Hugh KaznowskiandClaude Sonnet 5
af7a4f1327
`coerce_to_f64` (src/engine.rs) mapped every MetricPrimitive::String to a flat 1.0, so sum()/avg() over a string-kind metric silently became an occurrence count with no signal that the aggregate didn't mean what it looked like it meant. evaluate_promql now looks up the target metric's declared value kind (the kind of its Value-role param, defaulting to Number when it has none) and rejects sum()/avg() with a clear, actionable error when that kind is String. sum(rate(...))/avg(rate(...)) are exempt since rate() only counts occurrences per window and never reads the coerced value. Also add a real count() PromQL aggregation (parser + evaluator) so occurrence-counting is something a user opts into by name, matching the ticket's preferred "count() or numeric metric" error message and the existing sum()/avg()/rate()/histogram()/gauge() aggregation design. Audited existing fixtures/tests for sum()/avg() over string-kind metrics that this would newly reject: none found — the repo's only sum()/avg() usage in tests and docs is over the "requests" occurrence-counter metric, which has no Value-role param and so is Number-kind, unaffected by this change. EngineResponse::Promql already carried Result<Vec<PromqlSeriesDto>, String> for parser errors, so evaluate_promql now returns that same Result and the new error flows through the existing 400 Bad Request path in run_promql_query with no API changes needed. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01XFUNZwCvz3kcod3352ZR2o
ottyl
Observability for any command's terminal output — no logging backend, no agents, no config to ship.
your_app --verbose | ottyl
Pipe a program's stdout into ottyl and turn it into a searchable log timeline, ad-hoc metrics extracted from the log lines themselves, and Grafana-style dashboards — served from a single local binary, for the lifetime of that process:
- Search: A syntax-aware filter bar over your logs — regex matches and metric comparisons like
latency_ms[value]<200, with relative (5h,-3m) and absolute datetimes. - Log timeline: A bucketed bar chart of message volume over time, Kibana-style. Drag across it to set the active time range.
- Metrics from logs: Declare a metric by matching a regex against your log lines and extracting a value. It applies retroactively to everything already ingested, and to everything that follows.
- Dashboards: Bar, line, and pie chart panels driven by PromQL-style queries over your declared metrics.
- Mixed sources: Poll an existing Prometheus/OpenMetrics endpoint alongside log ingestion, in the same dashboard.
- Profiles: Parsing rules and dashboards are saved per app, so they're there again next time that app runs through ottyl.
Use cases
- Debugging a noisy CLI or long-running script, instead of scrolling a terminal buffer.
- Getting metrics and charts out of a program that only ever prints text.
- Local dashboards without standing up a metrics stack.
- Combining scraped Prometheus metrics with log-derived ones during a debugging session.
Get started
Requires Rust and Node.js. The webapp is built and embedded into the binary, so the result is a single self-contained executable.
git clone <this-repo>
cd ottyl
make build
your_app --verbose | ottyl
Or point it at a log file instead of stdin, and mix in a Prometheus/OpenMetrics endpoint:
ottyl --file /var/log/your_app.log --metrics-url http://localhost:9090/metrics
ottyl prints the URL of the local web server it starts (an OS-assigned free port by default; pin one with --port). See ottyl --help for the rest, including where settings and profiles live (--settings-path).
Full documentation — filtering syntax, parameters, metrics, dashboards — is built into the app itself, under the nav menu's docs section.
Development
make dev # frontend dev server with hot reload, proxying to a separately running `cargo run` backend
make test # backend + frontend tests
make lint # clippy + oxlint