Tracing Sucks · Cra.mr
Science, Technology & Innovation · Mar 25, 2026
Auto-instrumentation—especially OpenTelemetry in JavaScript—often produces unreliable telemetry that mismatches developers' mental models, so the author largely rejects it in favor of manual tracing and recommends evaluating instrumentation fidelity by stack/framework rather than assuming automation will scale.
Tracing Sucks · Cra.mr
Science, Technology & Innovation · Mar 25, 2026
Distributed tracing often fails in practice because reliably propagating Trace IDs across heterogeneous, hidden abstractions (frameworks, queues, service boundaries) requires invasive instrumentation and subjective fan-out/continuation decisions that produce inconsistent, fragile causal graphs—so trace quality depends more on architectural control than observability vendor choice.
Tracing Sucks · Cra.mr
Science, Technology & Innovation · Mar 25, 2026
The author argues that precise span-based caller accuracy is unnecessary in about 99% of real-world cases and that flattened structured logs with trace context—plus human or LLM investigation for edge cases—are simpler, more usable, and lead to better observability ROI than exhaustive span-capture platforms.
Tracing Sucks · Cra.mr
Business, Finance & Industries · Mar 25, 2026
The author argues that distributed tracing is uniquely costly and complex because sampling—standard cost-control—fails to preserve trace completeness for long-lived, evolving traces, and business-driven selection breaks random-sampling simplicity, creating a persistent cost-versus-value tension that undermines SLAs and customer-tiered observability for long-running or premium workloads.
Tracing Sucks · Cra.mr
Science, Technology & Innovation · Mar 25, 2026
Recommend a logs-first observability approach: attach standardized semantic fields and a trace_id (optionally span_id) to structured logs—flattening context and merging trace context at log time—so teams keep cross-system traceability without collecting full span hierarchies.