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triggerdotdev--trigger.dev/references/hello-world
Eric Allam 469b039090 feat: OTEL metrics pipeline for task workers (#3061)
- Adds an end-to-end OTEL metrics pipeline: task workers collect and
export metrics via OpenTelemetry, the webapp ingests them into
ClickHouse, and they're queryable through the existing dashboard query
engine
- Workers emit process CPU/memory metrics (via
`@opentelemetry/host-metrics`) and Node.js runtime metrics (event loop
utilization, event loop delay, heap usage)
- Users can create custom metrics in their tasks via
`otel.metrics.getMeter()` from `@trigger.dev/sdk`
- Metrics are automatically tagged with run context (run ID, task slug,
machine, worker version) so they can be sliced per-run, per-task, or
per-machine
- The TSQL query engine gains metrics table support with typed attribute
columns, `prettyFormat()` for human-readable values, and per-schema time
bucket thresholds
- Includes reference tasks
(`references/hello-world/src/trigger/metrics.ts`) demonstrating
CPU-intensive, memory-ramp, bursty workload, and custom metrics patterns

## What changed

### Metrics collection (packages/core, packages/cli-v3)
- **Metrics export pipeline** — `TracingSDK` now sets up a
`MeterProvider` with a `PeriodicExportingMetricReader` that chains
through `TaskContextMetricExporter` (adds run context attributes) and
`BufferingMetricExporter` (batches exports to reduce overhead)
- **Host metrics** — Enabled `@opentelemetry/host-metrics` for process
CPU, memory, and system-level metrics
- **Node.js runtime metrics** — New `nodejsRuntimeMetrics.ts` module
using `performance.eventLoopUtilization()`, `monitorEventLoopDelay()`,
and `process.memoryUsage()` to emit 6 observable gauges
- File system and diskio metrics
- **Custom metrics** — Exposed `otel.metrics` from `@trigger.dev/sdk` so
users can create counters, histograms, and gauges in their tasks
- **Machine ID** — Stable per-worker machine identifier for grouping
metrics
- **Dev worker** — Drops `system.*` metrics to reduce noise, keeps
sending metrics between runs in warm workers

### Metrics ingestion (apps/webapp)
- **OTEL endpoint** — `otel.v1.metrics.ts` accepts OTEL metric export
requests (JSON and protobuf), converts to ClickHouse rows
- **ClickHouse schema** — `017_create_metrics_v1.sql` with 10-second
aggregation buckets, JSON attributes column, 60-day TTLs

### Query engine (internal-packages/tsql, apps/webapp)
- **Metrics query schema** — Typed columns for metric attributes
(`task_identifier`, `run_id`, `machine_name`, `worker_version`, etc.)
extracted from the JSON attributes column
- **`prettyFormat()`** — TSQL function that annotates columns with
format hints (`bytes`, `percent`, `durationSeconds`) for frontend
rendering without changing the underlying data
- **Per-schema time buckets** — Different tables can define their own
time bucket thresholds (metrics uses tighter intervals than runs)
- **AI query integration** — The AI query service knows about the
metrics table and can generate metric queries
- **Chart improvements** — Better formatting for byte values,
percentages, and durations in charts and tables

### Reference project
- **`references/hello-world/src/trigger/metrics.ts`** — 6 example tasks:
`cpu-intensive`, `memory-ramp`, `bursty-workload`, `sustained-workload`,
`concurrent-load`, `custom-metrics`

## Test plan

- [ ] Build all packages and webapp
- [ ] Start dev worker with hello-world reference project
- [ ] Run `cpu-intensive`, `memory-ramp`, and `custom-metrics` tasks
- [ ] Verify metrics in ClickHouse: `SELECT DISTINCT metric_name FROM
metrics_v1`
- [ ] Query via dashboard AI: "show me CPU utilization over time"
- [ ] Verify `prettyFormat` renders correctly in chart tooltips and
table cells
- [ ] Confirm dev worker drops `system.*` metrics but keeps `process.*`
and `nodejs.*`
2026-02-20 13:16:34 +00:00
..
2024-09-05 12:56:23 +01:00