windmill/docs/feature-telemetry.md

Feature usage telemetry

feature_usage is the product-telemetry accumulator: day-bucketed counters that roll into the anonymous usage-stats payload. It answers "does anyone use this, and which variant do they pick" without any identifying data leaving the instance.

It currently carries 62 registered actions across twenty-one features (ai_session, ai_chat, ai_fix, ai_agent, ai_decision, ai_agent_eval, app_sandbox, datatable, db_manager, flow_editor, flow_run, flow_step, home, run_form, debugger, trigger, command_script, hub_script, usage_meter, sso_groups_claim, cloud_trial_offer). Nearly all of the product is uninstrumented, so new user-facing work is the opportunity to change that.

When to instrument

Raise it in the plan, with the concrete vocabulary written out, and let the user keep or drop it in one line. Don't stop and ask as a standalone question.

Propose it when a new user-facing affordance leaves a real question open:

• a new panel, mode, tab, toggle, or entry point — is it discovered and used at all?
• competing UX paths, or a new default — which one wins?
• an opt-in or beta gate — what is the take rate?
• a multi-step flow — where do people stop?

Stay silent for bugfixes, refactors, internal plumbing, and anything whose useful signal would need per-item identifiers (paths, names, prompts, code) — those cannot be logged at all, see Privacy rules. If the answer wouldn't change a decision, instrumenting is overkill; say nothing.

Designing the vocabulary

Field	Meaning	Limits
feature	Product area: ai_chat, flow_editor	≤50 chars
kind	The action within it: message, panel_placement. (feature, kind) is the allowlisted pair	≤50 chars
key	A facet of the action — mode, tab kind, tool name, provider:model. Aggregation groups by (feature, kind, key), so this is what splits one counter into comparable buckets	≤100 chars, identifier-shaped, optional
entity_id	An opaque random id (e.g. a session id) when you need per-entity distributions rather than a flat count	≤50 chars, identifier-shaped, optional
value	Increment, default 1	clamped to 1…1,000,000

Identifier-shaped means ASCII alphanumerics plus _ - : . / — no spaces. Anything else is rejected.

Supplying entity_id is what unlocks the distribution stats: the payload reports entity_count, total_value, median_value, p90_value, and inactive_3d_entity_count per (feature, kind, key). Omit it for a plain "how many times did this happen" counter. Keep the key vocabulary closed and small — enumerate the values in a TS union next to the call site, the way flowEditorTelemetry.ts does, so the whole set is reviewable in one place.

The recipe

Four steps. Skipping step 1 fails quietly.

1. Register the pair in FEATURE_USAGE_KINDS (backend/windmill-common/src/feature_usage_ee.rs, tracked in windmill-ee-private). An unregistered (feature, kind) is dropped by is_recordable_event with a bare continue — no error, no log, still a 204 to the browser. Frontend-only instrumentation records nothing and looks like it worked.

2. Log from the frontend:

import { logFeatureUsage } from '$lib/utils/featureUsage'

logFeatureUsage('flow_editor', 'panel_placement', { key: 'force_detach' })

Fire-and-forget. Events sum locally per (workspace, feature, kind, key, entityId) and flush every 30s, on visibilitychange → hidden, and on pagehide; 50 events per request, and a failed batch is dropped rather than retried.

3. Check the disclosure. InstanceSettings.svelte describes a non-minimal payload by category, not counter by counter (two places — the copy appears twice); keep it short. A plain counter needs no edit. Only a key that sends a new kind of value beyond a count — a name or an identifier, like the AI model and hub names it already lists — must be added there.

4. Verify a row lands. The silent-drop path means "no error" proves nothing:

SELECT feature, kind, key, entity_id, day, value FROM feature_usage ORDER BY updated_at DESC LIMIT 10;

Collection sits behind the private feature, so a public build records nothing from either the HTTP route or the Rust helper. Run the backend with --features enterprise,private or this query stays empty however correct the instrumentation is.

Privacy rules

Only aggregated counts ever leave the instance, and only when telemetry is enabled and minimal mode is off. Never put a path, prompt, script body, workspace name, email, or any user identifier into key or entity_id. Entity ids must be opaque random ids, never anything that maps back to a user or a resource. If the signal you want can only be expressed with identifying data, it cannot be collected — drop it.

Counters aggregate over the last 30 days; rows are pruned after 60.

Logging from the backend

A feature with no UI is instrumented the same way, from Rust:

windmill_common::feature_usage::log_feature_usage("trigger", "fired", kind.as_str());

Same registry, same key rules, and the same silent drop when the pair is unregistered. feature and kind are &'static str so a call site cannot pass a computed pair. The call increments an in-memory counter and returns; the monitor loop flushes the accumulator, so it is cheap enough for hot paths — but only cheap per call, not free: a key with unbounded cardinality would grow the map until it hits the per-action cap and starts dropping new keys.

There is no entity_id and no explicit value on this path: it counts occurrences.

feature_usage_ee holds the registry and the writer; the public build gets the inert feature_usage_oss, since a CE instance never sends a stats payload.