v4.5.9
230 Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
 Eric Allam
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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>
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Katia Bulatova
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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. |
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Chris Arderne
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dc87b884e7 |
chore: upgrade to typescript 6 (#4310)
## Summary Upgrades the workspace to TypeScript 6.0.3 and applies the compiler, type, and build configuration changes required to preserve package layouts and existing runtime behavior, apart from correcting the HTTP status field used for deployment connection errors. ## Compatibility - Centralizes TypeScript 6.0.3 through the pnpm workspace catalog. - Replaces compiler options and module resolution modes that TypeScript 6 no longer accepts. - Restores explicit Node types where TypeScript 6 no longer includes them transitively. - Adds explicit declaration build roots that preserve each package's existing output layout. - Patches tsup to stop injecting the removed `baseUrl` option during declaration builds. - Uses type-only assertions for stricter typed-array and stream definitions without changing runtime behavior. - Reads the EventSource v3 HTTP status from `code`, so deployment connection errors include it correctly. - Keeps standalone CLI compatibility fixtures pinned to their existing TypeScript version and lockfiles. `turbo run typecheck` and the complete PR test suite are green. |
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Chris Arderne
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6997aeb05e |
fix: security release 2026-07-08 (#4316)
⚒️ Publish Worker (v4) / build (supervisor) (push) Has been cancelled
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Daniel Sutton
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a7c734c223 |
test: caller-driven replica-lag + idempotency guards (stacked on #4284) (#4285)
## Stacked on #4284 — tests only This PR contains **only the tests** that guard the production fixes in #4284 (its base). Review #4284 first; this branch adds no production code. ## What Caller-driven replica-lag and idempotency guards for every fixed site: - Each guard **drives the real exported caller** (route loader/action, presenter `.call()`, service, or engine method) against a **real Postgres** with the owning replica frozen via the shared `laggingReplica` testcontainer primitive — never a store-seam reimplementation. - For a **fixed** site the guard goes **RED when the production change is reverted**; for a **tolerated read-view** site it's a caller-driven **GREEN** proof the miss self-heals (returns null/empty, no mutation, row live on primary). - The **global-scope idempotency** guard drives the real dedup + claim path through a **real `MollifierBuffer` over a Redis testcontainer** (real SETNX/poll/publish), and covers the cross-DB **andWait** waitpoint wiring and the **expired/failed clear-and-recreate** reacquire cases. Run with `vitest --no-file-parallelism` (testcontainers). Verified GREEN, and revert→RED verified per fixed site. |
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Daniel Sutton
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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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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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Chris Arderne
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d7ec75d5ad |
feat(runtime): add experimental Node.js 24 and 26 task runtimes (#4085)
⚒️ Publish Worker (v4) / build (supervisor) (push) Has been cancelled
## Summary Adds experimental Node.js 24 and 26 task runtimes through the `experimental-node-24` and `experimental-node-26` config values. Existing runtime defaults and the `node`, `node-22`, and `bun` behavior remain unchanged. The unprefixed `node-24` and `node-26` config values remain unavailable until the runtimes are ready for general use. ## Design Experimental config values normalize to canonical runtime identifiers before build manifests are created, keeping deployment metadata and execution behavior consistent. Kubernetes task pods also use the runtime-default seccomp profile so modern Node.js versions fall back from io_uring to checkpoint-compatible system calls. |
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Chris Arderne
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b902e65dfb |
chore: standardise internal node on 24.18.0 (#4254)
## Summary Updates the internal development, CI, and runtime-image Node version to 24.18.0. SDK compatibility coverage continues to include Node 20, 22, 24, and 26. The Node type definitions and the package-manager lockfiles now resolve against Node 24 types. |
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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. |
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Daniel Sutton
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c601739d35 |
perf(webapp,run-store): grouped run-ops reads + mint-kind flip grace (#4227)
