Sampling Strategies for Traces
Understand why traces are sampled, the difference between head-based and tail-based sampling, and how to balance visibility against cost.
Sampling Strategies for Traces is a free System Observability: Logging, Metrics & Tracing (ELK + OpenTelemetry) lesson on CoddyKit — lesson 4 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the System Observability: Logging, Metrics & Tracing (ELK + OpenTelemetry) learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
Why Sample Traces?
Capturing every trace in a busy system produces enormous data volumes. Sampling keeps a representative subset to control storage and processing cost while preserving useful insight.
The Cost of Full Tracing
A service handling thousands of requests per second can emit millions of spans per minute. Storing all of them is expensive and rarely necessary for diagnosis.
- Network overhead
- Backend storage
- Query latency
Head-Based Sampling
Head-based sampling decides at the start of a trace whether to keep it, before the outcome is known. It is cheap and simple.
sampler: traceidratio
ratio: 0.10 // keep 10% of tracesProbabilistic Sampling
A common head-based form keeps a fixed percentage. The decision is made on the trace ID so all spans in a trace agree.
if hash(trace_id) % 100 < 10:
keep()
else:
drop()Tail-Based Sampling
Tail-based sampling waits until a trace finishes, then decides using the full picture. It can prioritize errors and slow requests.
if trace.has_error or trace.duration > 2s:
keep()
else:
sample(0.05)Trade-Offs
Each approach has costs.
- Head-based: cheap, but may drop the rare error you needed
- Tail-based: keeps interesting traces, but buffers spans and uses more memory
Consistent Sampling
The sampling decision must be consistent across services so a trace is kept whole, not in fragments. The decision propagates via the trace context.
traceparent: 00-<trace-id>-<span-id>-01
// the 01 flag marks the trace as sampledRate Limiting
Rate-limiting samplers cap traces per second, protecting the backend during traffic spikes regardless of percentage.
sampler: rate_limiting
max_traces_per_second: 100Sampling in the Collector
The OpenTelemetry Collector can apply tail sampling centrally, freeing apps from the decision.
processors:
tail_sampling:
policies:
- name: errors
type: status_code
status_codes: [ERROR]Choosing a Strategy
Start with head-based probabilistic sampling for simplicity. Move to tail-based when you must guarantee that errors and slow traces are always captured.
Always Keep the Important
Combine strategies: sample normal traffic lightly but keep 100% of errors and high-latency traces. This maximizes signal per stored byte.
Quick Check
Pick the strategy that guarantees error traces are kept.
Recap
You learned why traces are sampled, how head-based sampling decides up front cheaply while tail-based waits for the full trace to keep errors and slow requests, and that decisions must propagate consistently. Combining light sampling of normal traffic with full capture of important traces gives the best signal for the cost.
Frequently asked questions
Is the “Sampling Strategies for Traces” lesson free?
Yes — the full text of “Sampling Strategies for Traces” is free to read here on the web, and the System Observability: Logging, Metrics & Tracing (ELK + OpenTelemetry) course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the System Observability: Logging, Metrics & Tracing (ELK + OpenTelemetry) course, upgrade to CoddyKit PRO.
What will I learn in “Sampling Strategies for Traces”?
Understand why traces are sampled, the difference between head-based and tail-based sampling, and how to balance visibility against cost. You practise System Observability: Logging, Metrics & Tracing (ELK + OpenTelemetry) with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start System Observability: Logging, Metrics & Tracing (ELK + OpenTelemetry)?
No prior experience is required. System Observability: Logging, Metrics & Tracing (ELK + OpenTelemetry) on CoddyKit is structured for beginners through advanced learners; this is — lesson 4 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Sampling Strategies for Traces” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this System Observability: Logging, Metrics & Tracing (ELK + OpenTelemetry) lesson?
Yes. Every System Observability: Logging, Metrics & Tracing (ELK + OpenTelemetry) lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- Understanding Trace Spans and IDs
- How Distributed Tracing Works
- Tracing vs. Logging vs. Metrics
- Sampling Strategies for Traces