Log aggregation data simulation
Skill kjuhwa/skills-hub/skills/workflow/log-aggregation-data-simulation
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Generating realistic synthetic log streams with bursts, level distributions, and source correlation for demo UIs
SKILL.md
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log-aggregation-data-simulation
Realistic log aggregation demos require simulation that matches production statistical shape, not uniform random noise. Generate logs with a Poisson base rate per source (e.g., 5-50 logs/sec/service) multiplied by a diurnal sine envelope (higher daytime volume), then inject burst events: correlated error storms where one upstream service failure triggers elevated error rates in 2-4 downstream services within a 10-60 second window. Level distribution should follow roughly info 70%, debug 15%, warn 10%, error 4%, fatal 1% during normal operation, shifting to error 30-50% during burst windows. Without bursts and correlation, the heatmap looks flat and the stream river looks like static — operators instantly recognize this as fake.
Log message bodies should draw from a templated pool per service ("GET /api/users/{id} 200 {ms}ms", "Connection pool exhausted: {n}/{max}", "JWT expired for user={uid}") with a small set of structured fields (trace_id, span_id, user_id, host) so query-console filters have something meaningful to match. Reuse trace_ids across 3-8 correlated log entries to demonstrate distributed tracing joins. Seed the RNG so demos are reproducible, but expose a "live mode" that streams new entries on a wall-clock interval (50-200ms ticks) with a rolling window (last 10k-100k entries) to avoid unbounded memory. Pre-generate 1-2 hours of historical data on page load so the heatmap has content before the live stream fills it in.