## Summary Two threads on the run-ops split path. Read path: per-item run reads are batched into grouped queries, a waitpoint's connected-run reads are bounded, and the dedicated-schema relation hydrators fetch only the requested columns instead of whole rows. Retrieve also falls back to the other database when a routed read misses, so a run whose physical residency diverges from its id shape is still found rather than returning a spurious not-found. Fewer and lighter queries on the run read path, with no change to results. Mint-kind flip safety: flipping which database new runs mint to is now a deterministic wall-clock cutover, for both per-org and global flips. For a grace window every process resolves the same database, so a flip cannot route two concurrent triggers that share an idempotency key to different databases (which would bypass the per-database unique constraint and create a duplicate run). Supersedes the earlier #4205 and #4208. Draft: validation in progress. |
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Daniel Sutton
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e4ae8cbcd4 |
fix(run-engine,run-store,webapp): stop split-mode waits hanging on resume (#4164)
## Summary On the run-ops database split, a run that waits (`triggerAndWait`, `batchTriggerAndWait`, `wait.forToken`) could hang forever after its wait had already completed. The runner reads a resume from `/snapshots/since` exactly once: if that read returned the resume snapshot without its completed-waitpoints, the runner logged "executing without completed waitpoints", advanced its cursor, and never re-read it, so the awaiting run never continued. ## Root cause The resume snapshot and its completed-waitpoint rows were written as two separate commits. This regressed when the split replaced Prisma's atomic nested `connect` with an FK-free insert (in [#4163](https://github.com/triggerdotdev/trigger.dev/pull/4163)), and `/snapshots/since` is served from a read replica. A fetch landing in the sub-millisecond gap between the two commits, or a multi-reader replica serving the snapshot from a different point in time than its join rows, delivered an empty resume. Because the runner consumes each snapshot once and treats an empty resume as terminal, a single stale read was fatal and produced a permanent, nondeterministic hang. ## Fixes - Commit a snapshot and its completed-waitpoint links in one transaction, restoring the atomicity the split removed. - Repair the completed-waitpoints from the owning primary when a multi-reader replica serves the snapshot without its join rows. This covers single-waitpoint resumes, which carry no `completedWaitpointOrder` and so were missed by the count-based repair. - Read the primary in the checkpoint `WAIT_FOR_BATCH` pre-check, so a batch that already resumed is not re-suspended into a stall. - Fall back to the primary when a waitpoint token misses both read replicas, so a token completed immediately after it was minted no longer returns a spurious 404. - Route batch-item creation by `batchTaskRunId`, consistent with the batch-completion count and the row's foreign key. - Reject control-plane-only relation selects on the dedicated schema with a clear error instead of an opaque Prisma failure, and stop `createDateTimeWaitpoint` bypassing residency routing through a caller transaction. Verified against the deployed split topology: a resume snapshot and its completed-waitpoints are now always delivered together, so the runner can no longer drop a resume. |
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Daniel Sutton
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f101983a70 |
fix(run-store,run-engine): fix run-ops split hangs from wrong-store reads on the resume path (#4163)
## Summary On the run-ops split, NEW-residency runs could hang. Time-based waits (`wait.for`, `wait.until`, `delay`, waitpoint tokens), `batchTriggerAndWait`, and attempt starts stalled and never resumed. Each was a run-ops read or update that hit the wrong database: either the owning store's read replica when it needed read-your-writes, or the wrong store entirely because it routed by an id that does not encode residency. ## Fixes **Waitpoint resume (the main hang).** The managed resume path reads a run's completed waitpoints by snapshot id (`findSnapshotCompletedWaitpointIds`). Snapshot ids are cuids, which always classify to the legacy store, so a NEW run's join rows (which live on the new store) were never found. The resumed run saw zero completed waitpoints and hung. It now fans out across both stores and merges, like its sibling readers. **Batch completion.** Batch item completion (`updateManyBatchTaskRunItems`) routed by the item id, which is also a cuid, so a NEW batch's items were updated on the wrong store, matched zero rows, and the batch was treated as already complete (its parent's `batchTriggerAndWait` then hung). It now routes by the batch id, which does encode residency, matching the sibling `countBatchTaskRunItems`. **Read-your-writes on the resume path.** The block-time pending-waitpoint check (`countPendingWaitpoints`) and the attempt-start lock check (`findRun` in `startRunAttempt`) both read the owning store's replica with no read-your-writes guarantee, so a just-committed waitpoint completion or dequeue lock could be missed under replica lag and strand the run. Both now read the owning primary. Each fix ships with a two-database store or engine test that reproduces the hang and passes with the fix. |
||
|
Daniel Sutton
|
092b9ef07a |
fix(run-ops): DNS-safe, sortable base32hex run id (replace base62 KSUID) (#4154)
## Problem
The run-ops split mints NEW-store run ids as **27-char base62 KSUIDs**.
The supervisor writes the run id into the Kubernetes pod name
(`runner-<id>`), and pod names must be DNS-1123 labels (lowercase
`[a-z0-9-]`) — so uppercase base62 ids make k8s reject the pod (422) and
**those runs never launch** (they loop in `PENDING_EXECUTING` until the
heartbeat-stall handler nacks them, forever). `.toLowerCase()` can't fix
it: base62 has both `A`(10) and `a`(36) as distinct symbols, so folding
collides distinct ids and destroys sort order.
