docs-live
704 Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
 Eric Allam
|
c0b84595a3 |
feat(webapp): hosted webhook ingress, delivery pipeline, and dashboard (#4344)
## Summary The server half of hosted webhooks: the public ingress endpoint, signature verification, the delivery pipeline (Postgres partitioned storage + ClickHouse for ordering), the in-app partition manager, the HTTP API, and the dashboard (Deliveries, Endpoints, and the in-app test console). The public SDK and docs half is #4537. That PR carries the user-facing API (`webhook()`, `chat.event` / `chat.channels`, the `@trigger.dev/slack` connector) and builds on the shared `@trigger.dev/core` schemas that ship here. ## Shipping behind a flag A `WEBHOOK_ENABLED` env var (default off) gates the public ingress route and the engine worker plus partition cron, so merging and deploying this changes nothing in production until it is flipped on per environment. The dashboard is separately gated per org by the `hasWebhooksAccess` feature flag. ## Note on packages This PR includes the `@trigger.dev/core` schema additions the server compiles against, but carries no changeset. Core is not consumed independently of the SDK, so it is released together with the SDK via #4537. Keeping its changeset off `main` means no release cut from `main` publishes it early. |
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Eric Allam
|
b98dd79fe4 |
feat(webapp,run-store,database): env-configurable transaction resilience (maxWait + tx-start retry) (#4623)
## What
Makes two transaction-resilience behaviors real and env-var
configurable, defaults set to the good values, so we can tune during and
after the Aug 15 database patch window without a redeploy:
- **maxWait 2s → 10s** (TRI-12982): how long Prisma waits to borrow a
connection before it can `BEGIN`. A restart freeze holds the pool full,
and the only thing that errored was transaction starts giving up at 2s.
- **Retry transaction-start P2028-at-acquisition** (TRI-12984): when
Prisma can't borrow a connection within `maxWait` it raises P2028
(`Unable to start a transaction in the given time`) and **no SQL ran**,
so retrying is safe. Scoped narrowly: only that error (never P2024
pool-exhaustion), 2 attempts, jittered backoff, and a token-bucket
budget so a mass freeze can't amplify into a retry storm.
## Env vars (`DATABASE_*` convention)
Generic defaults:
| var | default |
|---|---|
| `DATABASE_TRANSACTION_MAX_WAIT_MS` | `10000` |
| `DATABASE_TRANSACTION_START_RETRY_ENABLED` | `true` (kill switch) |
| `DATABASE_TRANSACTION_START_RETRY_MAX_ATTEMPTS` | `2` |
| `DATABASE_TRANSACTION_START_RETRY_BACKOFF_MIN_MS` | `50` |
| `DATABASE_TRANSACTION_START_RETRY_BACKOFF_MAX_MS` | `250` |
| `DATABASE_TRANSACTION_START_RETRY_BUDGET_PER_SEC` | `50` |
| `DATABASE_TRANSACTION_START_RETRY_BUDGET_BURST` | `100` |
Per-writer-pool overrides, each falling back to the generic when unset
(same pattern as the per-client pool/connect-timeout work):
`RUN_OPS_DATABASE_TRANSACTION_*` and
`RUN_OPS_LEGACY_DATABASE_TRANSACTION_*` (all 7 knobs each). Transactions
only open on writer pools, so those are the only pools with their own
knobs. Each pool gets its **own** token bucket, so a storm on one pool
can't drain another's retry budget.
## Design
- The retry primitives live in `internal-packages/database` and never
read `process.env` (IoC): a P2028-at-acquisition classifier, a
`TokenBucketRetryBudget`, and `withTransactionStartRetry`, folded into
the `$transaction` helper via a new `startRetry` option. Config is
resolved at the app boundary and threaded in.
- The `$transaction` helper is the chokepoint (wraps the whole
transaction), not the per-statement `$allOperations` extension.
- The run engine's writes go through `PostgresRunStore`'s own
`.$transaction(...)`, not the webapp helper, so both the helper and the
two `PostgresRunStore` sites apply maxWait + retry (sharing the per-pool
config). Builds on the `options?: { timeout, maxWait }` seam added in
#4514.
- Webapp `$transaction` call sites get the default `maxWait` + retry
injected at one merge point, so no call site needed editing.
## Evidence
- Unit red/green in `internal-packages/database`: reverting the helper
wiring turned the acquisition-retry test red (`Unable to start a
transaction in the given time`), re-applying it green. Full package
suite 25/25. Covers: classifier (P2028-acq yes, P2024 no, in-tx P2028
no), retry (retry-then-succeed, no-retry P2024, stop at maxAttempts,
disabled, budget-exhausted, jitter bounds), token bucket, and
`$transaction` wiring.
- Typecheck clean: webapp, run-store, run-engine.
- Full-stack run: bounded queue-ay pass (15 projects, real dev runs
through the run-engine `PostgresRunStore` transaction path). 13 pass;
the 2 failures are one documented known-failure and one
stale-worker-state flake that passes 2/2 with this change active on a
fresh app.
- Boots cleanly with per-pool overrides set.
## Configuration & rollout
Ship **inert** first (zero behavior change), then flip to the good
values **live via env** — no redeploy needed for either.
### Inert — behaves exactly as today
```
DATABASE_TRANSACTION_MAX_WAIT_MS=2000 # Prisma's built-in default (change defaults to 10000)
DATABASE_TRANSACTION_START_RETRY_ENABLED=false # disable the new retry entirely
```
`maxWait=2000` is what every path used before (Prisma's default; the
run-store sites and the helper passed no maxWait). `retry=false`
short-circuits `withTransactionStartRetry` to a single run and makes the
serialization-retry exclusion a no-op. Verified on the pooler-freeze
rig: identical fail-fast P2028 at ~2003ms with zero retries —
byte-for-byte current behavior, across all pools.
### Production ("good") — the baked defaults
Rely on defaults (nothing to set) or set explicitly:
```
DATABASE_TRANSACTION_MAX_WAIT_MS=10000
DATABASE_TRANSACTION_START_RETRY_ENABLED=true
DATABASE_TRANSACTION_START_RETRY_MAX_ATTEMPTS=3 # 3 attempts (2 retries); ~30s acquisition tolerance covers a ~20-25s freeze
DATABASE_TRANSACTION_START_RETRY_BACKOFF_MIN_MS=50
DATABASE_TRANSACTION_START_RETRY_BACKOFF_MAX_MS=250
DATABASE_TRANSACTION_START_RETRY_BUDGET_PER_SEC=50
DATABASE_TRANSACTION_START_RETRY_BUDGET_BURST=100
```
Per-pool overrides `RUN_OPS_DATABASE_TRANSACTION_*` and
`RUN_OPS_LEGACY_DATABASE_TRANSACTION_*` (all seven knobs each) are
optional and fall back to the generic set — not needed for v1; the
generic set covers the control-plane, run-ops, and run-ops-legacy writer
pools. Readers open no transactions and take nothing.
**Guardrail:** the retry only engages when a pool's `pool_timeout` >
`maxWait`. Prod is fine (`DATABASE_POOL_TIMEOUT=60` >> 10). Do not set
any writer pool's `pool_timeout` at or under `maxWait`, or saturation
failures flip from retryable P2028 to non-retryable P2024 and the retry
silently stops helping.
### Rollback
Env flip (set inert) or revert. Retry only fires where no SQL ran, and
the per-pool token bucket caps a storm. No migration.
refs TRI-13295, TRI-12982, TRI-12984
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claude[bot]
|
69f396fbef |
fix(webapp): keep paused environments paused when concurrency limits are pushed (#4625)
<!-- ccr-slack-attribution --> _Requested by **Matt Aitken** · [Slack thread](https://triggerdotdev.slack.com/archives/C045W9WM3E1/p1786732623292829?thread_ts=1786732623.292829&cid=C045W9WM3E1)_ **Before:** you pause an environment, then a deploy lands (or a background worker is created, or an admin changes the concurrency/burst-factor). The environment starts picking up runs again even though the dashboard still shows it as paused. **After:** a paused environment stays paused until it is resumed, no matter what else pushes its concurrency limit. Pausing an environment sets `paused` in the database and writes a `0` env concurrency limit into the run queue — the `0` is the only thing that actually stops dequeueing. Any caller that pushed the limit without an explicit value (`finalizeDeployment`, `createBackgroundWorker`, the two admin environment routes) rewrote the real limit and silently un-paused the environment. ## ✅ Checklist - [x] I have followed every step in the [contributing guide](https://github.com/triggerdotdev/trigger.dev/blob/main/CONTRIBUTING.md) - [x] The PR title follows the convention. - [x] I ran and tested the code works --- ## Testing `apps/webapp/test/pauseEnvironment.server.test.ts` gains two `containerTest` cases that wire a real `RunEngine` (real Redis) in place of the stubbed app singleton and assert the actual run-queue env limit: - pause a PRODUCTION env → limit is `0` → run the real `FinalizeDeploymentService` → limit is still `0`, plus a control on a running env in the same test proving that deploy path really does push the limit (so the `0` can't just mean "nothing happened"). - pause → resume → the real limit is restored, so the clamp can't regress resuming. Both cases fail on `main` (`expected 17 to be +0` and `expected +0 to be 17`) and pass with this change. `pnpm run typecheck --filter webapp` is clean. --- ## Changelog Fix paused environments starting to run work again after a deploy. --- ## How The clamp lives in the shared `updateEnvConcurrencyLimits` helper in `apps/webapp/app/v3/runQueue.server.ts`, so every present and future caller is covered: when no explicit limit is passed and the environment is paused, `0` is written instead of the stored maximum. An explicitly-passed limit still wins, which is what pausing itself relies on. The resume path now passes the post-update environment state (its in-memory copy was read before the un-pause and would otherwise be clamped back to `0`), and the helper no longer mutates the caller's environment object — that aliasing made a pause followed by a resume on the same object write `0` twice. The existing `!paused` guards in `allocateConcurrency` and the queue-level guard in `createBackgroundWorker` are left in place as defence in depth, and queue-level `TaskQueue.paused` behaviour is untouched. --------- Co-authored-by: Claude <noreply@anthropic.com> |
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|
Eric Allam
|
dc8f90e66e |
fix(run-engine,webapp): resolve dequeue worker version fresh per task (#4622)
## Summary After a deployment promotion or rollback, newly triggered runs could keep dispatching onto the previously deployed version for up to 30 seconds. Runs now resolve the current version fresh on every dequeue, so a promotion or rollback takes effect immediately. ## Fix The dequeue path resolved the worker version through a 30s in-process cache that nothing invalidated on promotion, and it loaded the worker's entire task and queue set only to keep the single row matching the run. Both go away: the resolve now fetches just the matched task and queue by unique index and reads them fresh, so there is no cache left to serve a stale version. ``` - cache.get(env:current) # 30s TTL, never invalidated -> stale - worker + ALL tasks + ALL queues + worker + one task WHERE slug=... + one queue WHERE id/name=... # fresh ``` A kill-switch env var (`RUN_OPS_WORKER_VERSION_FRESH_READ_ENABLED`, default on) falls back to the old cached path without a code deploy. Verified end-to-end on an isolated stack: a run triggered after a mid-stream promotion now dequeues onto the new version, with the previous stale behavior reproduced first. |
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Eric Allam
|
dd78dd92ee |
perf(webapp): select only needed columns in dev current-worker lookup (#4621)
## Summary When resolving the current worker for a development environment, `findCurrentWorkerFromEnvironment` loaded the entire `BackgroundWorker` row, including the large `metadata` JSON, even though it only ever returns a handful of small fields. It is a frequently-run query, so the wasted payload adds up: every call pulled data it immediately threw away. ## Fix Add a `select` to the development-environment lookup listing exactly the fields the function returns (`id`, `friendlyId`, `version`, `sdkVersion`, `cliVersion`, `supportsLazyAttempts`, `engine`). The query plan is unchanged, still a single-row indexed lookup; only the row width shrinks. No behavior change: the dropped columns were never read. |
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Eric Allam
|
8dc8e1b58b |
perf(run-engine,webapp): narrow the control-plane worker-version read to the columns dequeue uses (#4619)
## Summary
The worker-version resolve path fetched every column of every
`BackgroundWorkerTask` for a worker (`include: { tasks: true }`), plus
full `WorkerDeployment` and `TaskQueue` rows, just to match one task at
dequeue. That pulls large JSON columns none of this path reads (task
`payloadSchema`/`config`/`queueConfig`/`description`, deployment
`externalBuildData`/`buildServerMetadata`/`errorData`/`git`, queue
`rateLimit`), so each resolve transfers and deserializes far more than
it uses.
## Fix
Replace the includes with explicit `select`s of only the columns dequeue
reads, in both the passthrough resolver and the app resolver:
- task: `id`, `slug`, `machineConfig`, `retryConfig`,
`maxDurationInSeconds`
- deployment: `id`, `friendlyId`, `imageReference`, `imagePlatform`
- queue: `id`, `name` (the queue matcher keys on both)
The shared `ResolvedWorkerVersion` element types narrow to match
(mirrored in the cache), which also shrinks each cached worker-version
entry.
## Impact
The `tasks` read fetches every task of a worker to match one, so its
cost scales with task count and payload-schema size. For a worker with
~70 registered tasks, dropping the unread columns cuts the per-query
transfer roughly:
| Task shape | Before | After | Reduction |
|---|---|---|---|
| Light (no payload schema, small config) | ~28 KB | ~14 KB | ~54% |
| Typical (mixed schemas / config) | ~62 KB | ~14 KB | ~77% |
| Schema-heavy (large `payloadSchema`) | ~200 KB | ~14 KB | ~93% |
The `after` size is roughly fixed because the kept columns are small;
the win grows with how heavy the dropped JSON is. Narrowing `deployment`
(four JSON columns off a single row) and `queues` saves further on top.
No behavior change: pure read-shape narrowing, no flag and no schema
change, so rollback is a plain revert. Verified with a red/green
run-engine test that asserts the resolved task, deployment, and queue
carry only the used columns, plus the queue feature-matrix runs (batch,
retry-policy, machine-preset, plain trigger) that exercise the kept
columns.
