Build the safeguards
Eight explained steps, a source example and a checkpoint at each decision.
Read the tutorialOpenTelemetry / Observability / SLOs
The server is healthy. Customers are waiting.
Every request returns successfully. One takes much longer than the others. Follow its spans, find the missing context and choose a signal that reflects the customer’s wait.
The idea in plain language
A successful response can still be a poor experience. Metrics tell you how often that happens; a trace helps explain where a particular request spent its time.
The worker creates real OpenTelemetry spans around actual timer waits. The database is a timed fixture. Eight requests teach latency arithmetic, not a production SLO window. A local HTTP service and optional Prometheus/Grafana recipe accompany the download.
Your independent testing environment
Make the failure visible.
Run the slow-query example. Inspect the mean, p95 and under-100-ms fraction. In the safeguarded trace, the database span shares the customer request’s trace ID instead of starting an unrelated root.
Compare a green availability signal with the slow request’s trace and latency.
Ready. Choose an example and run both implementations.
Explain the result
Both versions return eight successful responses. The safeguarded trace contains the request, service and database spans under one trace ID. A long tail remains visible even though availability is 100%.
Open the exact source behind this edition. The viewer checks each file against its SHA-256 manifest and explains the decision it implements.
Choose your next step
Compare the alternatives
Understand where each pattern helps and which tradeoffs remain.
Compare approachesRun and extend it locally
Download the independent environment, run the native checks and try a specific extension.
Open the how-to guideBefore you try it
Do I need to know how to code?
Start with Run and read the comparison. The tutorial introduces the code after you have seen the behavior. The how-to guide separates a no-install route from the native developer exercise.
What happens to the experiment when I reset?
Reset discards this lab’s temporary state and cancels or terminates its pending work. These examples use invented records. No account, production data or cloud deployment is needed.
What does the browser experiment establish?
The worker creates real OpenTelemetry spans around actual timer waits. The database is a timed fixture. Eight requests teach latency arithmetic, not a production SLO window. A local HTTP service and optional Prometheus/Grafana recipe accompany the download.
Lab discussion / Trace the Wait Lab
What would you try next?
Ask about the code, compare an approach, or describe an edge case. Reader comments are reviewed before publication.
Code walkthrough
Follow the implementation in code.
These short excerpts connect the lab’s behavior to its implementation. Each example links to the full tutorial step for its context, tradeoffs and checkpoint. Names and dependencies belong to that source; the excerpts are not standalone programs.
Start with the customer’s operation
The root span describes GET /report, not an internal implementation detail. Its duration represents the operation the visitor experiences in this fixture. A health endpoint checks a different question and cannot replace a user-facing indicator.
const root = tracer.startSpan("GET /report", {}, ROOT_CONTEXT);Create spans with useful boundaries
The service and database-fixture spans surround work that can explain the request. Their names remain bounded and understandable. Attributes describe the operation without embedding arbitrary customer data. Too many tiny spans can create overhead without helping a diagnosis.
tracer.startSpan("db.query orders", {}, parentContext);Carry parent context explicitly
The broken database span uses ROOT_CONTEXT and becomes an independent trace. The safeguarded path places the service span in the supplied context before creating the child. A real network boundary additionally needs compatible injection and extraction of trace headers.
const childContext = trace.setSpan(parentContext, serviceSpan);Inspect the distribution, not just its mean
The example uses nearest-rank p95 on eight recorded durations. One long sample can dominate the tail while barely moving an average. A tiny fixture makes this arithmetic visible; production percentile estimates depend on aggregation methods, histogram buckets and traffic volume.
sorted[Math.ceil(0.95 * sorted.length) - 1]Relate a target to an error budget
A target states the permitted fraction of bad eligible events over a specified window. The lab’s illustrative target is 90% under 100 ms. Real alerts need a window, minimum useful volume and burn-rate reasoning rather than an alarm on every slow request.
budgetFraction = 1 - target;
consumedFraction = badEvents / eligibleEvents;