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Head-based sampling decides whether to keep a trace near the start of its request; tail-based sampling waits until spans have arrived and can use what happened across the trace. Head sampling is usually simpler and lighter. Tail sampling can prioritize errors, slow requests, and important services, but it needs stateful processing and careful capacity planning.
What is the difference between head-based and tail-based sampling?
The difference is when the decision happens and what evidence is available at that point. OpenTelemetry describes head sampling as making a decision “as early as possible.” In practice, an SDK commonly makes that choice when a span starts. Tail sampling makes the choice downstream, after all or most spans for a trace have arrived, so the sampler can consider outcomes across the request. See the OpenTelemetry sampling concepts.
| Dimension | Head-based | Tail-based |
|---|---|---|
| Decision point | Early, typically when a span starts in an SDK | Downstream, after all or most spans in a trace arrive |
| Information available | Trace ID, parent sampling decision, and data available at span creation | Accumulated trace outcomes and attributes, such as errors or total latency |
| Typical use | Efficiently reduce volume while retaining a representative sample | Keep or sample traces according to outcomes, attributes, or service class |
| Main trade-off | Cannot reliably target an error or latency discovered later | Requires state, compute and memory capacity, monitoring, and reliable trace routing |
How head sampling works
A head sampler chooses at the start, before it knows whether the request will eventually fail or exceed a latency threshold. A common ratio-based approach makes a deterministic choice using the trace ID and a configured probability. The aim is to make a repeatable sample rather than independently make a random choice for every span.
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In distributed tracing, the root span’s sampling decision should generally be propagated to child spans. With parent-based sampling, a child follows the parent’s sampled state instead of making an unrelated decision. This helps preserve coherent traces across services; independent decisions can leave a trace fragmented, with some spans present and others missing. Exact sampler names, defaults, and configuration behavior vary by language SDK. For Go, consult the Go SDK sampling documentation and check the documentation for the SDK you deploy.
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The Go documentation describes samplers including AlwaysSample, NeverSample, TraceIDRatioBased, and ParentBased. Its default tracer provider uses ParentBased with AlwaysSample, and it suggests considering ParentBased with TraceIDRatioBased in production. Those details are specific to the Go SDK documentation; do not assume other languages share the same defaults.
How tail sampling works
A tail sampler receives spans, holds them long enough to group them by trace, and decides whether to retain the trace using the information accumulated so far. Rules can prioritize traces with errors, high latency, selected attributes, or greater service criticality. Because this happens after spans are produced, the decision path must deliver a trace’s spans to the same sampling stage and allow enough time for the relevant spans to arrive.
The OpenTelemetry Collector offers a Tail Sampling Processor. The Collector component catalog lists it as a contrib and Kubernetes distribution component with beta trace support; packaging and stability can change, so verify the target distribution and release in the Collector processor catalog before implementing a configuration. The catalog also lists a Probabilistic Sampling Processor.
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- State and capacity: The processor must retain trace data while waiting to make decisions. Size memory and processing capacity for the actual trace rate, span volume, and decision delay; demo settings are not sizing guidance.
- Routing: Spans from one trace need to reach the same tail-sampling decision point. A deployment that splits a trace across independent samplers may lack the complete context its policies require.
- Decision delay: The sampler waits for spans. A short wait may miss late-arriving spans; a longer wait retains state longer and can increase resource demands.
- Overload and failure behavior: Define what happens when capacity is exceeded or a sampling component is unavailable. Whether data is dropped, delayed, or routed elsewhere affects the risk of missing useful traces.
- Monitoring: Watch processor resource use, incoming and retained trace volume, and whether spans arrive in time for decisions. Revisit capacity and policy as workload changes.
When should I use tail sampling?
Use tail sampling when the signals that matter become known only after a request runs—for example, whether it failed, was unusually slow, or carried an attribute tied to a critical operation. It is most useful when that additional context justifies the stateful processing and routing complexity.
The OpenTelemetry demo configuration illustrates policy choices, not universal production recommendations. Its example samples 100% of traces for critical services, 50% for high-criticality services, 10% for medium-criticality services, and 1% for low-criticality services. It makes error traces eligible regardless of criticality and applies a 5,000 ms slow-trace threshold to critical and high-criticality services. These rates and threshold belong to that demo, not to a general baseline. Its configuration also sets decision_wait: 10s, num_traces: 100000, and expected_new_traces_per_sec: 1000; these are demo values, not sizing recommendations. See the service criticality tail-sampling example.
How to choose a sampling strategy
- Consider no sampling when trace volume is manageable or regulations prohibit dropping data and there is no safe route for retaining unsampled data.
- Choose head sampling when you need straightforward volume reduction and a representative sample is enough. It is efficient, but cannot guarantee retention based on trace-wide outcomes that have not happened yet.
- Choose tail sampling when errors, latency, service class, or other request-level criteria should influence retention and your collection path can support stateful decisions.
- Combine head and tail sampling only when early volume control is necessary and downstream context adds value. Any trace rejected at the head never reaches the tail sampler, so tail rules cannot recover it.
Compare the options against the workload and the consequences of missing data: how representative the resulting sample must be, what the backend can handle, implementation and maintenance effort, compute and memory needs, trace routing, behavior under overload, and whether the system can tolerate gaps.
Sampling is not automatically appropriate just because a system uses OpenTelemetry. The concepts documentation offers 1,000 or more traces per second as one condition for considering sampling, not a universal cutoff; it also says 1% or lower can be representative in high-volume systems, not that it will be representative for every workload. Its listed costs include compute, engineering maintenance, and the opportunity cost of missing critical information. See OpenTelemetry’s sampling guidance when weighing those trade-offs.
Probability sampling and consistent decisions
OpenTelemetry’s probability-sampling specification describes consistent probability decisions using shared randomness and a rejection threshold. It distinguishes SDK parent-and-child decisions from downstream decisions in the collection path. It also describes trace randomness and threshold values carried in TraceState; sampling stages that change the effective threshold must update the encoded threshold to preserve its statistical interpretation. Treat this as specification context, not proof that every installed SDK or Collector release implements every detail identically. Consult the probability-sampling specification alongside documentation for the versions you deploy.
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