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Chris Arderne
|
3e7964e7fa |
feat: surface cron windows in webapp, cli, sdk (#4572)
## Summary Adds execution-window product surfaces for both declarative and imperative schedules. - Declarative schedules can set `window` through `schedules.task()`, with support for whole-minute, hour, and percentage values. - Imperative schedules can create, update, clear, and inspect windows through the API and dashboard. - Schedule API responses preserve `nextRun` as the nominal CRON time and expose `nextRunEffectiveAt` as the stable assigned time. - The dashboard displays configured windows alongside assigned upcoming-run times. - Deploy output summarizes declarative schedules and suggests adding a wider window when the default 60-second placement range is used. ## Design Window validation remains authoritative on the server and ensures each window is compatible with the schedule cadence. Omitting a window uses the default 60-second range, while explicit zero-duration windows remain supported. Deployment summaries are derived from the deployment's stored task metadata, so they reflect the declarations associated with that deployment. |
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|
Katia Bulatova
|
ee854480fe |
fix(webapp): dashboard agent maintenance moves into the agent project (#4599)
## What & why The dashboard agent's upkeep — retention deletes and the investigation sweep — ran as cron jobs on the webapp's common worker, even though it only touches the agent's own datastore. This moves that upkeep into the agent's Trigger project as scheduled tasks (TRI-13182). ## What's inside **Retention** — `internal-packages/dashboard-agent/src/maintenance.ts`, a daily task (03:00 UTC). Deletes turn evals older than 30 days, hard-deletes chats soft-deleted more than 30 days ago, and purges terminal watches and submission rows older than 7 days. It used to run every 5 minutes; nothing needs a hard delete that fast, so it is daily now, draining in bounded batches and warning if it hits the cap. It retries (3 attempts) because the next run is a day away. It connects with `DASHBOARD_AGENT_DATABASE_URL`, falling back to `DATABASE_URL` like every other task in the package (the deletes are confined to the agent's own Postgres schema), and skips when neither is set. **Investigation sweep** — `src/investigation-sweep.ts`, every 5 minutes, same as before: settles investigation cards stuck `in_progress` (30-minute window, attempt cap, force-abandon note). It keeps the fast cadence because it fixes live state the UI is showing. **What stays in the webapp.** The watch finalize/deliver sweep and batch rearm: they cover a dead agent-side tick chain — a backstop can't live inside the thing it backstops — and they need the main database and the alerts worker. The org-deletion chat purge also stays: deletion must not depend on the agent project being deployed. The removed cron job keeps a cron-less tombstone entry so already-queued items drain cleanly; remove it in a follow-up. **Test plumbing** — the drizzle migration replayer that webapp tests hand-rolled is now exported once from `@internal/dashboard-agent-db/testing`; the moved tests live in the agent package as `src/*.test.ts` against real Postgres. ## Testing Agent package: retention passes (backlog drain, batch cap, no-op guard, chat-delete cascade) and the sweep, on testcontainers Postgres. Webapp: the watch/chat suites, plus a test that a settlement card stops the dashboard spinner. Full typecheck on both. |
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Eric Allam
|
aca234d1c3 |
perf(webapp): bound checkSchedule environment load to the requested ids (#4598)
## What
`CheckScheduleService.call` loaded **every** environment of a project
(`{ id, type, archivedAt }`, no filter) and then immediately narrowed to
just the requested `environmentIds` via
`resolveProjectScopedEnvironments`. It only ever uses the requested envs
(to reject foreign env ids and reject archived branches). On a
preview-heavy project that meant loading hundreds of archived branch
rows to validate one, on a path called in a per-scheduled-task loop on
the deploy path (`createBackgroundWorker` -> `syncDeclarativeSchedules`)
and from `upsertTaskSchedule`.
The query is index-backed and individually fast (rows_read/returned = 1
per predicate), so this is about result-set width / egress and wasted
work at scale (~580k calls/24h observed via Insights), not a slow plan.
## Change
Bound the `environments` relation load to `boundedIn(environmentIds)`:
```ts
environments: {
where: { id: { in: boundedIn(environmentIds) } },
select: { id: true, type: true, archivedAt: true },
}
```
Returns `<=` the number of requested envs (usually 1) instead of the
whole project. Both existing behaviors are preserved:
- **Foreign-id rejection**: the relation is still scoped to the project,
so a requested id belonging to another project never comes back and
`resolveProjectScopedEnvironments` reports it as `foreign` (a missing
requested id is already treated as foreign).
- **Archived-branch rejection**: a requested id that is an archived
branch still comes back with `archivedAt` set, so the downstream `Can't
add or edit a schedule for an archived branch` check still fires.
`archivedAt` is kept in the select deliberately, so this bounds by id
rather than filtering archived rows out.
## Evidence (isolated stack, seeded 1 prod env + 40 archived branch
envs)
Local `EXPLAIN (ANALYZE)` of the exact environments sub-select:
| | rows returned | buffers |
|---|---|---|
| before (unbounded) | **41** | shared hit=12 |
| after (`id IN (requested)`) | **1** (`Rows Removed by Filter: 40`) |
shared hit=4 |
Same `RuntimeEnvironment_projectId_idx`, no plan change. Rows to the
client drop to `len(environmentIds)`, which is the point.
**Unit (vitest, testcontainers, real Postgres):**
`apps/webapp/test/checkSchedule.test.ts` extended to prove, on real
rows, that the bounded load returns only the requested env (1 of 10),
still reports a foreign id as foreign, and still surfaces an archived
branch when it is the requested one. 5/5 pass.
**Full e2e (both execution modes, real stack):** a purpose-built project
with two declarative `schedules.task`s.
- `trigger dev`: dev worker created, both schedules synced through the
edited `checkSchedule` loop, no errors.
- `trigger deploy` (managed deployment): PRODUCTION worker registered,
both schedules synced against the **prod** environment through the same
loop, prod + dev schedule instances active, no errors.
`typecheck --filter webapp` clean.
## Rollout / rollback
Straight deploy, no flag, no migration. Rollback is revert-only
(read-path narrowing, no data change). Old and in-flight rows read
correctly under both the old and new code.
## Out of scope
The two lower-priority sibling reads in the ticket (the Query/metrics
env id->slug map and the env-var repository fan-out) are left for
follow-ups; they need caching / per-method scoping rather than this
single bound.
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Matt Aitken
|
bc3a33be24 |
fix(webapp): stop the billing limits page timing out under enforcement (#4594)
## Summary Opening the billing limits page while a spend limit was being enforced could time out with no response for organizations with many preview branches. That is exactly the moment the page matters: it is the only self-serve way to raise or resolve the limit. The page now loads fast regardless of how many environments the organization has. ## Root cause and fix The loader's queued-run count ran one ClickHouse count per billable environment, sequentially, with no timeout, and the environment list included every archived preview branch ever created. Thousands of environments times one round trip each held the response open past the edge timeout. The count is now a single org-level ClickHouse query filtered on environment type, capped server-side with max_execution_time. If the count fails, the loader falls back to 0 (the page hides the count label at 0) instead of throwing, so the recovery panel stays reachable even when the count errors. The billing-limit bulk-cancel path also stops enumerating archived environments. |
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Eric Allam
|
4fd7cc0f55 |
perf(webapp,database): index RuntimeEnvironment.pauseSource for the billing-limit reconcile tick (#4590)
## What
The `billingLimit.reconcileTick` worker calls
`getOrgIdsWithBillingPauseSource()` on
`BILLING_LIMIT_RECONCILE_INTERVAL_MS` (~every 90s) to find which orgs
currently have billing-limit-paused environments. Two problems:
1. `RuntimeEnvironment.pauseSource` had no index, so `WHERE pauseSource
= 'BILLING_LIMIT'` was a **sequential scan of the whole table** on the
control-plane primary, every tick.
2. Prisma `distinct` dedups **after** fetching, so it read every paused
row (thousands) to produce a handful of distinct org ids.
This PR:
- Adds a **partial index** on `RuntimeEnvironment (pauseSource,
organizationId) WHERE pauseSource IS NOT NULL`. Nearly all rows have
`pauseSource = null`, so the index stays tiny. Second column lets the DB
satisfy the distinct-org lookup from the index. Defined in SQL (Prisma
can't express partial indexes), matching the existing partial-unique
indexes on this model.
- Switches the query from `findMany({ distinct })` to
`groupBy(["organizationId"])`, pushing DISTINCT into the DB so it
returns only the distinct orgs.
## Evidence
**Correctness** — colocated `postgresTest` (testcontainers, no mocks):
multiple `BILLING_LIMIT` envs in one org collapse to one org id,
`pauseSource = null` envs are excluded, each org id returned once. 5/5
tests in `billingLimitReconciliation.test.ts` pass.
**Plan change** — `EXPLAIN ANALYZE` on a synthetic table (200k rows,
5,250 `BILLING_LIMIT` across ~40 orgs, mirroring the test-side numbers
from the investigation):
| | Before (no index) | After (partial index) |
|---|---|---|
| Plan | Seq Scan (194,750 rows removed by filter) | Bitmap Index Scan
on partial index |
| Buffers | 1355 | 51 (index 6 + heap 45) |
| Exec time | 6.06 ms | 0.59 ms |
Index size 56 kB vs table 11 MB. The key win: cost now scales with the
paused-env count, not total table size, which matters most on prod where
the table is far larger.
## Rollout & rollback
- **Index**: `CREATE INDEX CONCURRENTLY IF NOT EXISTS`, in its own
migration file. Pre-apply the index manually on the control-plane
primary before deploying the migration (the migration is a no-op if the
index already exists).
- **Query change** is behavior-equivalent (same distinct org set), so no
flag needed.
- **Rollback**: revert the deploy and drop the index. No data migration
either direction.
## Notes / limitations
- The planner uses a Bitmap Heap Scan, so `organizationId` is still read
from the heap (45 blocks for the matched rows only, not the whole
table). A pure index-only scan isn't chosen for the bitmap path; the
second index column keeps that open for the index-scan path at
negligible cost.
