tail sampler never receives it. Set the early rate with that irreversible loss in mind.
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How to choose and operate a sampling strategy
Compare strategies against the consequences of missing data, not just the reduction in exported spans. OpenTelemetry identifies direct compute cost, engineering maintenance, and the opportunity cost of missing critical information as relevant sampling costs.
- Decision context: Do you need to select by a fact available at span creation, or by an error, latency, or attribute known only later?
- Representativeness: Is a probabilistic subset adequate, or must particular classes of traces be retained?
- Capacity: Can the collection path sustain stateful buffering, memory use, and processing at peak traffic?
- Routing and reliability: Can spans from each trace reach the same sampler, and what happens to decisions or data during overload?
- Backend and compliance: Can the backend use the chosen trace set, and are you permitted to drop unsampled telemetry?
Set rates and policies from workload risk, volume, and backend needs. Measure the effect on both data volume and the ability to diagnose real incidents; an example percentage in OpenTelemetry documentation is not a substitute for that judgment.
Quick Recap
Official OpenTelemetry references
- Sampling concepts — decision models, trade-offs, and example decision cues.
- Go SDK sampling — Go sampler behavior and guidance.
- TraceState probability sampling specification — probability decisions and threshold context.
- Collector processor catalog — component packaging and stability information.
- Tail sampling service-criticality demo — an illustrative policy configuration.
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