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J. Scott Miller
6c102cc3f3
Adds permission-based license seat counting behind the
`workspace-capable-licensing` experiment. When the experiment is enabled
and a valid license carries the AI Governance add-on, the `user_limit`
feature counts only active users the RBAC engine authorizes to create a
workspace, instead of every active user. Users without workspace-create
capability ("gateway accounts", e.g. AI-Gateway-only users) no longer
consume seats.
## How it works
- A new `GetActiveUsersAuthorizationRoles` bulk query returns effective
roles (implied member roles, org default member roles) and group
memberships for every seat-eligible user (active, not deleted, not
system, not a service account), matching `GetActiveUserCount` semantics.
- `license.CountWorkspaceCapableUsers` evaluates `workspace.create`
against the any-organization object form, which covers site-wide grants,
membership grants, and org-scoped bans in one check. Evaluation is
deduplicated on a sha256 of each user's canonical subject JSON (a fixed
sentinel user ID, sorted deduplicated roles and groups), so cost scales
with unique subjects rather than user count, and every subject field
participates in both the evaluation and the key.
- The AI Governance add-on is only known after license claims are
parsed, so `Entitlements()` passes a lazy `WorkspaceCapableUserCountFn`
(following the `ManagedAgentCountFn` precedent) and
`LicensesEntitlements` resolves it when a validated add-on is present.
Each license's `user_limit` claim becomes a candidate pair of limit and
counting mode, the most favorable pair is selected (see Behavior notes),
and the selected pair's limit, entitlement, and count become the
`user_limit` feature's terms; the warnings read the same values.
`license.Entitlements` gains `logger`, `authorizer`, and `experiments`
parameters.
- All custom roles are prefetched in a single query before evaluation
(new exported `rolestore.PrefetchCustomRoles`), and each count emits one
Info log line (capable count, eligible active users, unique subjects,
elapsed) whose presence identifies the counting mode. The count is
bounded by a 60s timeout.
## Behavior notes
- Without the experiment or without the add-on, the legacy
`GetActiveUserCount` path is unchanged.
- When the mode is active, the over-limit and expired-limit warnings say
"workspace-capable users" instead of "active users", since that is what
was counted.
- With multiple licenses, each license's `user_limit` claim forms a
candidate pair of limit and counting mode (workspace-capable for add-on
licenses, all active users otherwise), and the most favorable pair is
enforced: a pair satisfied by its own count wins over any unsatisfied
one, then higher entitlement, then higher limit. One license's limit is
never combined with another license's counting mode, so a small add-on
license can neither borrow a bigger non-add-on limit nor suppress it.
- Licenses in their grace period still gate the count; it reverts to the
legacy count only on hard expiry. While the add-on exists only on
grace-period licenses, a warning tells admins the counting mode will
revert and states the legacy active-user count they will then be
measured by.
- Count errors (database failures, timeout) abort the entitlements
computation, matching the legacy count's error semantics: the refresh
fails and the caller keeps the previous entitlements rather than a
silently different count. One exception: a stored role string that fails
to parse is logged and treated as not workspace-capable instead of
failing the refresh, since authorization fails closed on such roles
anyway.
- The experiment is deliberately not in `ExperimentsSafe`.
Part of the gateway-accounts feature; no behavior changes for
deployments without the experiment.
## Stack
Part 1 of the gateway-accounts stack. Each PR builds on the previous:
1. **#27279 (this PR)**: permission-based license seat counting. Behind
the `workspace-capable-licensing` experiment and gated on the AI
Governance add-on, `user_limit` counts only users the RBAC engine
authorizes to create workspaces.
2. **#27280**: adds the `organization-ai-gateway-access` org role
carrying the AI Bridge interception permissions (extracted from the
member floors, backfilled into org default roles by migration) and
enforces it at AI Gateway authentication; bridge usage stops claiming AI
Governance seats under the experiment.
3. ~~**#27281**: gates workspace ACL grants on matching member-level
capability (each granted action only takes effect while the recipient
holds that action in the org), so workspace sharing is ineffective for
(and rejected toward) users without workspace capabilities, evaluated
live on every authorization.~~ Tabled — excluded from the
gateway-accounts MVP.
Related but independent: **#27278** hides the Workspaces page create
CTAs for users without workspace-create permission.
## Benchmarks
`BenchmarkCountWorkspaceCapableUsers` (in `usercount_bench_test.go`, run
manually with `go test ./enterprise/coderd/license/ -bench
BenchmarkCountWorkspaceCapableUsers -benchtime 5x -run '^$'` — never
executed by CI) measures the count across user-scale and role-diversity
shapes:
| Scenario | Users | ~Unique subjects | per count |
|---|---|---|---|
| Uniform | 1k | 4 | 8.5ms |
| Uniform | 10k | 4 | 71ms |
| Uniform | 50k | 4 | 344ms |
| ManyOrgs (100 orgs) | 10k | ~200 | 112ms |
| CustomRoles (1000 org-scoped roles) | 10k | ~1000 | 168ms |
| UniquePairs (every user a distinct subject) | 10k | ~10,000 | 2.66s |
Summary:
- **Row-side cost is ~7µs per user, linear** (role parsing, subject
canonicalization, and sha256 per row). The bulk query + subject dedupe
handles 50k users in ~350ms; extrapolated 100k ≈ 0.7s. A non-issue at
the 10-minute refresh cadence.
- **Unique subjects are the dominant axis at ~0.26ms each** (role
expansion + one any-organization rego evaluation per subject). The
worst-case scenario — every user a distinct subject — costs ~2.7s at 10k
users, extrapolating to ~13s at 50k.
- **Realistic deployments sit near the cheap rows.** Subject diversity
tracks orgs × role/group combinations, not user count; only per-user
custom roles or per-user org-membership patterns approach the worst
case.
- Caveat encountered while building the harness: the roles query's plan
depends on accurate table statistics. With stale stats (e.g. right after
a bulk user import, before autovacuum ANALYZEs), the planner picks a
nested-loop plan that re-runs the aggregation per user row — a ~300×
regression (1.08s for 1k users). Fresh statistics restore the hash-join
plan; the harness ANALYZEs after seeding, so the numbers above reflect
the healthy plan.