refs TRI-13169
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Chris Arderne
|
ed1bb72fb8 |
feat: implement cron window spread backend (#4566)
- New DB fields on Schedule and ScheduleInstance - Use `queueTimestamp` for the "effectiveAt" delayed start time, propagate it to Clickhouse TaskRun table - Disable fastpath for delayed jobs - Add schedule timing logic, API endpoints with windows, persistence - Calculate phase for every schedule, only persist when window is non-null - Additional o11y for phased rollout |
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Matt Aitken
|
c2c6e5c705 |
fix(webapp): keep session runs off the legacy realtime streams backend (#4564)
## Summary Runs created for a Session were triggered without a realtime streams version, so they fell through to the `realtimeStreamsVersion` column default of `v1`. A Session's own `.in` / `.out` channels are always `v2`, so any run-scoped `streams.append()` or `streams.pipe()` call made inside a session run wrote to a different backend than the session it belongs to, and stayed there for the life of the run. The API trigger routes were never affected. They call `determineRealtimeStreamsVersion` with the client's `x-trigger-realtime-streams-version` header and always pass an explicit value, so a current SDK asking for v2 gets it. Only the internal callers that build trigger options by hand were leaning on the column default, which no env var can influence because that path never calls the resolver at all. ## The version resolver Fixing the call site exposed a second problem in `determineRealtimeStreamsVersion`. Its two paths disagreed: an explicit `v2` was checked against the S2 configuration first, but when the caller expressed no preference it returned `REALTIME_STREAMS_DEFAULT_VERSION` verbatim with no check. A deployment that set the default to `v2` without configuring S2 therefore stamped runs `v2`, nothing failed at trigger time, and every later read or write against those runs' streams threw `Realtime streams v2 is required for this run but S2 configuration is missing` for the life of the run. Both paths now resolve through one pure function that takes its configuration rather than reading `env`: ```ts const requested = streamVersion ?? config.defaultVersion; if (requested !== "v2") return "v1"; const hasCredentials = Boolean(config.accessToken) || config.skipAccessTokens; return hasCredentials && Boolean(config.basin) ? "v2" : "v1"; ``` ## The basin requirement `resolveStreamBasin` resolves run, session and organization basins ahead of the global setting, so a deployment that provisions a basin per organization can serve v2 with no global basin at all. Gating purely on the global setting would degrade every run there to `v1`. `determineRealtimeStreamsVersion` therefore takes an optional organization basin, and every caller that holds one passes it, including the session path: ```ts basin: organizationBasinName ?? env.REALTIME_STREAMS_S2_BASIN, ``` This is deliberately the resolved basin and not the `REALTIME_STREAMS_PER_ORG_BASINS_ENABLED` flag. The flag says the feature is on, not that a given organization has been provisioned, and provisioning happens out of band. Keying off the flag would stamp `v2` on runs for unprovisioned organizations, recreating the failure this removes. **This widens behaviour for explicit `v2` requests**, which previously required the global basin: a provisioned organization on a per-org deployment now resolves `v2` where it used to get `v1`. That is intentional, and it makes every path agree. ## Scope Only newly created runs change. A run already stamped `v1` keeps that version for its lifetime by design, since readers resolve the backend from the same column and its existing streams have to stay readable. Scheduled runs reach the same column default through `scheduleEngine.server.ts` and are deliberately left alone: that one is a policy question about `REALTIME_STREAMS_DEFAULT_VERSION` rather than an inconsistency inside a single feature. ## Verification A full-stack e2e boots the real webapp plus Postgres, Redis and s2-lite, creates a Session through the public API so the run comes from the real trigger path, appends records the way `streams.append()` does, and asserts three things at once: the version stamped on the run, that the payload is readable from S2, and that no key exists in Redis. It appends at a realistic record size so the route's body cap and S2's per-record cap are both exercised. Reverting the session-path change flips all three observations, so it fails against the old behaviour rather than passing vacuously. Unit tests cover the resolver matrix, including organization-basin-only and credential-only configurations; two of them fail against the previous resolver. Also verified by hand against a local stack: a real `chat.agent` session run writing 8 records of 250KB through `streams.append()` put 2,049,072 bytes into S2 with no Redis key, while the same agent with the session-path change removed put 2,102,360 bytes into Redis and nothing into S2. |
||
|
Katia Bulatova
|
0b750d00dd |
feat(webapp): dashboard agent — Watch (#4525)
Watch is the agent noticing something later: you ask it to tell you when a condition holds, and it answers when it does — or when it can't any more. A watch is a **durable one-shot promise**. The condition is checked on a schedule by deterministic code (no LLM in the checks), the answer lands in the chat once, and then the watch is over. Ten kinds: three on a run, five on a queue, error recurrence, health recovery. ## Stack Stacked on **#4529** (UI), which is stacked on **#4418** (chat, reports, investigate). Merge those first. **#4516** (storybook gallery) sits on top of this branch. ## How to review [**GUIDEBOOK.md**](https://github.com/triggerdotdev/trigger.dev/blob/feat/dashboard-agent-flows-watch/internal-packages/dashboard-agent/GUIDEBOOK.md) on this branch is the behaviour reference — it states the conditions rather than the code, so you can predict what happens without running anything. "The ten watch kinds, and what makes each fire" and "Creating a watch" describe exactly this PR, and the tables there are the spec the code is written against. ## What's inside - **Ten watch kinds**, one deterministic check each (`dashboardAgentWatch*Checks.ts`), with the spec union in `dashboard-agent-contracts/src/watch.ts`. - **Scheduling** — each watch schedules its own next check; due watches of one `(environment, cadence)` group can be checked together in one batch pass, with a sweep as the backstop for expiry, redelivery and retention. - **Delivery** — the in-chat wake and card, an optional email alert (new `DASHBOARD_AGENT_WATCH` alert channel, so it shows on the project's Alerts page with one-click unsubscribe), and an optional investigation when the outcome needs attention. - **Submission ledger** — `watch_submissions`, keyed `(chat_id, client_request_id)`, so a retried card submission replays the recorded outcome instead of creating a second watch. - **Watch token** — a delayed-execution credential accepted only by the watch endpoints, re-checked against the user's live access on every tick. - **Unread work** — the panel polls for wakes that landed while it was closed, so a chat can go unread and light the launcher dot. ## Key decisions **A check result is a 4-way, and only two of them are verdicts.** `satisfied` / `terminal_unsatisfied` are answers; `pending` and `unavailable` are not. Any exception inside any check is caught in one place and becomes `unavailable` with an unverified observation — a check that failed is never evidence. **A completed window is an answer, and whether it is good or bad news is declared per kind, never inferred.** There is a table for that in the guidebook: `run_failed` completing its window is *good* news ("hasn't failed"), `backlog_drain` completing it is not. One rule overrides the table: a window that completed on an unverified observation is neutral and says only that the watch ended without a confirmed answer. **An unreadable source is never a negative answer** — and, because investigations only open on `attention`, it never starts one either. **Identity is `(chat, project, environment)` plus the condition,** enforced by a partial unique index over active rows (`watches_chat_active_identity_key`), not by the read-then-insert check. Cadence, window, note and `ticks` are deliberately not part of it. Two different chats may watch the same thing — a watch is a promise to a chat. **The server resolves the target's name, whatever the model calls it.** The model can't tell a task queue (`task/<id>`) from a custom queue, so both spellings are tried and the stored one wins — and the rewrite happens **before** identity and before the row is written, so the identity, the checks, the link and the wording all see one spelling. **Freshness fences.** Depth falls back from the live counter to the newest 60 s ClickHouse bucket, which only counts as current within 60 s of now. A non-current reading at or below the *quiet line* is refused as `unavailable` rather than believed, so a stale empty bucket is never read as "drained". The stall streak is the one piece of carried state: it lives in the previous check's facts and *freezes* on an unreadable reading rather than breaking. **Chain reliability.** There is no shared cron — each watch (or batch group) schedules its own next tick, so the failure mode to review is the chain dying. A failed batch check is caught, the next tick is scheduled anyway and the run resolves rather than failing, so the chain survives a check that couldn't run; the sweep re-arms groups and finalizes anything still active past its deadline, even when delivery isn't configured. Wake redelivery is id-deduped rather than conditional, because the sweep can't know whether the user was already told. Access is re-authorized on **every** check against the primary — replica lag would extend access the user has already lost. **Wording lives in one place.** `watch-wording.ts` is read by the card, banner, toast, email and the agent's own narration, and the numbers come from the frozen observation rather than a fresh read, so a retry produces the same sentence. Replay reproduces the **recorded** decision instead of deciding again — the transcript is append-once, so a second decision would contradict it forever. **Cancellation is the ending without an answer** — no resolution, no wake. One exception, decided during testing: a watch the *user* cancelled leaves a single neutral transcript line ("Stopped watching …"), keyed off the watch id so a retry can't repeat it. The other four reasons stay silent. **Email is opt-in and only a fired watch emails.** An expiry is narrated in the chat and nowhere else. Both gates (agent access, a configured email transport) are checked at subscribe time *and* again at delivery, and the subscription outcome is frozen on the ledger row so a retry replays it. Neither gate is a plan check. **One watch offer per turn.** The prompt and the renderer guard this independently — if the turn already proposed a watch card, the action button is dropped, because the card is the better affordance. Two eval cases pin the prompt side: exactly one offer with the line last and the button after it, and zero offers when the rendered card already carries one — deterministic assertions, over a real-model run. ## Testing Unit tests (vitest, testcontainers, no mocks) under `apps/webapp/test/dashboardAgentWatch*.test.ts` and `internal-packages/dashboard-agent/src/watch-*.test.ts` cover the invariants above: the 4-way check results and the freshness fences, identity/dedup and the submission ledger, queue-name resolution, the batch chain surviving a failed check, sweep boundaries and alert-once, tenancy and the watch token's scope, and the wording snapshot. The load-bearing ones were verified by control-breaking the guard first and checking the test goes red. Live-tested end to end against a local stack, following the guidebook: all ten watch kinds firing and expiring, cancellation, the email pair (a fired watch mails, an expired one does not), and watch recovery from a health report. |
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|
Eric Allam
|
326e9950f4 |
perf(webapp): scope declarative schedule sync to the current environment (#4577)
## Summary Background worker registration runs on every deploy and every `trigger dev` file save. Its declarative-schedule reconcile loaded every declarative schedule for the whole project across all environments, then re-fetched the deletion candidates it already had in memory. For projects with many scheduled tasks or many environments, that meant reading tens of thousands of rows on each registration. This scopes the load to the environment being registered, drops the redundant re-fetch, and selects only the columns the reconcile needs. It also fixes the schedule-limit count (`getUsedSchedulesCount`), which joined `TaskSchedule` and `RuntimeEnvironment` without a project constraint and could scan those tables in full. Pushing `projectId` onto both joins gives it a project-scoped index path with the same result. Follow-up to [#4522](https://github.com/triggerdotdev/trigger.dev/pull/4522), which batched the delete side of the same reconcile. |
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|
Eric Allam
|
26cdedda1c |
perf(webapp): scope env var create pre-check to submitted keys (#4579)
## Summary Setting or importing environment variables ran a conflict pre-check that loaded every variable in the project and every value across all of its environments, only to decide whether the submitted keys already had a value in the target environments. On projects with many variables and environments that meant reading tens of thousands of rows on each create/import call. This scopes the pre-check to the submitted keys and target environments, so it reads only the rows it actually inspects (submitted keys × target envs), wrapped in `boundedIn` to keep the prepared-statement cache stable. Same conflict detection, a handful of rows instead of the whole project's env-var values. |
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|
Katia Bulatova
|
4569657923 |
feat(webapp): dashboard agent — chat, reports, investigate (#4418)
## What & why This is the system behind the Dashboard Agent — an assistant that answers questions about a project's runs, errors, queues, deploys and health, and can investigate failures end to end. The agent runs as a chat.agent task in its own Trigger project. It has no access to the main database or ClickHouse; all platform data is read through the public API using a delegated, read-only user token. Everything here is behind `canAccessDashboardAgent` and inert with the flag off. The UI that mounts the panel lands in #4529. ## Stack `#4418` (this, base) ← `#4529` UI ← `#4525` Watch ← `#4516` storybook gallery. The scenario/contract reference for the whole stack is `internal-packages/dashboard-agent/GUIDEBOOK.md` (it lands on the Watch branch): it states, per feature, what makes each thing happen and where that is decided. ## What's inside **Agent runtime and tools** — `internal-packages/dashboard-agent`: prompt, tool set (API reads, TRQL query, docs, navigation, evidence/investigations, repo source), conversation compaction, a prompt-prefix token budget pinned by snapshot test, and sampled LLM-judged turn evals. The package cannot import webapp server code, which is what makes the "no DB access" claim structural rather than a convention. **Contracts** — `internal-packages/dashboard-agent-contracts`: `trigger://` URIs, intents, and the block envelope every rendered card travels in. **Conversation store** — `internal-packages/dashboard-agent-db`: drizzle over postgres-js in its own `trigger_dashboard_agent` Postgres schema, plus one additive migration. **Auth boundary** — the user-actor token gains an optional environment claim; one guard (`userActorEnvironment.server.ts`) enforces it so routes don't each re-derive the rule. Token minting, cap ceiling, and the RBAC fallback path for self-hosted. **Transport** — webapp resource routes that mint the token and proxy each turn, and SDK-side mid-turn reconnect. **Public API the agent reads through** — orgs, projects, environments, runs, queue metrics, workers, a run's commit metadata, repo snapshot, reports, and `POST /api/v1/query`. **Reports** — the health report's layout is declared once and shared by the card, the markdown surface and the JSON/MCP surface, so the same report reads the same in the dashboard, the terminal and an editor. **Block renderers** — the report and investigation cards the flows above already emit (`app/components/dashboard-agent/`). The panel that hosts them, and the rest of the chat UI, is #4529. **Query safety and CSP** — see below. ## Key decisions - **The agent is a separate Trigger project, not webapp code.** It reads platform data over the public API with a delegated user-actor token whose `cap` ceilings it to read scopes. No Prisma, no ClickHouse, no webapp imports. - **The PAT-only auth helper now refuses user-actor tokens.** This is an intentional behavioral change: its callers consume only a bare userId and do not enforce delegated-token capabilities. Actor-aware routes continue through the scoped route builders instead. - **RBAC fallback builds a delegated token's ability from its own cap**, never the blanket ability a PAT gets (read-only when the token declares none). Without this, the agent's read-only cap would buy a write JWT on self-hosted. - **Org creation checks RBAC only for user-actor tokens, and only after the env gate**, so an install with `ORG_CREATION_API_ENABLED` off returns 404 rather than 403, and an ordinary PAT never consults an ability the route has no org to scope. Both orderings are pinned by test. - **The query path is read-only in depth.** TRQL rejects write statements at the grammar level (they don't parse, rather than being filtered), ClickHouse runs with `readonly=1`, and the org/project/env filters are injected server-side from the credential — the request body cannot widen scope. An unparseable query denies instead of falling through to the permissive resource. - **Document-wide img-src CSP.** Remote images are an outbound-request/exfiltration surface, so the policy permits only own-origin/data/blob, the required SSO avatar hosts, and the favicon endpoint. Operators can add exact origins through CSP_IMG_SRC_ALLOWLIST; wildcard hosts and bare schemes are intentionally not allowed. - **The chat transport reconnects on a mid-turn EOF** (`@trigger.dev/sdk`). A body that ends without a turn-complete is terminal only when the server says `X-Session-Settled: true`; otherwise the transport resubscribes from `lastEventId` with bounded backoff, and any record re-earns the budget. Previously a closed long-poll window or a proxy restart left the reply stuck as if still generating. - **Conversations live in their own datastore**, schema-scoped and foreign-key-free (it references `organizationId`/`userId` by id, because in cloud it is a different database). It is a display read-model for the History tab and transport resume; `chat.agent`'s object-store snapshot remains the model's source of truth. - **Deterministic first.** Reports and health checks contain no LLM — they are computed from the same data the dashboard shows, and the model only narrates and links them. That is what makes a number in an answer auditable. ## Testing - 63 new test files, run with `pnpm run test --filter webapp` and per-package vitest. Heaviest coverage on the auth boundary (`userActorPatOnlyBoundary`, `userActorTokenClaimsAndScopes`, `contextlessPatRoutes`, `rbacFallbackBranch`), TRQL read-only, the report layout, and the SDK reconnect. - The agent package has a separate eval lane (`pnpm run test:evals`, `vitest.eval.config.ts`) that hits the real model, so it never runs in `pnpm test`. - Live-tested against a local stack scenario by scenario; the GUIDEBOOK lists the condition each behaviour is expected under, which is what those runs were checked against. ## Changelog `.server-changes/dashboard-agent.md`, plus changesets for `@trigger.dev/core` (report schemas), `@trigger.dev/sdk` (chat reconnect) and the CLI's `mint-token` help text. |
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|
Eric Allam
|
6449a644b9 |
feat(webapp,cli,database): track real dev onboarding progress (#4563)
## Summary
The dev environment "Get set up" panel used to be a static list of CLI
commands that only disappeared once your tasks registered, so nothing
ever changed after you ran `init` and people assumed it was stuck. It
now tracks real progress: `trigger init` records the project as
initialized, so step 1 checks off, and the panel updates live as the dev
server connects and your tasks register.
It also adds a prominent "Copy AI agent prompt" button, presented as a
clear alternative ("or") to the manual CLI steps, that copies a
ready-to-paste setup prompt pre-filled with your project reference for
Claude Code, Cursor, or any coding agent.
## Notes
- Adds a `Project.initializedAt` column (migration
`20260811065646_add_project_initialized_at`); the CLI `init` command
calls a new project-scoped `POST /api/v1/projects/:ref/init` best-effort
at the end of setup.
- The `init` scaffold now imports from `@trigger.dev/sdk` instead of the
deprecated `/v3` subpath.
