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.
Analyze dial9 Tokio runtime traces stored in S3 buckets. Use when a user provides an S3 bucket containing dial9 traces and wants to understand runtime behavior, diagnose performance issues, or explore what data is available.
Compile dial9 trace analysis insights into a polished HTML report folder with embedded flamegraphs, timeline strips, and viewer deep-links. Use when you have findings from trace analysis and need to deliver them as something a human can open in a browser.
Zoom into a narrow time window of a dial9 trace to see every worker and OS thread at one moment. Use after an aggregate pass (`analyze.js`, `red_flag_scan.js`) flags a timestamp — a long poll, a queue spike, a latency outlier. Use when the user says "zoom…
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.
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.
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.
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…