## Fix: change the encoding, not the structure
Mint a **26-char lowercase base32hex** run id:
```
run_<24-char base32hex core><region char><version char>
[ 6-byte ms timestamp ][ 9 CSPRNG bytes ]
```
- **base32hex** (RFC 4648 §7, alphabet `0-9a-v`): lowercase,
order-preserving, DNS-safe; 15 bytes → exactly 24 chars, no padding.
Hand-rolled encode/decode (no new dependency).
- **48-bit ms timestamp** in the leading bytes → plain string sort ==
creation order at millisecond resolution.
- **72 bits CSPRNG** entropy; PK unique constraint is the backstop (no
retry loop).
- **region / version** are raw positional chars (read via one `charAt`
before decoding/routing), version = `"1"`.
DNS-safe from birth and hyphen-free, so **firekeeper is unchanged** —
`runner-<id>-attempt-N` → strip `runner-`, cut at first hyphen still
recovers the exact id incl. region+version.
## Residency discriminator: length → version char
`classifyKind`/`classifyResidency` (`runOpsResidency.ts`) previously
distinguished NEW vs LEGACY by **id length**. That gets ambiguous with a
third format. It now discriminates on the **version char at a fixed
position** (`isRunOpsIdBody`: 26 chars, `[25] === "1"`, base32hex
alphabet) → NEW; everything else → LEGACY. Total, never throws. The
`Residency` (NEW/LEGACY) contract the routing store consumes is
unchanged; the `"ksuid"` `ResidencyKind` label is retained only because
it's the persisted `runOpsMintKsuid` feature-flag value.
## Scope / verification
- Generator + discriminator in `@trigger.dev/core` isomorphic; mint path
+ all id-shape call sites swept (~40 webapp files); changeset added
(`@trigger.dev/core` patch).
- Core unit tests (encode/decode round-trip + property, generator shape,
ms sort-order incl. intra-second, parse partitioned-vs-legacy,
firekeeper round-trip): **24 pass**. `@trigger.dev/core` builds; webapp
typechecks; format/lint clean.
## Open decisions (flagged, not silently chosen)
1. **Backward-compat**: existing 27-char base62 KSUID runs now classify
LEGACY. On test cloud these are the broken/looping runs that never
completed, so this is acceptable — but worth a conscious call before
prod. No transitional length-recognition added (keeps the discriminator
clean).
2. **Storage collation**: the sort guarantee is byte-order — if the
run-ops id column is `TEXT` with default locale collation it's silently
not honored. Confirm whether `COLLATE "C"` / `BYTEA` is needed on the
run-ops schema.
3. **Region sourcing** wiring — see `regionCharForRegion` /
`REGION_CODES`.
---
## ⚠️ Required migration — deploy in lockstep
This PR renames a persisted feature-flag key/value and an env var. These
are **not** changed by the code alone and must be migrated when this
deploys, or affected orgs silently fall back to `cuid` minting (no crash
— `defaultValue: "cuid"`):
1. **Env var** (terraform): `RUN_OPS_MINT_KSUID_ENABLED` →
`RUN_OPS_MINT_ENABLED` (carry the value over).
2. **DB** `organization.featureFlags`: migrate both the key and value
together:
- key `runOpsMintKsuid` → `runOpsMintKind`
- value `"ksuid"` → `"runOpsId"`
Until an org's flag row is migrated, its `runOpsMintKind` lookup misses
and it mints `cuid` (legacy) — so no NEW-store ids for that org until
the data lands.