## Screenshots
<img width="2400" height="1794" alt="v7-redesigned-card"
src="https://github.com/user-attachments/assets/c2fb4fa1-9484-4700-8bd3-110d66f5a44e"
/>
|
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Eric Allam
|
820c079145 |
perf(webapp): read per-run environment config from the replica at dequeue (#4560)
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## Summary Adds an opt-in path to serve a run's per-run configuration reads from the control-plane read replica instead of the primary, reducing primary database load during task execution. The managed-worker dequeue resolves each run's environment, organization, and environment variables before starting the run; those rows are stable for the life of a run, so they can safely come from the replica. Gated by `CONTROL_PLANE_DEQUEUE_READS_FROM_REPLICA`, defaulting to `"0"` (reads from the primary, unchanged from today). Set it to `"1"` to route the reads to the replica. The env-var read is scoped to the dequeue/resolution path (`resolveVariablesForEnvironment`); dashboard env-var reads and writes always stay on the primary. When no read replica is configured, `$replica` transparently falls back to the writer, so single-database self-host is unchanged either way. Verified end-to-end against a real primary/replica split, in both `trigger dev` and deployed (managed-worker) runs: with the flag on, env vars inject correctly and a value set immediately before triggering a deployed run is present on the run. |
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Eric Allam
|
951d8e8d7b |
feat(webapp): per-client database pool metrics that survive the driver adapter (#4541)
## What Follow-up to #4539. The driver-adapter work is inert until a client flips to the pg driver adapter, but the moment one does, our database observability degrades: the OTel metrics pipeline reads pool stats from Prisma's `$metrics`, which is owned by the Rust engine's `quaint` pool. Under the adapter, `pg.Pool` owns the pool, so those gauges read zero. The pipeline also only ever scraped a single client (the control-plane writer singleton). This PR makes database metrics driver-agnostic and per-client: - Every configured client registers a metrics source: control-plane writer/replica, run-ops writer/replica, legacy writer/replica. Previously only the control-plane writer singleton was scraped. - Each OTel instrument is observed per client with `db_client` and `db_driver` (`quaint` | `pg-adapter`) attributes. `db_client` uses our canonical datasource-role labels (`control-plane-writer`, `control-plane-replica`, `run-ops-writer`, `run-ops-replica`, `legacy-run-ops-writer`, `legacy-run-ops-replica`) — the same strings used for the `db.datasource` span attribute, so a metric and a trace point at the same pool. - Pool figures come from the authoritative source per driver: - **pg-adapter**: `pg.Pool` (`totalCount`/`idleCount`/`waitingCount`, plus cumulative opened/closed from `connect`/`remove` events). - **quaint**: the Rust engine's `$metrics` pool gauges/counters, exactly as before. - Query counters and duration histograms still come from `$metrics` for both drivers (the Rust engine executes queries in both cases). - New `db.pool.connections.waiting` gauge (pg.Pool exposes this; quaint reports 0). - Stops exporting Prisma metrics from the Prometheus `/metrics` route. Pool observability now lives entirely in the OTel pipeline, per driver, per client. ## Why So we can flip any client (including the control-plane writer, the primary desync-fix target) to the driver adapter without losing pool visibility. Existing dashboards keyed on the same metric names keep working; they gain a per-client dimension. ## Testing Unit (`apps/webapp/app/utils/databaseMetrics.server.test.ts`): the pure normalizer — quaint reads pool from `$metrics`; adapter reads pool from `pg.Pool` and keeps engine query metrics; `busy` never goes negative; graceful zeroing when `$metrics` is unavailable (adapter still reports live pool figures). Live smoke test against a prod-shaped local stack: three physically-distinct Postgres DBs (control-plane, run-ops, legacy) behind dual PgBouncers, split mode on, with a mix of adapter and quaint clients. Reading the actual emitted OTel metrics, every pool shows up as its own series: ``` db.pool.connections.total{db_client="control-plane-writer", db_driver="pg-adapter"} = 1 db.pool.connections.total{db_client="control-plane-replica", db_driver="quaint"} = 1 db.pool.connections.total{db_client="run-ops-writer", db_driver="pg-adapter"} = 1 db.pool.connections.total{db_client="run-ops-replica", db_driver="quaint"} = 1 db.pool.connections.total{db_client="legacy-run-ops-writer", db_driver="quaint"} = 1 db.pool.connections.total{db_client="legacy-run-ops-replica",db_driver="quaint"} = 1 db.client.queries.total{db_client="control-plane-writer",db_driver="pg-adapter"} = incrementing db.client.queries.duration.count{db_client="control-plane-writer",db_driver="pg-adapter"} = incrementing ``` Confirms: metrics are attributed per pool with the correct driver; adapter pools' figures come from `pg.Pool`; and query counters/duration histograms keep incrementing under the pg adapter. Also verified `/metrics` (Prometheus) now returns zero `prisma_*` series while still serving the app's own metrics. `pnpm run typecheck --filter webapp` passes. ## Notes - `/metrics` (Prometheus) no longer includes `prisma_*` series. Anything scraping that endpoint for Prisma metrics should read the equivalent `db.*` metrics from the OTel exporter instead. - **PgBouncer + `?schema=` gotcha (separate from this PR, worth flagging for rollout):** since #4539 parses `?schema=` from the DSN and passes `{ schema }` to the adapter, node-postgres sends `search_path` as a startup parameter. A transaction-mode PgBouncer rejects that with `FATAL: unsupported startup parameter: search_path`. Our prod control-plane DSNs use the default `public` schema with no `?schema=` param, so this is latent, but any client we flip to the adapter must not carry `?schema=` in its DSN (or the pooler needs `ignore_startup_parameters = search_path`). --------- Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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Eric Allam
|
90e8bd5c12 |
feat(webapp,database): opt-in per-client Prisma driver adapters (#4539)
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## What
Adds an opt-in path to run each Prisma client through
**`@prisma/adapter-pg`** (the node-postgres driver) instead of the
built-in engine driver, controlled by a **per-client env var, all off by
default**:
| env var | client |
|---|---|
| `CONTROL_PLANE_DATABASE_WRITER_DRIVER_ADAPTER` | control-plane writer
|
| `CONTROL_PLANE_DATABASE_REPLICA_DRIVER_ADAPTER` | control-plane
replica |
| `RUN_OPS_DATABASE_WRITER_DRIVER_ADAPTER` | new run-ops writer |
| `RUN_OPS_DATABASE_REPLICA_DRIVER_ADAPTER` | new run-ops replica |
| `RUN_OPS_LEGACY_DATABASE_WRITER_DRIVER_ADAPTER` | legacy run-ops
writer |
| `RUN_OPS_LEGACY_DATABASE_REPLICA_DRIVER_ADAPTER` | legacy run-ops
replica |
With every flag unset the construction path is byte-identical to today
(`datasources` URL + Rust engine), so this is inert until a flag is
turned on. Per-client granularity allows enabling the adapter only where
it's wanted.
## How
- Enables the `driverAdapters` preview feature on both schemas
(`@trigger.dev/database` and `@internal/run-ops-database`). This keeps
the **Rust query engine** — it does NOT add `queryCompiler` — so query
behavior, result types, and engine tracing spans are unchanged.
- A shared `buildDriverAdapterPool` builds each client's `pg.Pool` with
an explicit `max`, a bounded `connectionTimeoutMillis` (the
node-postgres pool otherwise waits unbounded on acquire), and an
`onPoolError` handler (an unhandled idle-connection error would
otherwise crash the process). Threaded through all four client builders
via a `useDriverAdapter` flag.
- Adds `@prisma/adapter-pg` + `@types/pg` to the webapp; `pg` is already
pinned at `8.15.6` (adapter-pg 6.x requires `pg < 8.17`).
## Connect-failure handling (the important correctness/security bit)
Under the adapter an unreachable DB no longer surfaces as
`PrismaClientInitializationError` / `P1001`; it becomes a `P2010`
"Database not reachable: <host>" (or a raw
`ECONNREFUSED`/`ENOTFOUND`-class error). Two handlers are updated so a
client on the adapter behaves like today:
- **`isInfrastructureError`** now recognizes those shapes (P2010 with a
connectivity message, and raw connectivity errno codes). Without this,
the DB **hostname would leak into API-client-facing errors** and the
failure would go unlogged. Security-relevant.
- **`isPrismaRetriableError`** treats the adapter's pool-acquire timeout
("timeout exceeded when trying to connect") as retriable, preserving the
`P2024` retry behavior the adapter otherwise drops.
## Evidence
Validated on an isolated stack that mirrors the production DB topology
(chained PgBouncers in front of writer + reader):
- **Behavioral parity:** raw-query results and Prisma error codes/`meta`
are byte-identical between the engine driver and the adapter across the
queried shapes (unique-constraint `meta.target`, record-not-found,
transaction-timeout, serialization-failure, etc.).
- **Feature matrix:** a full 380-project queue-ay pass shows no
adapter-caused regressions — pass/fail parity between adapter-off and
adapter-on, with the residual failures being pre-existing
known-failures/flakes common to both.
## Rollout / rollback
All flags default off; enable per client via env var, roll back by
unsetting and redeploying (no data migration). Recommended first target
is a single writer; enable one client at a time.
## Follow-ups (not in this PR)
- `$metrics`-based pool observability is removed under the adapter (the
Prometheus route + `db.pool.connections.*` instruments); the metrics
replacement (via `pg.Pool` counters) lands in a separate PR.
- Note for operators: on the adapter path, interactive-transaction
`maxWait` does not bound pool acquisition — `connectionTimeoutMillis`
does.
## Note on connection-string parameters
The adapter pool is built from the base DSN, so Prisma-specific DSN
parameters that node-postgres does not understand are not honored when a
client is on the adapter:
- **Prisma TLS spellings** (`sslaccept`, `sslcert`, etc.) —
node-postgres uses `sslmode`/`ssl` instead. Our production DSNs do not
use these Prisma-specific TLS params, but any deployment whose DSN
relies on them must be checked before enabling a flag.
- `pgbouncer=true` and `statement_cache_size` — effectively moot under
the adapter, which uses no persistent named prepared statements.
`connection_limit`, `pool_timeout`, and `schema` are handled explicitly
(passed as `max`/`connectionTimeoutMillis` and PrismaPg's `{schema}`
option).
refs TRI-13039
---------
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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||
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Eric Allam
|
c526528d8f |
feat(webapp,database): bound Prisma list filter arity (#4480)
⚒️ Publish Worker (v4) / build (supervisor) (push) Has been cancelled
## Summary Prisma expands `in` / `notIn` into one bind parameter per element, so every distinct list length is a separate prepared statement. Where the length tracks data volume (a batch size, a run-graph fan-out, a prior query's id set) one call site can mint hundreds of them. Each is used about once, but inserting it evicts an entry that was being reused, so the cost lands on unrelated queries sharing the pooler's statement cache. An unbounded list also risks the 65535 bind-parameter ceiling. `boundedIn()` pads a filter list to the next power of two by repeating its last element. `IN` and `NOT IN` ignore duplicates, so results are unchanged, and a call site drops from one statement per length to at most `log2(cap)`. Applied to all existing sites. ## Enforcement Two oxlint rules require the helper: a list filter must be an inline array literal or a `boundedIn()` call. - The first covers filters reached through `where` / `having` / `cursor`, and deliberately never descends into `data`, `create`, `update`, `set` or `equals`. A key named `in` in those positions is user data, not a predicate, and rewriting it would corrupt what gets stored or compared. - The second covers bare filter objects passed to where-building helpers, which the first cannot see. It found five sites in the run-graph batch loaders that were otherwise invisible. Both rules follow filters through the shapes they are actually written in: conditional expressions, logical-and objects, spread-conditional properties, computed keys, and call arguments. An array literal only counts as fixed-arity when nothing spreads into it, since `[...new Set(ids)]` has a runtime length. Twelve sites were hidden behind those shapes until the rules handled them. Scoped to `in` and `notIn`. The scalar-list filters `hasSome` and `hasEvery` compile to `&& $1` and `@> $1`, passing the whole array as a single bind parameter, so their arity never reaches the statement text and there is nothing to bound. Both rules are `error`, so new call sites fail CI. That ratchet has already caught four sites added by other PRs while this one was in review. ## Notes `boundedIn` pads by repeating rather than with null: `x NOT IN (a, b, NULL)` is never true, so null-padding a `notIn` filter would silently return no rows. Lists above 32768 are returned unchanged so padding can never push a query past the parameter limit. Route modules reach the helper through `~/db.server` rather than importing the database barrel directly, since a value import of that barrel into a module that also exports a React component is only safe while dead-code elimination prunes it. Measured on a local rig: 300 distinct list lengths produce 300 prepared statements unpadded, 10 padded. Verified end-to-end against a local stack with the full task-suite sweep, which surfaced no regressions. |
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Eric Allam
|
63176a6d69 |
fix(webapp): stop api inheriting inbound sampled traceparents so trace sampling applies (#4532)
## What The internal tracing `ParentBasedSampler` in `tracer.server.ts` left `remoteParentSampled` at its default of `AlwaysOn`. Any request arriving with a `traceparent` whose sampled flag was set got recorded in full, bypassing `INTERNAL_OTEL_TRACE_SAMPLING_RATE` entirely. Because the SDK propagates its (always-sampled) trace context on calls back to the platform from inside running tasks, the large majority of API server spans inherited a sampled parent and ignored the divisor. The sampling knob was effectively inert on the busiest service. This registers a custom propagator (`NonInheritingTraceContextPropagator`) that stops adopting the inbound trace as the parent: - `inject` still delegates to the standard W3C trace + baggage propagators, so outbound propagation is unchanged. - `extract` drops the parent span (`trace.deleteSpan`) while preserving baggage, so every incoming request roots its own trace and the ratio sampler applies uniformly. `remoteParentSampled` is also set to the ratio sampler as a belt-and-suspenders fallback, in case an inbound sampled parent ever reaches the sampler another way. Two effects: the divisor becomes effective on the API server, and the API no longer stitches onto (and inflates) the propagated task-run traces, which is where the very large, un-thinnable trace chains came from. Rooting each request removes those chains rather than only diluting them. Only the internal APM trace pipeline (`INTERNAL_OTEL_TRACE_EXPORTER_URL`) is affected. The user-facing run-trace pipeline (`otel.v1.traces` -> ClickHouse) is a separate path and is untouched. The only consumer of the global propagator's `extract` is the OTel HTTP/Express auto-instrumentation, so the blast radius is inbound-request trace shape. ## Evidence (local full-stack red/green, divisor 10) A local OTLP/JSON sink counting spans; a driver fires N requests at a real endpoint, each carrying a distinct sampled `traceparent`, then counts how many spans/traces carry that run's marker. | run | code | sent | kept traces | kept fraction | | --- | --- | --- | --- | --- | | before | unmodified | 500 | 500 | 1.00 | | after | this PR | 500 | 67 | 0.134 | | after | this PR | 2000 | 213 | 0.1065 | Before: 100% of inherited-sampled requests kept, divisor ignored. After: ~10% kept (the divisor), converging on it at larger N. In every after-run each kept request is a single self-rooted trace (kept spans == kept distinct traces), confirming the inherited chains are gone, not just thinned. `typecheck` passes. ## Rollout / rollback No flag. Behavior stays governed by the existing `INTERNAL_OTEL_TRACE_SAMPLING_RATE`. Rollback is a straight revert with no data migration. ## Notes Internal dashboards that count raw span or request volume from this pipeline will read lower once this ships. That is expected: those counts were inflated by the bypass, not a real drop in traffic. Latency/percentile monitors retain plenty of samples at the current divisor. refs TRI-13031 |
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Chris Arderne
