JavaScript analysis toolkit for parsing and analyzing dial9 Tokio runtime traces. Always start trace diagnosis with analyzeTraces() from analyze.js, then use parseTrace() and lower-level helpers only to confirm assumptions or drill into raw events.
Analysis pipeline API for dial9 traces. Covers analyzeTraces() aggregation, buildWorkerSpans, attachCpuSamples, scheduling delays, flamegraphs, span data, and the full return schema. Use when analyzing parsed traces or building custom analysis pipelines.
Parse and load dial9 Tokio runtime trace files. Covers the ParsedTrace schema, event types, field definitions, parse options, time filtering, symbol resolution, and timestamp conversion. Use when loading traces or understanding the trace data model.
Diagnostic recipes for common questions about dial9 Tokio runtime traces. Covers finding long polls, task leaks, worker utilization, blocking calls, wake chains, span analysis, task dumps, time-window debugging, and estimating allocation totals from sampled `Alloc` events. Use when answering specific diagnostic questions about trace data.
Automated health checks for dial9 Tokio runtime traces. Detects long polls, task leaks, scheduling delays, blocking calls, queue buildup, worker imbalance, CPU contention, and span anomalies. Use when you want a quick automated assessment of trace health.
Tokio async runtime internals reference. Covers the execution model, waking and scheduling, cooperative scheduling, poll duration effects on tail latency, worker parking, and how to connect trace data to application behavior. Use when reasoning about runtime performance from first principles.