---------
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
|
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|
Daniel Sutton
|
c266e96c87 |
feat(run-ops): run-store routing seam + run-engine read seams (#4116)
## What Introduces the run-store routing seam and the run-engine read seams that let run lifecycle operations be dispatched to either the control-plane database or a separately-generated run-ops database, depending on where a run/batch resides. - **run-store** (`internal-packages/run-store`): adds `runOpsStore.ts` and substantially expands `PostgresRunStore.ts` so the store can resolve residency and route reads/writes to the correct backing client. `types.ts` grows the routing/residency types; `NoopRunStore.ts` is removed. - **run-engine** (`internal-packages/run-engine`): adds `engine/controlPlaneResolver.ts` and routes the per-system read paths (dequeue, enqueue, waitpoint, checkpoint, run-attempt, ttl, delayed-run, execution-snapshot, pending-version, debounce, batch) through the resolver/store instead of talking to a single Prisma client directly. `engine/errors.ts`, `engine/types.ts`, and `engine/index.ts` are extended to support injecting the store/resolver. Three fixes are included on top of the seam work: - `c6cadd85f` — routes read-your-writes to the owning store's **writer**, not its lagging replica, so an operation immediately reading back what it just wrote sees a consistent result. - `05c912e05` — normalizes run-ops-generation Prisma errors to the control-plane error class at the store **write boundary**, so `instanceof` checks and the `P2002` → 422 handling continue to work across the separately-generated run-ops Prisma client. - `88d12907f` — resolves NEW-resident batches in `ApiBatchResultsPresenter` by routing the batch read through the store, so a dedicated-DB batch resolves instead of returning 404. The change is heavily test-first: the bulk of the diff is new unit/integration coverage for the store routing, residency, and each run-engine system's control-plane resolver path. ## Why PR4 of the run-ops split stack (PR1–PR3 land the ClickHouse test-container and earlier plumbing). This PR is the read-path foundation: it adds the seam and read-routing but leaves the write path to route through the same seam in a later PR. Behavior-changing where the three fixes above touch existing read-your-writes / error-normalization / batch-resolution paths; otherwise additive (new store module, new resolver, injectable dependencies with existing single-client behavior preserved when no dedicated store is configured). ## Tests Extensive new vitest coverage under `run-store/src/*.test.ts` (routing, residency, dual-schema select, cross-generation error normalization, read-after-write, idempotency dedup, mixed residency, waitpoint co-location) and `run-engine/src/engine/**/*.test.ts` (per-system `controlPlaneResolver` tests, injectability, block-edge residency, waitpoint read residency, trigger-create routing, lifecycle router). Testcontainers-backed; no mocks. ## Notes Draft, **stacked on #4114** (`runops/pr03-clickhouse-tc`). Review that first; this diff is against it. Server-change / changeset note to be added at stack-assembly time. 🤖 Generated with [Claude Code](https://claude.com/claude-code) --------- Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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|
Daniel Sutton
|
a93b101d44 |
feat(run-ops): cross-producer ClickHouse version helper + cross-Postgres-version compat tests (#4114)
## What - Adds `composeTaskRunVersion` to `@internal/clickhouse` (exported from the package index). It packs a small `originGeneration` epoch into the top 8 bits of the ReplacingMergeTree version and keeps the producer's own LSN in the low 56 bits, so `task_runs_v2` rows replicated from more than one Postgres producer become globally comparable while preserving in-producer ordering. Single-producer setups never call it and keep using the raw LSN version. - Adds unit coverage for the helper in `taskRuns.test.ts` (bit layout, ordering, epoch precedence, range validation). - Adds two run-engine tests that exercise the cross-Postgres-version testcontainer fixture: - `heteroPostgresFixture.test.ts` — a smoke test asserting byte-identity and identical `ORDER BY` (under a pinned ICU collation) across two different Postgres major versions. - `crossVersionCompat.test.ts` — mirrors the run-engine's real raw-SQL surfaces and asserts byte-identical, ordering-identical results across the two versions. Ships with an env-gated block (skipped by default) that can be pointed at a real dedicated database in CI. ## Why Third PR in the run-ops split stack. It is purely additive: it introduces one new exported helper plus tests and changes no runtime call sites, so on its own it has no runtime behavior change. It lays down the version-composition primitive and the cross-version compatibility proof that later PRs in the stack rely on. ## Tests - New unit tests for `composeTaskRunVersion` in `internal-packages/clickhouse/src/taskRuns.test.ts` (run against a real ClickHouse testcontainer). - New cross-version tests in `internal-packages/run-engine/src/engine/tests/` running against real Postgres containers of two different major versions (no mocks). The env-gated dedicated-database block is skipped unless its URL is set. ## Notes Draft, **stacked on #4113** (`runops/pr02-db-foundation`). Review that first; this diff is against it. Server-change / changeset note to be added at stack-assembly time. 🤖 Generated with [Claude Code](https://claude.com/claude-code) --------- Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
||
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Chris Arderne
|
c7861be520 |
chore: activate no-unused-vars and import linters (#4096)