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0a44b88b39 | fix: security release 2026-07-21 (#4528) | ||
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Eric Allam
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db67a856fe |
perf(webapp,database): index the newest-task-version lookup (#4518)
📦 Preview packages (pkg.pr.new) / Build and publish previews (push) Has been cancelled
📚 Publish docs / publish (push) Has been cancelled
Implementing PlanetScale Insights improvement. ## Summary Validating a schedule (creating or updating one through the API or the dashboard, and deploying a project that declares schedules) looks up the newest version of a task by slug. That lookup reads *every* version of the task and sorts them to return one. A project gains a row per task on every deploy, so the work grows with the project's age: the oldest projects pay the most, and dev-mode redeploys make it worse. This was picked because it was the largest single consumer of database time on the schedules path, and the fix is a sort key with no index behind it. ## Fix `BackgroundWorkerTask` is indexed on `(projectId, slug)`, which serves the equality but not the `ORDER BY createdAt DESC`. Postgres seeks the index, then bitmap-scans and top-N sorts the whole group to produce a single row. Adding `createdAt` to the index lets it scan backward and stop at the first row. The same call site also selected all 21 columns, including five JSON blobs, to read one field (`triggerSource`), so it now selects that field alone. ## Benchmark Local Postgres 17, 997,000 seeded rows / 748 MB, group sizes chosen to match the distribution seen in production. | Group size | Before | After | | --- | --- | --- | | 15,000 versions of one task | 11.118 ms, 1,510 buffers, 15,000 rows scanned | 0.027 ms, 4 buffers, 1 row | | 2,000 versions of one task | 2.081 ms, 1,455 buffers, 2,000 rows scanned | 0.022 ms, 4 buffers, 1 row | ``` before: Limit -> Sort (top-N heapsort) -> Bitmap Heap Scan after: Limit -> Index Scan Backward using BackgroundWorkerTask_projectId_slug_createdAt_idx ``` An ascending index scanned backward is enough here, so no descending index is needed. ## Impact and risk Real-world gain lands between the two rows above and scales with how many deploys a project has accumulated. Projects with few deploys will see little change, since there is barely anything to sort. The new index costs noticeably more than the existing two-column one: 43 MB against 7.3 MB on the benchmark rig. Adding `createdAt` makes every key unique, which defeats btree deduplication, so this is a real disk and write cost rather than a rounding error. Writes to this table happen at deploy time, not on the run path, so the write amplification is acceptable. The existing `(projectId, slug)` index is now a redundant prefix and could be dropped, but this PR keeps it so index usage can be observed before removing it. Behavior is unchanged: same predicate, same ordering, same row returned. The narrowed select is the only code change, and the field it keeps is the only one the caller read. Deploy note: the migration is `20260806100000_add_background_worker_task_project_id_slug_created_at_index` and uses `CREATE INDEX CONCURRENTLY IF NOT EXISTS`, so it can be pre-applied by hand before the deploy. |
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Eric Allam
|
6c6e58e6ff |
perf(webapp): batch declarative schedule cleanup queries (#4522)
## Summary `syncDeclarativeSchedules` runs on every background-worker creation (every deploy, and every file save during `trigger dev`). It issued one instance-delete per declarative schedule the current worker no longer declares, in a loop, and the overwhelming majority of those deletes matched zero rows. This collapses the loop into at most two set-based statements and skips the instance delete entirely when the current environment owns no instance of the schedule. ## Why so many, and mostly no-op The loop runs once per entry in `missingSchedules`, which starts as every DECLARATIVE schedule for the whole project across all its environments (the query filters only by `projectId`). A schedule leaves that set only when a declared task matches it by `taskIdentifier` **and** the schedule already has an instance in the current environment. That last clause is the amplifier. When a task's schedule has no instance in the current environment, the create branch inserts a brand-new `TaskSchedule` row with an instance for this environment rather than adding an instance to the existing row. So the same scheduled task, once it has run in dev and been deployed to prod, exists as two separate schedule rows: one carrying a dev instance, one carrying a prod instance. On a dev worker sync of that project: - the dev-instance row matches the declared task and is removed from the set - the prod-instance row has the same `taskIdentifier` but no dev instance, so it stays in the set and gets `deleteMany(taskScheduleId = prodRow, environmentId = dev)`, which matches zero rows So every declarative task that has been synced in another environment contributes one guaranteed no-op delete per sync, and the count scales with (declarative tasks x environments), plus any leftover rows from renamed or removed tasks. A project does not need to have dropped a schedule to generate these; it just needs the same declarative tasks present in more than one environment, which is the normal develop-in-dev, deploy-to-prod case. ## Fix The candidate schedules are already loaded with their instances, so the branch is decided in memory: - schedules with no instances (or only current-environment instances) are removed in a single `taskSchedule.deleteMany` - schedules that still have another environment's instance have only the current environment's instance detached, in a single `taskScheduleInstance.deleteMany`, and only when such an instance actually exists Behavior is unchanged (cascade delete still removes the instances of a deleted schedule); the difference is statement count. A zero-row delete writes no WAL and creates no dead tuples, so the removed work was pure query and commit overhead. Verified with a testcontainer test (red before, green after) counting the emitted deletes across the no-op, batched-detach, and schedule-delete cases, and end to end through `trigger dev`: three declarative schedules created, surviving a re-sync, then two removed in a single batched delete with the third preserved. |
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Chris Arderne
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9409ddf9bc |
feat(webapp): add multiple environment API key management (#4390)
## Summary Projects can create, inspect, expire, and revoke multiple API keys for each environment. Plaintext values are shown only at creation; stored credentials are hashed and the API keys page displays only an obfuscated suffix afterward. Self-hosted installations support full-access additional keys by default. Authorization extensions can provide additional access presets and optional task selection. Additional keys can also mint scoped public access tokens through the Trigger.dev API without receiving the environment signing key. ## Feature notes - Only admin+ can create API keys (Developer can make in Development branch). - JWT self-signing will be a server call when used with new `_ak_` keys. - JWTs with long expiry can keep working even with api key deleted (gets priveleges from api key, signed with root key) - Unfiltered session listings intentionally preserve the existing broad task-read behavior. Filtered listings enforce task-level scopes for every requested task. - Buffered runs without a task identifier are not safely authorizable, so cancel/replay requests fail closed rather than resolving an unscoped run. - Batch and waitpoint endpoints intentionally return server-minted, narrowly scoped public tokens to all callers. These tokens have bounded lifetimes and may remain valid until expiry after API-key revocation. ## Deployment notes Deploy the management UI and public-token endpoint with new key creation disabled. Enable creation for selected organizations after the authentication path and released SDK have been verified, then expand availability gradually. Revoking an API key prevents new bearer requests and new token minting. Public tokens already minted by that key remain valid until their own expiration because they are signed by the environment signing key. ## TODO - [x] Add "Created by" to the key table - [x] Document that streamed batch ingestion is non-atomic and may partially accept items before a validation or authorization error. ## Follow-ups - [x] Add an organization-level feature flag for the API key management UI and creation action. - [x] Document rollout ordering: enable additional-key lookup before enabling issuance. - [x] Add a system-wide gate that can stop new key issuance without disabling authentication for existing keys. - [x] Replace the generic SDK compatibility warning with the first published compatible version. Old SDK will mint an unusable token if given an `_ak_` key. - [x] Add public documentation covering creation, storage, expiration, revocation, SDK compatibility, and public-token lifetime behavior. - [x] Add observability for key creation, revocation, policy preparation failures, and public-token mint failures. - [ ] Exercise create, copy-once display, authenticate, mint, expire, and revoke flows end to end before broad enablement. |
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Eric Allam
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771937adf5 |
fix(webapp): clamp run priority so a large value can't fail run creation (#4512)
## Summary Triggering a run with a very large `priority` could fail run creation outright with an opaque database error. `priority` is multiplied by 1000 and stored in a 32-bit integer column, with nothing bounding it, so a big enough value overflowed the column and the create failed. The trigger now caps the value to the highest supported priority instead of erroring, so the run is still created. ## Fix `priorityMs` (the stored `priority * 1000`) now goes through a `clampPriorityMs` helper before the write. It rounds to a whole number and clamps into the column range at both ends, so only a valid integer ever reaches the column and an out-of-range priority caps rather than failing. Single and batch triggers share the write path, so both are covered. |
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Katia Bulatova
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fbd6df33b4 |
feat(webapp): Themes + contrast settings update (#4206)
Adds System Preferences, Dark and Light themes, gated by the `hasThemeSwitcher` feature flag (off by default — dark stays the default theme for everyone). Old theme is now "Classic"and set as default. "System preferences" theme has both Light and Dark modes and uses your laptop settings to use a correct one. It has less color accents (specifically less colored text), and they are the same for both modes, only grayscale values change between them. And Light/Dark themes can be used separately. New Contrast setting is available for System Preferences, Dark and Light themes - it changes the contrast for the whole app. All new visual Settings live in Account. |
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Chris Arderne
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763b5dc582 |
feat(webapp): enforce scopes for environment API keys (#4389)
## Summary Environment API keys backed by the additional-key table can authenticate API requests using their stored effective scopes. Revoked and expired keys are rejected, branch environments retain their existing routing behavior, and last-used timestamps are updated on a throttled best-effort basis. ## Design API route builders receive the resolved ability and reject restricted keys on routes without an authorization declaration. Existing deployment, environment variable, queue, run, task, batch, session, and waitpoint routes declare the resources they access. Trigger and batch responses return server-signed public access tokens, so additional keys never need access to the environment signing secret. Root-key rotation also keeps public tokens valid for the existing grace window. ## Feature notes - Root environment keys remain unrestricted for backward compatibility. Additional keys enforce their persisted scopes and fail closed on routes without an authorization declaration. - Machine-key requests never exchange one credential for another. Additional keys cannot retrieve the root key, and rotated root keys are not upgraded during their grace window. - Public JWT validation remains host-owned, while installed RBAC plugins continue to supply root-key abilities. - Unfiltered session and run listings preserve existing broad task-read behavior. Filtered requests enforce the supplied task identifiers. - Related-run summaries remain embedded in run retrieval for API compatibility. Retrieving or mutating a related run independently still requires permission for that run. - Queue management authorizes at collection scope, matching the queue permissions currently issued. - Batch responses deliberately include server-signed public access tokens for all clients. Selected-task credentials continue using their original credential for per-item authorization. - Two-phase batches authorize declared task identifiers before creation and authorize every streamed item. Streaming paths that cannot declare the complete task set remain fail closed. - Authentication telemetry records successful credential resolution separately from subsequent resource-authorization failures. - API keys are high-entropy random tokens. SHA-256 is intentionally used for deterministic indexed lookup, not password hashing. ## Deployment notes The schema migration must be present before this code is deployed. Because bearer resolution runs on every authenticated request, deploy the resolver with additional-key lookup disabled, verify root-key and public-token parity, then enable lookup before any additional keys can be issued. The multi-task authorization tightening changes the result for narrowly scoped tokens that request tasks outside their grants. Observe would-deny results before enforcing that check. Request-idempotency keys are also newly isolated by environment and task, so a retry crossing the deployment boundary may execute once more before old cache entries expire. ## Follow-ups - [x] Add a system-wide kill switch for additional-key lookup, defaulted off for the initial deployment. - [x] Add authentication observability by credential kind, result, latency, and lookup path without recording credential values. - [ ] ~Add would-deny observability and an independent enforcement switch for multi-task authorization.~ - [ ] ~Add an independent switch for server-issued batch tokens while root-key parity is verified.~ - [ ] Confirm every API route reachable by a restricted key has an explicit authorization declaration or intentionally fails closed. - [x] Verify root-key rotation, revoked-key grace, and public-token validation through each bearer resolver path. |
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Eric Allam
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f9c8d518c7 | perf(webapp,run-engine,database): resolve the newest worker and deployment by createdAt (#4452) | ||
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Eric Allam
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0445b8ec27 |
fix(webapp,clickhouse): keep the rest of a ClickHouse batch when one run or span has un-ingestable JSON (#4358)
## Summary A single run output, trace span, or payload carrying JSON that ClickHouse can't ingest (for example nesting past its depth limit) used to fail the whole insert batch, so unrelated runs and spans silently disappeared from the runs list, traces, and logs. This keeps the rest of the batch and handles the offending row instead of dropping everything around it. ## Fix Recovery is per-table, matched to what each table needs: - **Runs** (`task_runs_v2`) keep their status. We follow ClickHouse's failing-row hint to strip just the un-ingestable JSON column(s) so the run still lands (its output reads from Postgres on the detail page), up to a configurable limit (`RUN_REPLICATION_MAX_POISON_STRIPS_PER_BATCH`, default `1`). Past the limit we stop and land the batch with `allow_errors` in a single pass, skipping the remainder. Cost stays a fixed handful of inserts no matter how large or poisoned a flush is. - **Trace events and payloads** (high volume, append-only) recover with a single `allow_errors` insert: the good rows land in one pass and only the un-ingestable rows are skipped. Before falling back, a lightweight sanitizer still repairs what it can losslessly (lone UTF-16 surrogates, out-of-range integers) so a repairable row lands in full. To read the failing-row hint we patch `@clickhouse/client-common`: its error parser truncates the server response and discards the `(at row N)` position, so the patch preserves the full text for the recovery path to read. |
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Eric Allam
