Trace: the end-to-end view of one request. When a user clicks "Buy", a trace records: API gateway (10ms), Order service (50ms), Inventory service (30ms), Payment service (200ms), Notification service (20ms) — with their relationships (parent-child), timing, and outcomes. Components: Trace: the entire DAG of spans for one request. Identified by trace ID (16 bytes, globally unique). Span: a named, timed operation. Has: span ID, parent span ID, start time, duration, status (OK/ERROR), attributes (key-value). Root span: the first span, with no parent. Spans form a tree showing the call hierarchy. OpenTelemetry: the CNCF standard for trace instrumentation. SDK auto-instruments HTTP, gRPC, DB calls in many frameworks.
2 / 5
What is context propagation in distributed tracing?
Context propagation: the mechanism that ties together all the spans from different services into one trace. Standards: W3C Trace Context (RFC 9209): traceparent: 00-4bf92f3577b34da6a3ce929d0e0e4736-00f067aa0ba902b7-01 format: version-traceID-parentSpanID-flags. Now the recommended standard. B3 (Zipkin): X-B3-TraceId, X-B3-SpanId, X-B3-Sampled headers. Still widely used. Application responsibility: HTTP frameworks auto-inject/extract headers (if instrumented). For async (Kafka messages, SQS), embed trace context in message headers. The critical failure mode: a service receives traceparent, processes the request, makes downstream calls — but forgets to forward the header. The trace breaks. The downstream spans have a new trace ID. Root cause: missing header propagation in one service.
3 / 5
What is sampling in distributed tracing and why is it necessary?
Sampling: at 10,000 RPS recording every trace produces 864M traces/day. Impossible to store and process. Sampling strategies: Head-based (probabilistic): decision made at request entry. 1% or 5% of requests traced. Simple. Misses rare errors. Tail-based: collect all spans temporarily; after the request completes, decide to keep it based on outcome (error, slow). Captures 100% of errors even at 0.1% base sampling. More complex — requires buffering. Rate limiting: keep N traces/second regardless of load. Adaptive: lower sampling during high load. OpenTelemetry sampler config: ParentBased (respect upstream sampling decision), TraceIdRatioBased (probabilistic), AlwaysOn/AlwaysOff. Honeycomb, Lightstep: support tail-based sampling. Jaeger: configurable sampling per service.
4 / 5
What are span attributes and what should they contain?
Span attributes: OpenTelemetry defines semantic conventions for common attributes. HTTP spans: http.method (GET/POST), http.url, http.status_code, http.request_content_length. DB spans: db.system (postgresql), db.statement (the SQL query — careful with PII), db.name. RPC spans: rpc.system (grpc), rpc.service, rpc.method. Custom attributes: user.id, tenant.id, feature_flag.key, order.id. Events: time-stamped messages within a span (e.g., "cache miss", "retry attempt 2"). Status: OK, ERROR (+ description). Links: span can reference other spans (for async, fan-out patterns). High-cardinality attributes (user ID, order ID) enable finding the trace for a specific user complaint.
5 / 5
What is the difference between distributed tracing, metrics, and logs in observability (the "three pillars")?
Three pillars (observability signals): Metrics: aggregated numeric measurements over time. Cheap to store (just numbers). Great for: alerting (error rate > 1%), trending, dashboards. Cannot answer "why did this specific request fail?". Examples: request_duration_seconds (histogram), error_rate_total (counter), active_connections (gauge). Prometheus/Grafana. Logs: discrete event records with context. Expensive at scale. Great for: debugging specific events, audit trails. Cannot show causality across services. Examples: JSON logs with trace ID, request ID, user ID. Elasticsearch/Loki. Traces: causal request flow across services. Can show which downstream call is slow. Cannot show aggregate patterns. Best used to debug specific slow/failed requests identified via metrics. The key: use metrics to find the problem, traces to understand the causal chain, logs to see the details at each step. OpenTelemetry unifies collection of all three.
What does the "Distributed Tracing" vocabulary exercise cover?
This exercise tests real IT vocabulary related to distributed tracing through 5 multiple-choice questions, each built from realistic workplace sentences rather than abstract definitions.
Is this vocabulary exercise free to use?
Yes. Every exercise on CoderSlingo, including this one, is completely free — no account, sign-up, or payment required.
How many questions does this exercise have?
This exercise has 5 questions. Each one shows a real-world sentence or scenario with multiple-choice options and an explanation once you answer.
What happens after I answer a question?
You'll see immediate feedback showing whether your answer was correct, along with a short explanation of why — then a button to move to the next question, and a full results screen at the end.
Can I retry the exercise if I get questions wrong?
Yes. Once you reach the results screen, click "Try again" to reset your answers and go through the exercise from the start as many times as you like.
Do I need to create an account to take this exercise?
No account is needed. Your answers are scored in your browser during the session — nothing is saved to a server, so you can jump straight in.
Is my progress saved if I leave the page?
No — progress within an exercise resets if you navigate away or reload. Each exercise is short enough to complete in a few minutes in one sitting.
Are these vocabulary exercises connected to other topics?
Yes — this module shares real-world context with 2 other vocabulary modules. See "Related vocabulary" below to keep building a connected skill set.
How is this different from reading a glossary or blog article?
Exercises like this one are active recall drills — you have to choose the correct term or phrasing yourself, which builds retention faster than passively reading a definition.
Where can I find more vocabulary exercises?
Browse the full Vocabulary exercises hub for hundreds of modules covering Agile, DevOps, security, databases, architecture, and more — organised by IT role and skill.