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c72ebf9084 |
fix(webapp,run-engine): stop batchTriggerAndWait hanging when item streaming never completes (#4397)
## Summary `batchTriggerAndWait()` could leave a parent run waiting forever. The 2-phase batch API blocks the parent on the batch's waitpoint as soon as the batch is created, but the batch is only sealed at the end of item streaming. If streaming never completed, nothing sealed the batch, nothing completed the waitpoint, and the parent stayed suspended with no timeout and no way to recover. Supersedes #4016, which added the reaper alone. ## Fix Admission for item streaming was being decided twice. Batch creation passes its own rate limiter, which fixes `expectedCount` and blocks the parent, and then the item stream had to pass the general API limiter as well, competing with unrelated traffic. A second limiter could therefore veto work the first had already committed the parent to. Creation now mints a bounded grant that the item stream spends, so an admitted batch can finish streaming. The grant is capped per batch rather than exempting the path, and every failure mode (no grant, spent grant, unreachable store) falls back to the normal limiter. That makes stranding much rarer but not impossible, since a request timeout or a crash can still end streaming for good. So a seal-timeout reaper aborts any batch still unsealed after `BATCH_SEAL_TIMEOUT_MS` and completes the parent's waitpoint with an error, letting `batchTriggerAndWait()` reject instead of hang. It is race-safe against a late seal, and it is only scheduled for batches that actually block a parent, so fire-and-forget batches cost nothing. Finally, the batches page used to report "Batch completion checked." for these batches while doing nothing, because the completion path returns early on an unsealed batch. It now says the batch cannot be resumed. Rate limiting is no longer the reason a batch strands, so the reaper's default stays at 30 minutes, comfortably above the SDK's worst-case stream-retry budget. ## Verification Unit and container tests cover the grant cap, the bypass ordering (it runs after the authorization check, so it can never skip authentication), and the reaper's abort, seal race, idempotency, and no-waitpoint cases. Also verified end-to-end against a running stack. With the general limit exhausted, batch creation and other API calls returned 429 while a granted batch still streamed and sealed; an ungranted batch id was rate limited rather than bypassed; and the grant cut off exactly at its configured attempt count. Reproducing the stranded state on a real parent run, the batch was aborted at the timeout, the waitpoint completed with an error, and the parent resumed and finished instead of hanging. A parentless batch left unsealed was untouched well past the reaper window. ## Verified against deployed runs The reaper was proven end to end with a real deployed run (locally-run supervisor, containerised run) and a real network fault, rather than a simulated one: toxiproxy severs the phase 2 item stream mid-flight so every SDK stream retry genuinely fails, while phase 1 still succeeds. Only the batch calls traverse the fault, so control-plane traffic is untouched. The reproduction is the shape that actually strands a parent: the task catches the `BatchTriggerError` the SDK throws and carries on, so the phase 1 block outlives the thrown error and the parent hangs at its next suspension point. With the reaper disabled, the parent sat in `EXECUTING_WITH_WAITPOINTS` for over 24 minutes holding two blockers, and stayed stuck across a full infrastructure restart: ``` type | status | has_timeout BATCH | PENDING | f <- orphan, completedAfter NULL DATETIME | COMPLETED | t <- the wait already elapsed ``` With the reaper enabled the same task under the same fault completed in about 75 seconds with zero blockers left, the batch `ABORTED`, and its waitpoint completed carrying the error. Two conditions are required to observe this at all, which is worth knowing for any future test: the run must be deployed rather than `trigger dev` (dev runs execute in process and finish while still holding blocker rows), and the wait after the caught error must exceed the checkpoint threshold, or it is served in process and never suspends. ### Why completing the batch waitpoint is sufficient `batchTriggerAndWait` runs create, then stream, then wait. A phase 2 failure throws before the wait is ever reached, and the reaper only fires on an unsealed batch, so the parent is never suspended awaiting the batch when it runs. The parent therefore does not need a synthetic result, only to stop being blocked. Note this reasoning depends on that ordering: if the wait were ever reached with an unsealed batch, completing the batch waitpoint alone would not settle the caller. ## Follow-ups - Batches stranded before this ships still need a one-off recovery; the reaper only schedules at creation time. - That same property leaves a gap if the process dies between creating the batch and scheduling the job. A periodic sweep would close it, but wants a supporting index. - When a partially streamed batch aborts, children already enqueued keep running while the parent fails. Left as-is deliberately, since cancelling triggered work is a bigger semantic call. |
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Oskar Otwinowski
|
4efe0a07c4 |
fix(webapp): create dev environments for SSO and Directory Sync members (#4426)
Members added by SSO just-in-time provisioning or Directory Sync never got their per-member DEVELOPMENT environments - only invite acceptance and project creation created them. `trigger dev` returned "Environment not found" for those members and the dashboard had no dev view. ensureOrgMember now queues provisioning for every membership it settles, so both paths are covered and members missing environments are repaired on their next sync. Provisioning runs as a common-worker job to keep sign-in and directory webhooks off the per-project write loop. A failed enqueue surfaces for Directory Sync, whose worker retries the idempotent effect, and is swallowed for sign-in, where the next login enqueues again. Environment creation now tolerates a concurrent creator so the project-creation loop and the job cannot collide on the unique index. Also fixes environment resolution ignoring dev-environment ownership: a member without their own dev environment could be handed a colleague's and have it persisted as their dashboard preference. |
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Iss
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d90f06ba5e |
feat(webapp): migrate Plain to @team-plain/graphql + attribute support threads to org tenant (#4368)
## What
Two changes, shipped together:
1. **SDK migration (TRI-12460).** `@team-plain/typescript-sdk` is
deprecated. Move the webapp to its successors — `@team-plain/graphql`
(client) and `@team-plain/ui-components` (`uiComponent` builder).
Behaviour-preserving: the `PlainClient` customer upsert + thread
creation move to the new `client.mutation.*({ input })` shape; the
client now throws on failure, so `sendToPlain` wraps its calls and logs,
staying best-effort.
2. **Org tenant attribution (TRI-12461).** When org context is
available, `sendToPlain` now upserts a Plain tenant keyed by `externalId
= org_id`, links the customer to it, and stamps the created thread with
that tenant — so support threads become attributable to a Trigger.dev
org. Wired into the four add-on quota requests and the plan-cancellation
feedback (which already have org context). The tenant steps are isolated
in their own try/catch and the thread's `tenantIdentifier` is gated on
their success, so a tenant failure never blocks thread creation.
## Not affected
- `customer.externalId` stays `User.id` — the customer cards +
impersonation link are unchanged.
- No ticket content leaves Plain.
- Callers without a single org (e.g. the feedback widget) are unchanged
— the org params are optional.
## Deploy prerequisite
The webapp's Plain API key needs three **new** scopes for attribution to
work (it already has `customer:create`, `customer:edit`,
`thread:create`):
- [x] `tenant:create`
- [x] `tenant:edit`
- [x] `customerTenantMembership:create`
Until granted, nothing breaks — `sendToPlain` logs the forbidden error
and creates the thread without attribution.
## Testing
- `pnpm typecheck --filter webapp` passes; oxfmt + oxlint clean.
- Ran the real `sendToPlain` end-to-end via a throwaway vitest harness
against live Plain — confirmed the code path executes; the live write is
gated only by the key scopes above.
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Eric Allam
|
6e5f0f0fe7 |
fix(webapp,clickhouse): stop invalid customer queries alerting, and isolate Sentry scope per request (#4372)
## Summary
A query sent to the query API with a typo in it, like a column name that
does not exist, was being reported as a server error. That put customer
SQL mistakes into our error alerting, where they made up almost all of
the volume on one of our noisiest alerts, and it drowned out the
failures that are actually ours to fix. This makes the level match who
is at fault, and fixes two related problems found alongside it.
## Invalid queries are the caller's, not ours
The query API route already got this right. It checks for `QueryError`,
logs at warn, and returns a 400, with a comment saying the system
handles it gracefully and no alert is needed.
The layer underneath ignored that. `executeTSQL` logged every exception
out of its catch block at error, including the compile failures the
route was about to turn into a 400, and error-level logs are forwarded
to error reporting.
The TSQL package already draws the line we need:
```ts
export class ExposedTSQLError extends BaseTSQLError {
/** An exception that can be exposed to the user. */
}
export class InternalTSQLError extends BaseTSQLError {
/** An internal exception in the TSQL engine. */
}
```
`SyntaxError` and `QueryError` extend the first. So the catch block now
branches on `ExposedTSQLError` and logs those at warn, keeping error for
`InternalTSQLError` and anything unanticipated, which is a genuine
compiler bug.
## SQL the caller wrote is their mistake, not ours
The same asymmetry showed up one level down. A query that compiles fine
can still be rejected by ClickHouse at execution, and most of those
rejections mean the caller's SQL is wrong rather than that we generated
something bad.
This is where the volume actually is. Checking production, one error
group alone, a missing `GROUP BY` on the public query API
(`NOT_AN_AGGREGATE`), accounts for over a million events across hundreds
of users. It is by far the largest error group in the project, and
classifying only by resource limit would have left every one of those at
error level.
So rejections are split three ways in `ClickhouseClient`, which is the
only place holding the parsed `ClickHouseError` and its symbolic type.
By the time the error reaches `executeTSQL` it has been wrapped and the
type is gone, and the type never appears in the message text, so it
cannot be recovered by string matching.
- **Resource limits** (memory ceiling, timeout, row/byte caps) log at
warn. The query is valid, it just asked for more than it is allowed to
spend.
- **Invalid SQL** (`NOT_AN_AGGREGATE`, `UNKNOWN_IDENTIFIER`,
`SYNTAX_ERROR`, the type and parse families) logs at warn **only when
the caller wrote the SQL**.
- **Everything else** keeps alerting.
That gate matters. The client is shared, so the identical rejection on
TRQL *we* generated is our bug and has to stay at error. Callers opt in
with `userAuthoredQuery`:
| caller | who wrote the SQL | opts in |
| --- | --- | --- |
| public query API | the customer | yes |
| query editor | the customer | yes |
| agent charts | the agent's model | yes |
| built-in dashboard tiles | us, in code | no |
| queue metric cards | us, in code | no |
| health report | us, in code | no |
The agent is the one judgement call. Its TRQL is not typed by a person,
but it is also not something a code fix makes correct, so a query it
gets wrong is not worth waking anyone for. The same endpoint serves
built-in tiles whose TRQL we do write, so the opt-in lives with the
caller rather than the route.
Separately, when one of these queries did fail, the log recorded the
generated ClickHouse SQL but not the query the caller actually wrote,
which made the reports hard to act on. `queryWithStats` takes an
optional `logFields` that `executeTSQL` uses to attach the original
TSQL.
## Events were attributed to the wrong request
Chasing the above turned up something broader: only a tenth of the
events on that alert pointed at the query API. The rest were pinned to
unrelated requests that happened to be in flight at the same time, so
the alert looked like the trigger endpoint was failing.
`Sentry.init` runs with `skipOpenTelemetrySetup: true`, because we
register our own OTel pipeline. That skips `initOpenTelemetry`, and one
of the things it does is:
```js
api.context.setGlobalContextManager(new SentryContextManager());
```
The async-context strategy is still installed, but `withIsolationScope`
only marks the OTel context and delegates the actual fork to that
context manager:
```js
// "We depend on the otelContextManager to handle the context/hub"
return api.context.with(ctx.setValue(SENTRY_FORK_ISOLATION_SCOPE_CONTEXT_KEY, true), ...)
```
`provider.register()` installed a plain
`AsyncLocalStorageContextManager`, which does not know that key. The
lookup found no scopes on the context and fell back to the
process-global default isolation scope, so every request wrote its
request data into the same object and the last writer won.
The tracer now registers `SentryContextManager`, which subclasses
`AsyncLocalStorageContextManager`, so OTel behaviour is unchanged. It is
also registered on the path where tracing is disabled, which previously
never called `register()` at all and so had no context manager of its
own.
Tenant tags were always correct, because those come from our own async
local storage rather than the isolation scope. That is why the
attribution being wrong was not obvious.
This affects every error report the webapp sends, not just the query
API.
## Verification
`internal-packages/clickhouse`: 76 tests pass, including eight covering
each level decision against a real ClickHouse container. Three pairs pin
the gate open and shut at both layers: an invalid query, a compile
failure, and a real limit breach driven with `max_rows_to_read` each log
at warn with `userAuthoredQuery` and at error without it.
The isolation fix has a test that reproduces the leak before asserting
the fix. Two overlapping requests each tag their own isolation scope;
with the plain context manager the slower one reads back the other's
tag, and with `SentryContextManager` each reads back its own.
Measured separately against a faithful reproduction of the server's
wiring (own OTel pipeline, CommonJS entry) at 200 concurrent requests:
per-request attribution goes from 0.5% to 100%, while span nesting,
context propagation across awaits, and distinct trace IDs are identical
before and after.
|
||
|
Chris Arderne
|
8ebc8a41af | fix(webapp,redis-worker): stop logging raw metadata, alert payloads, and job items (#4403) | ||
|
Chris Arderne
|
a09817169f | fix(webapp): stop logging full batch item contents in batchTriggerV3 (#4404) | ||
|
Eric Allam
|
4eb9292cbe |
feat(webapp,run-engine): queue metrics and health dashboard (#4131)
## Summary
Three related changes, each independently gated:
**Queue metrics and health.** Per-queue depth, throughput (enqueued,
started, completed), concurrency, whether a queue is throttled, and
scheduling delay (how long a run waits between becoming eligible and
actually starting), plus a per concurrency-key breakdown for keyed
queues. Collected from inside the run queue itself, stored in
ClickHouse, and surfaced on the Queues list, a new per-queue detail
page, the task pages, and the run inspector. The question it answers is
"does this queue have enough concurrency to keep up, and if not, which
key or which limit is the constraint".
**Percent-based queue concurrency limits.** A queue's concurrency
override can now be expressed as a percentage of the environment limit,
stored as the source of truth and re-materialized whenever the
environment limit changes. Absolute overrides above the environment
limit are now **rejected with a 400** instead of being silently capped,
which is a behavior change on `POST
/api/v1/queues/:queue/concurrency/override`.
**The `health` report.** A server-computed verdict on whether work is
flowing, whether the runs that do start are healthy, and whether
telemetry is fresh, rendered as text with sparklines. Available as `GET
/api/v1/reports/:key`, `trigger report`, and the `get_report` MCP tool
(plus a `report` MCP prompt, which shows up as a slash command in hosts
that support prompts).
With the flags off, the Queues page renders the pre-metrics component
verbatim, nothing is emitted, and nothing is written to ClickHouse.
## Configuration
Two independent gates, on purpose. Emission is global so data accrues
for everyone before anyone can look at it; the view is per organization
so it can be turned on for one org at a time without a deploy.
**Runtime flags (no restart)**
| Flag | Store | Gates |
| --- | --- | --- |
| `queue_metrics:enabled` | run-queue Redis key (`"1"`/`"0"`, off by
default) | All emission, gauges and counters. Cached in-process for 10s
with stale-while-revalidate, warmed eagerly at boot so the first op
after a deploy is not dropped. |
| `queue_metrics:gauge_sample_rate` | run-queue Redis key, `0..1` |
Fraction of queue ops that emit a gauge. Counters are never sampled, so
throughput stays exact at any rate. |
| `queueMetricsUiEnabled` | feature-flag catalog: global `FeatureFlag`
row, per-org `Organization.featureFlags` override wins | Whether an org
sees the metrics view at all: the Queues list variant, the queue detail
route, the built-in Queues dashboard, the concurrency-keys endpoint, and
the metrics blocks on task pages and the run inspector. Off by default;
a gated org gets a 404 on the detail route rather than an empty page. |
Both Redis keys are readable and writable from `/admin/queue-metrics`
(super-admin UI, with a live per-shard stream-health table) and
`GET`/`POST /admin/api/v1/queue-metrics` (admin PAT). The admin surface
uses its own Redis client, so it works on any instance regardless of
whether that instance runs the emitter or the consumer.
**Environment variables (boot time)**
| Variable | Default | Notes |
| --- | --- | --- |
| `QUEUE_METRICS_EMIT_ENABLED` | `0` | Constructs the emitter and
injects it into the run engine. Without it the run queue has no emitter
at all. |
| `QUEUE_METRICS_CONSUMER_ENABLED` | `0` | Boots the stream consumer on
this instance. Independent of emission, so consumers can be sized
separately from the API. |
| `QUEUE_METRICS_STREAM_SHARD_COUNT` | `4` | Stream shards, hashed per
queue. |
| `QUEUE_METRICS_CONSUMER_BATCH_SIZE` | `1000` | Poll batch equals
insert batch, so an ack can never outrun a write. |
| `QUEUE_METRICS_REDIS_{HOST,PORT,USERNAME,PASSWORD,TLS_DISABLED}` |
falls back to the run-queue Redis | Set `HOST` to move the metrics
stream onto a dedicated instance so a metrics backlog cannot compete
with the run queue for memory. Self-hosters can leave it unset and get a
single-Redis deployment. |
| `QUEUE_METRICS_COUNTER_STREAM_MAXLEN` | `2000000` shared, `8000000`
dedicated | Bound on how much a stalled consumer can hold. The default
is deliberately lower when the stream shares the queue-critical Redis. |
| `QUEUE_METRICS_COUNTER_ODOMETER_TTL_SECONDS` | `604800` | TTL on the
per-queue cumulative counter key, refreshed on every write, so only
queues idle for the whole window are purged. |
| `QUEUE_METRICS_MAX_QUEUE_NAMES_PER_ENV` | `1000` | Distinct queue
names tracked per environment; overflow collapses into `__overflow__`. |
| `QUEUE_METRICS_MAX_CONCURRENCY_KEYS_PER_QUEUE` | `10000` | Same idea
one level down, per queue. |
| `QUEUE_METRICS_GAUGE_SAMPLE_RATE` | `1` | Default for the live
sample-rate key above. |
| `QUEUE_METRICS_QUERY_TABLES_VISIBLE` | `0` | Lists the queue-metrics
tables in the Query page, its schema docs, the schema API and the AI
query context. Off keeps them unlisted while the feature is dark; a
query naming them still runs either way. |
| `QUEUE_METRICS_CLICKHOUSE_URL` | falls back to the shared wiring |
Runs queue metrics on their own ClickHouse service: the consumer's
inserts and every queue-metrics read go through it, so a metrics-heavy
chart refresh never competes with runs-list or trace reads. Unset
reproduces the previous split exactly (inserts on `CLICKHOUSE_URL`,
reads on the query pool). |
| `QUEUE_METRICS_CLICKHOUSE_READER_URL` | the write URL | Reader split,
so the consumer's inserts can never land on a read endpoint. |
|
`QUEUE_METRICS_CLICKHOUSE_{KEEP_ALIVE_ENABLED,KEEP_ALIVE_IDLE_SOCKET_TTL_MS,MAX_OPEN_CONNECTIONS,LOG_LEVEL,COMPRESSION_REQUEST}`
| `1`, unset, `10`, `info`, `1` | Pool tuning, matching the other
per-workload ClickHouse clients. |
Migrations to apply: ClickHouse `036_create_queue_metrics_v1.sql`, and a
Postgres migration adding the nullable
`TaskQueue.concurrencyLimitOverridePercent`. Both are additive.
## How collection works
Queue operations produce two kinds of signal, and they have opposite
failure modes, so they are handled differently.
**Gauges** (queued, running, queue limit, env queued, env running, env
limit, throttled, plus keys-with-backlog and worst-key wait on keyed
queues) are read *inside* the same Redis script that performs the
enqueue or dequeue, so the reading is atomic with the operation it
describes rather than a racy follow-up read. The script returns them on
its reply and the app forwards them to the stream. Gauges are sampled
and drop-tolerant: they are aggregated with `max`, so a lost reading
costs resolution, never correctness.
**Counters** (enqueued, started, completed, plus nack and dead-lettered)
are cumulative odometers. Each event increments a per-queue key on the
metrics Redis and emits the absolute total, and ClickHouse takes the
difference across buckets at read time. This is the important property
of the design: a summed-delta counter undercounts permanently on any
lost event, while a cumulative one self-heals, because the next
surviving reading restates the whole total. Only bucket granularity can
be lost, never the total. A queue returning after its odometer TTL
expired restarts at 1 and reset detection handles it, which is safe
precisely because expiry only spans a window with no activity.
Both land on one sharded Redis stream. A consumer reads it with a
consumer group, reclaims stale pending entries on a 15s interval rather
than on every poll, maps one entry to one or two ClickHouse rows
(whole-queue and, for keyed queues, per-key), and acks only after the
insert lands. Each batch carries a dedup token derived from its
stream-entry ids, and the target tables set
`non_replicated_deduplication_window`, so a retried batch cannot
double-count either the raw rows or the aggregates that hang off them.
Consumer and emitter both emit OTel metrics
(`queue_metrics.emitter.emitted`,
`queue_metrics.consumer.{entries,rows_inserted,insert_errors,insert_duration,stream_depth,group_lag,pending,lag_unknown}`);
stream depth and group lag are the two worth alerting on, and
`lag_unknown` exists because Redis can report a null lag after a trim,
which must not be read as zero.
## Storage and read path
`queue_metrics_raw_v1` is a short landing table with a 6 hour TTL. Four
aggregate tiers are materialized straight from raw, never cascaded off
each other, each with a 30 day TTL:
- `queue_metrics_v1`, 10 second buckets per queue, the default read path
- `queue_metrics_5m_v1`, 5 minute buckets per queue, for wide ranges and
cross-queue ranking
- `env_metrics_v1`, 10 second buckets per environment, queue-independent
so it stays cheap at any range
- `queue_metrics_ck_v1`, 10 second buckets per concurrency key
Every tier is an MV from raw because the counter states do not survive a
cascade: their merge is order sensitive, so a `-MergeState` chain off
the 10s table inflates the result, and the same property means an
aggregate state may only be merged inside one queue. That constraint is
now enforced by the query engine rather than by reviewer discipline: a
column can declare a `mergeGroupKey`, and any query that references it
without grouping by, or pinning to a single value of, every named key
fails to compile with an actionable message.
On the read side, TRQL gains three tables (`queue_metrics`,
`env_metrics`, and a `queue_metrics_by_key` that is hidden from the
editor, schema docs and schema API but still queryable, so per-key rows
can never silently merge into a plain per-queue query), plus
`deltaSumTimestampMerge` and `quantilesTDigestMerge`. Two schema-level
optimizations ride along: a table can declare coarser rollups, so a
query whose bucket interval is 5 minutes or wider is routed to the 5m
table with no change to the query itself, and it can opt into the
ClickHouse query cache with time bounds floored to a fixed grid, so the
auto-refreshing dashboards actually share cache entries instead of
missing on every tick. Both are caller-side substitutions, so the
printer stays unaware of physical layout.
All of this can also live on its own ClickHouse service. A table
declares the pool its reads run on, the three queue-metrics tables name
the dedicated one, and the ingestion consumer writes through the same
client, so both directions move together with one env var and nothing
else routes differently.
The other engine change is opt-in gap filling: charts can request rows
for empty buckets, where counters zero-fill and gauges carry forward.
Grouped gauge series are densified per group and carried inside a
partition, so a quiet queue's line holds its last value without bleeding
another queue's value into it.
## Queue concurrency limits
`concurrencyLimitOverridePercent` on `TaskQueue` is the source of truth
when an override is set as a percentage; the absolute `concurrencyLimit`
is materialized from it (floored, clamped to at least 1 so a percentage
can never act as a pause, and never above the environment limit). Every
path that changes an environment limit now recalculates the
environment's percent-based overrides afterwards, outside the
transaction, and pushes changed limits to the engine. The push is
attempted even when the stored value did not change, so a previously
failed sync self-heals rather than leaving the database and the engine
diverged; paused queues are skipped so a recalculation cannot
effectively unpause one.
The API accepts exactly one of `concurrencyLimit` or `percent`, and the
reject-instead-of-clamp change above means a request asking for more
than the environment allows now fails loudly. The percent bound (greater
than 0, at most 100) is defined once and shared by the zod schema, the
dashboard mutation handler and the service, so the three cannot drift.
The concurrency-keys table on a queue is now paginated against the
ClickHouse per-key tier, ranked by peak backlog with the total on every
row from a single scan, and only the keys on the current page are
enriched with live counts from Redis. That replaces a hard top-50 cap
with something whose cost is a function of page size rather than key
cardinality.
## The health report
`GET /api/v1/reports/:key?period=&format=markdown|ansi|json`. The
verdict is computed on the server and is deterministic, not
model-generated. Three independent analyzers run over one input
snapshot: flow (is work moving, and if not, is the cause a limit,
throttling, one bad queue, or dead-lettering), execution (are the runs
that start succeeding, and at what latency), and liveness (how fresh is
the telemetry). When telemetry is genuinely stale, the first two are
forced to unknown and every actionable field is stripped, so no surface
ever advises action off stale data.
Authorization is per query table rather than a blanket query grant: a
JWT must be scoped to every table the report reads (`runs`,
`env_metrics`, `queue_metrics`), so a narrowly scoped token cannot pull
a report that reads more than it was granted. `period` is validated as a
shorthand with a 90 day ceiling at the edge. The report catalog is a
registry of `{ load, interpret }` entries, so the next report is a new
entry and no change to the route, the view model, the renderers, the CLI
or the MCP tool.
`trigger mcp` no longer launches the install wizard when stdout is a
TTY, which fixed a real failure: hosts spawn the server over a PTY, so
the wizard would open and the client would time out waiting for a server
that never started. The wizard now needs `trigger mcp --install`.
## The part that is live regardless of every flag
The enqueue and dequeue scripts now return a 2-tuple so a gauge reading
can ride back on the reply. Every return site in the eight affected
scripts is wrapped, and a `nil` original is converted to `false` on the
way out, because a raw `nil` in the first slot would make Lua truncate
the multi-bulk reply and silently drop the gauge on the throttled and
empty-queue paths. The reply shape and the destructuring on the app side
are exercised on every queue operation whether or not metrics are
enabled, so that is the part of `run-engine` worth the closest review.
One behavior fix in the same area: the scheduling-delay anchor is set
only on a run's first entry into the queue. Anchoring it to trigger time
on re-enqueues made waitpoint and checkpoint resumes report the entire
wait as scheduling delay. Queue ordering is untouched, so a re-enqueued
run keeps its position, and nacks deliberately keep the original anchor
because a rolled-back dequeue is the same continuous wait.
A pending-version promotion still anchors to trigger time, on purpose:
that promotion is the run's first real entry into the queue, since the
trigger deliberately held it back waiting for a worker version, and the
TTL is armed at the same point for the same reason. The consequence is
worth naming, because it is a judgement call: a run that waits on a
deployment reports that wait as scheduling delay on its queue, which is
time unrelated to queue capacity.
## Verification
Unit and integration suites across the new package, the run queue, the
mapping layer, the query engine and ClickHouse (including a test that
applies migration 036 through the same splitter CI uses, and a
regression test that inserts the same batch three times to prove the
aggregates do not inflate). Beyond that, the whole path was driven end
to end against a live stack with real runs: emitter to Redis stream to
consumer to ClickHouse to the dashboards, for both the local dev path
and the deployed path where a supervisor drives the dequeue, with
assertions on exact counter reconstruction per queue and per concurrency
key, throttling, environment saturation, scheduling delay, and a
deliberate mid-stream reading drop to confirm the cumulative counters
still reconstruct the correct total. The gated-off state was checked on
every touched surface.
The dedicated ClickHouse service was verified against a second,
separately-schema'd instance: with it configured, the driven counters
reconstruct exactly on the dedicated instance, the shared instance gains
no rows for that window, a read through the query API returns the value
that exists only on the dedicated instance, and a `runs` query still
succeeds (it would fail outright if it were mis-routed to a service
without that table). With the variable unset, the full suite passes
unchanged.
---------
Co-authored-by: Katia Bulatova <katia@trigger.dev>
Co-authored-by: Katia Bulatova <katherine.bulatova@gmail.com>
Co-authored-by: James Ritchie <james@trigger.dev>
|
||
|
Saadi Myftija
|
44eca4d166 |
feat(webapp): org-gated internal API origin in run env vars (#4366)
Adds an opt-in way for operators to route deployed runs' API traffic through a different origin than the public one, per organization. Set `INTERNAL_API_ORIGIN` on the webapp and enable the `internalApiOriginEnabled` feature flag (globally or per org, with the org override winning in both directions): deployed runs for enabled orgs then get `TRIGGER_API_URL` set to the internal origin instead of `API_ORIGIN`. Useful for gradually moving run traffic onto a private network path. ## Design The origin is resolved when an attempt starts, so flag changes take effect on the next attempt and roll back the same way, with no task redeploys. The org override is read fresh per attempt; the global default comes from the cached flags registry (a cold read fails safe to the public origin). When `INTERNAL_API_ORIGIN` is unset the flag is a no-op and no extra queries run, so existing deployments are unaffected. Dev runs always use the public origin, and `TRIGGER_STREAM_URL` remains unchanged. |
||
|
claude[bot]
|
ec562c0e68 |
fix(webapp): remove unused Electric sync trace routes (#4400)
<!-- ccr-slack-attribution --> _Requested by **Eric Allam** · [Slack thread](https://triggerdotdev.slack.com/archives/C0AU83M3136/p1785222101937829?thread_ts=1785207509.304669&cid=C0AU83M3136)_ Removes two dead Remix routes and the helpers only they used. `app/routes/sync.traces.runs.$traceId.ts` (`/sync/traces/runs/:traceId`) and `app/routes/sync.traces.$traceId.ts` (`/sync/traces/:traceId`) were added with the original ElectricSQL run page and lost their only consumers when the dashboard hooks that called them were deleted. Nothing in the repo references either route today. Also removed, because the deleted routes were their only callers: - `OtelTraceIdSchema`, `RESERVED_ELECTRIC_SHAPE_PARAMS`, `TraceScope`, `buildElectricTraceWhereClause` from `app/v3/electricShape.server.ts` (the file stays — `UNSAFE_REALTIME_TAG_CHARS` / `sanitizeRealtimeTagForSql` / `sanitizeRealtimeTagsForSql` are still used by `realtime.v1.runs.ts` and `realtimeClient.server.ts`) - the loader-specific cases in `apps/webapp/test/spanTraceRoutes.replicaLag.test.ts` and `internal-packages/run-store/src/runOpsStore.routesSpanTraceReadView.replicaLag.test.ts` `app/utils/longPollingFetch.ts` is untouched — `realtimeClient.server.ts` still uses it. `runOpsStore.ts` / `PostgresRunStore.ts` are untouched too; the unrouted-lookup mechanism there is generic and stays. As a plain code fact: the run lookup these loaders performed keyed on `TaskRun.traceId` alone, which is not an index-backed query shape. That is noted only as context for why the code is not worth keeping around unused. ### Judgement call worth a maintainer's opinion The request was specifically about `/sync/traces/runs/:traceId`, the route that looks up a run by `traceId`. This PR **also** deletes its sibling `/sync/traces/:traceId`. The reasoning: - both routes came in with the same ElectricSQL run-page work - both lost their only consumers in the same later commit - neither has any caller anywhere in the repo - they share the same helper module, so keeping one means keeping the helpers half-used If you would rather keep the sibling, reverting just that one file deletion is easy and does not affect the rest of this PR — say the word and I will restore it along with the helpers it needs. ## ✅ Checklist - [x] I have followed every step in the [contributing guide](https://github.com/triggerdotdev/trigger.dev/blob/main/CONTRIBUTING.md) - [x] The PR title follows the convention. - [x] I ran and tested the code works --- ## Testing Verification run locally from the repo root: | Command | Result | | --- | --- | | `pnpm run format` | clean, no changes produced | | `pnpm run lint:fix` | clean | | `pnpm run lint` | pass (exit 0, no findings) | | `pnpm run typecheck --filter webapp` | pass | | `pnpm run typecheck --filter @internal/run-store` | pass | A ripgrep sweep for `sync.traces`, `sync/traces`, `syncTraceRunsLoader`, `buildElectricTraceWhereClause`, `OtelTraceIdSchema` and `RESERVED_ELECTRIC_SHAPE_PARAMS` (excluding `node_modules`) returns zero hits. **Not fully verified:** both edited test files are testcontainers suites and need a Docker runtime, which was not available in my environment. I confirmed each file *collects* correctly with exactly the three intended remaining tests and no import errors — notably, dropping the `session.server` / `controlPlaneResolver.server` / `longPollingFetch` / `env.server` mocks does not break module loading for the surviving loaders. The assertions themselves then failed only on `Could not find a working container runtime strategy`. CI should be the real signal here. Per `apps/webapp/CLAUDE.md`, `pnpm run build --filter webapp` was deliberately not run. --- ## Changelog Removed two unused sync routes left over from the original ElectricSQL run page, along with the helpers and tests that existed only to serve them. No behaviour change — neither route had any caller. --- ## Screenshots _n/a — no user-visible surface changes._ 💯 --------- Co-authored-by: Claude <noreply@anthropic.com> |
||
|
Katia Bulatova
|
b3b1441df9 |
fix(webapp): guard workload auth gate metric against dev HMR re-registration (#4339)
Wraps the workload_auth_gate_total Counter in the singleton helper (same pattern as reloadingRegistry.server.ts) so a dev hot reload doesn't crash with "A metric with the name workload_auth_gate_total has already been registered". No production behavior change. |
||
|
Katia Bulatova
|
d05f1a7398 |
chore(webapp): migrate from Remix compiler to Vite (#4188)
Replaces Remix compiler with the Vite plugin. The Express server (cluster, socket.io, ws) and the Docker image contract are unchanged. |
||
|
Chris Arderne
|
6997aeb05e |
fix: security release 2026-07-08 (#4316)
⚒️ Publish Worker (v4) / build (supervisor) (push) Has been cancelled
|
||
|
Daniel Sutton
|
ae96b6c175 |
fix: read-your-writes + global-scope idempotency correctness under the run-ops split (#4284)
## What & why
Two related correctness fixes for the run-ops DB split. Under the split,
run-store reads can route to a **lagging read replica**; a just-written
run/waitpoint/batch can then be missed, causing a wrong decision.
**1. Read-your-writes → owning primary.** Surfaced first as an
intermittent `wait.until({ idempotencyKey })` re-wait on retry. Auditing
the run-store read surface found the same class at sibling sites (some
gating mutations or returning spurious 404s, others
tolerable/self-healing). Reads that must observe their own writes now
route to the owning **primary**
(`findRun`/`findWaitpoint`/`findBatchTaskRunByFriendlyId` →
`*OnPrimary`, a primary re-read on a miss, or a retryable 404 where the
SDK polls). Read-view reads stay on the replica. All additive — the
happy path is unchanged.
**2. Global-scope idempotency across the split.** A `global`-scope key
carries no per-run salt, so the same `(env, task, key)` triggered
concurrently from parents resident on **different** run-ops DBs could
dedup-miss on each DB and create a duplicate (the per-DB unique index
can't enforce cross-DB uniqueness). Such triggers (global scope, or
scope-absent, while split is active) are serialized through the existing
Redis idempotency claim, the loser resolves the winner by id across both
DBs, and the claim is reacquired on the expired/failed
clear-and-recreate path. `run`/`attempt` scope embed the run id and
never contend.
## Stacked for review
This is the **base** of a 2-PR stack, split so review is easier:
- **This PR** — production code only (34 files).
- **Stacked tests PR →
https://github.com/triggerdotdev/trigger.dev/pull/4285** — the
caller-driven guards (55 test files) on top of this branch.
## Validation
Local run-ops split, **both 2-DB and 3-DB**, fresh boot on this branch:
SDK canary 64/71 (only the known concurrency/input-streams/s3 failures),
quarantine sweep **0 unexpected** (340 pass / 16 known / 4 local) in
each topology, dashboard e2e 0 failed. No product regressions.
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Daniel Sutton
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285666290f |
ci(webapp): wire the run-ops legacy guard into CI and add oxlint residency fences (#4279)
## What - Runs `apps/webapp/scripts/runOpsLegacyGuard.ts --check` as its own PR job (`runops-guard`), so code that reaches a run-graph table through the control-plane Prisma client instead of the RunStore fails the build. - Adds a `trigger-runops` oxlint plugin with two fast, in-editor rules scoped to `apps/webapp/app`: one for direct `prisma.taskRun`-style access, one for a control-plane client wired into a read-through slot. These are the cheap fence; the guard is the type-aware gate. - Fixes `CancelTaskRunService.callV1`: historical V1 runs are legacy-resident, so its two finalize writes now go through `runOpsLegacyPrisma` instead of the control-plane client (they'd miss the row once legacy is a separate database). - Regenerates the guard baseline, which had drifted stale (it referenced files deleted in an earlier PR). ## Why The guard existed but ran nowhere, so its baseline rotted and a real residency gap (the V1 cancel writes) sat undetected. Wiring it into CI turns it into a ratchet against new control-plane run-graph access. ## Verification Local, against a clean regen: `oxfmt --check`, `oxlint .`, `guard --check`, and `typecheck --filter webapp` all pass. Remaining baseline entries are 4 batch-results router reads through type-opaque `as PrismaReplicaClient` casts (correct at runtime, accepted) + 2 sanctioned legacy annotations. |
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Daniel Sutton
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821972176d |
fix(run-store,webapp): correct split-database read routing, write residency, and batches list ordering (#4272)
## Summary Correctness and performance fixes for deployments that split run data across more than one database. Single-database / self-hosted deployments are unaffected (they collapse to a single read/write path). - **Batches list (dashboard):** for some organizations the Batches list could hide older batches or show them out of order. It now orders and paginates by creation time (with the id as a stable tiebreak), so every batch appears exactly once, newest first. The pagination cursor format changes; older in-flight cursors simply restart from the first page. - **Reads:** waitpoint and snapshot lookups that are keyed by a single run now read only the database that holds that run instead of querying both, removing redundant queries on hot paths (unblock, snapshot reads). - **Writes:** environment-scoped writes with no owning run (standalone wait tokens, waitpoint tags, idempotency-key resets) now land in the same database as that environment's runs, rather than defaulting to the other one. An idempotency-key reset also falls back to the other database when it matches nothing, so a reset still clears the key wherever the run actually lives. ## Notes Verified end-to-end against multi-database setups: run-keyed reads and env-scoped writes land on the correct database with no cross-database writes, and the batches list surfaces every batch in creation order. New tests cover the batches ordering/reachability and the write-residency routing. |
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Eric Allam
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1ab5066ed0 |
perf(webapp,run-store): point-lookup batch idempotency keys (#4255)
## Summary Batch triggers that use per-item idempotency keys could take seconds instead of milliseconds when the target task had a large run history. This keeps the idempotency lookup fast regardless of how many runs a task has accumulated. ## Root cause The batch path checks which items already have runs by looking up their idempotency keys with a single `WHERE runtimeEnvironmentId = ? AND taskIdentifier = ? AND idempotencyKey IN (...)` query. On a very large `TaskRun` table Postgres underestimates the row count of a specific `(environment, task)` pair, so once the `IN` list grows past a handful of keys it stops doing per-key index probes and instead scans every run for that `(environment, task)` and filters the keys in memory. The cost is then flat and large regardless of how many keys are being checked, and a routine `ANALYZE` does not correct the estimate at that table size. ## Fix Look each idempotency key up on its own, batched into a `UNION ALL` of point lookups (chunked, run with bounded concurrency). Each branch is an equality on all three columns of the unique index, so the planner can only do a per-key index probe and can never fall back to the range scan. Same results, same columns, confined to the batch trigger path. |
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Eric Allam
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29598a77b8 |
feat(webapp): add option to disable PostgreSQL task-event writes (#4242)
## Summary Adds `EVENT_REPOSITORY_POSTGRES_WRITES_DISABLED` (default off), which makes the task-event store skip all PostgreSQL `TaskEvent` writes. It's for deployments that store task events in ClickHouse (`EVENT_REPOSITORY_DEFAULT_STORE=clickhouse_v2`) and no longer want the PostgreSQL copy. ## How it works The guard sits at the single postgres write boundary, `TaskEventStore.create` / `createMany`, so it covers every write path (OTLP ingestion and run-lifecycle events) with one check. Reads are untouched (`findMany` / trace queries / streaming), so existing PostgreSQL events remain readable. Leave it off unless the default store is `clickhouse_v2`, otherwise task events for any run still routed to PostgreSQL would be dropped. |
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Daniel Sutton
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bea7e2be90 |
feat(webapp,run-store): route run-graph reads and writes through the run-store router (#4237)
## Summary Run-graph data (runs, batches, waitpoints, and their related tables) can now live in a database separate from the control plane, with every read and write routed to the correct database by each run's residency. This makes reading and writing run data more reliable once the two are split, and is a no-op for single-database installs. ## Design - Run-graph table access goes through the run-store router, which selects the legacy or the new run-ops store per run instead of assuming one shared client. - The legacy run-ops client is now independently pointable, so legacy run data can be served from its own database (and replica) rather than the control-plane connection. - Run-graph writes go straight to the run-graph database instead of being forwarded through the control plane, and replication targets are split so runs in the new database still replicate to analytics without under-counting. - Read-through slots refuse the control-plane client, so a missing residency fails loudly instead of silently reading the wrong database. - Migration `20260710120000_drop_remaining_run_graph_seam_foreign_keys` drops the foreign keys that still crossed the run-graph / control-plane seam, which is what lets the two live in separate databases. The split stays off unless explicitly enabled and the two databases are confirmed physically distinct; startup fails closed otherwise. Verified by running the full dashboard end-to-end suite against both a single-database configuration and a three-database configuration (control plane, the new database, and a physically separate legacy database), with runs on both residencies. No misrouted reads in either configuration. |