Once this is merged, oxlint is at a pretty sensible baseline. **Enable `no-unused-vars`, `typescript/consistent-type-imports`, and `import/no-duplicates` lint rules** Turns on three previously-disabled oxlint rules across the monorepo and fixes all violations: - **`no-unused-vars`** – enabled as an error with standard ignore patterns: unused function arguments are ignored by default (`args: "none"`), variables/caught errors/destructured array elements prefixed with `_` are allowed, and rest siblings are permitted. - **`typescript/consistent-type-imports`** – enforced as an error; all type-only imports now use the `import type` syntax. - **`import/no-duplicates`** – enforced as an error; duplicate import statements from the same module have been merged. The remaining commits clean up the violations found across the codebase: removing unused variables/imports/type aliases, adding `_` prefixes to intentionally unused bindings, fixing duplicate imports, and converting value imports to `import type` where appropriate. |
||
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Chris Arderne
|
b54201f986 | chore: switch to oxfmt, oxlint - add ci checks (#3977) | ||
|
Eric Allam
|
8890d7a258 |
feat(run-engine,webapp): always report worker queue length metrics (#4029)
## Summary The `runqueue.workerQueue.length` gauge only reported a worker queue's depth while runs were being dequeued from it. When dequeues stop, the metric goes stale or missing, so a queue that has backed up because nothing is draining it can't be alerted on. This adds a small observer that refreshes the observed set of worker queues from the `WorkerInstanceGroup` records on an interval, so every active worker queue (and its scheduled split variant) keeps reporting its length regardless of dequeue activity. The observer is off by default and enabled per service via `RUN_ENGINE_WORKER_QUEUE_OBSERVER_ENABLED`, reads from the read replica, and skips a configurable set of cloud providers (`RUN_ENGINE_WORKER_QUEUE_OBSERVER_EXCLUDED_CLOUD_PROVIDERS`, default `digitalocean`). When enabled it is the source of truth for the observed set, so the per-dequeue registration is skipped on that instance, and it groups by worker queue so the per-instance duplicates collapse to the true depth. Also removes the unused `GET`/`POST /api/v1/workers` endpoints. Their only consumer was a CLI command group that is no longer registered. ## Verification Verified end to end against a local stack: the gauge reports each worker queue's length with no dequeues happening, excludes the configured providers, includes hidden groups, and the removed endpoints return as if they never existed. Added a run-engine test (`workerQueueObservation.test.ts`). |
||
|
Matt Aitken
|
5667461895 |
fix(run-engine): decrement totalWeight in fair-queue weighted env shuffle (#4019)
## Summary Fixes the fair-queue weighted environment shuffle, which biased environment ordering whenever fair-queue biases are enabled (the default configuration). ## Root cause `#weightedShuffle` in `fairQueueSelectionStrategy.ts` computed the total weight once and drew its random pivot against that full-set total on every iteration, but never decremented the total as items were removed from the working set. After the first pick, the pivot frequently overshot the sum of the remaining items, so the inner selection loop ran off the end and clamped to the last remaining element. The result systematically over-selected whichever environment sat at the tail of the set. The first slot stayed fair (the full total is correct on the first draw), but later positions were ordered by environment iteration order rather than by the intended concurrency-limit and available-capacity weighting. For four equal-weight environments, the final position landed on one env ~9% of the time and another ~42%, instead of ~25% each. The two sibling selection paths (`#weightedRandomQueueOrder` and `#selectTopEnvs`) already decrement the total before splicing; this brings the env shuffle in line with them. ## Fix ```ts result.push(items[index].envId); totalWeight -= items[index].weight; items.splice(index, 1); ``` Adds a regression test that runs the weighted shuffle over equal-weight envs with biases enabled and asserts each env lands in every position roughly uniformly. It fails on the old code (tail position ~37%) and passes with the fix. Reported in #4001. |
||
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Daniel Sutton
|
65c545da4e |
refactor(run-store,webapp,run-engine): route Postgres TaskRun reads through the run store (#3990)
## Summary Adds read methods to `RunStore` (`findRun`, `findRunOrThrow`, `findRuns`) and routes every Postgres read of `TaskRun` through them, mirroring how writes already go through the store. Behavior-preserving: each relocated read keeps its exact query, field selection, and database client (writer, replica, or transaction). This lets `TaskRun` reads be retargeted to a different backing store later without touching call sites. Stacked on #3981 (the write adapter); that PR is the base of this one. ## Scope In scope: the run engine, webapp services, presenters, and route loaders. Three reads that pulled `TaskRun` in through a parent model's relation `include` (alert delivery, batch results, attempt-dependency cancellation) are decomposed to fetch the run(s) through the store and stitch them back, since a relation include would not follow `TaskRun` to a new table. Left reading the existing table (out of scope): the legacy MarQS paths, the legacy trigger idempotency read, and one raw-SQL recovery script (commented for revisiting at cutover). ## Notes Reads default to the read replica; callers pass the writer or a transaction client wherever the original read did, so writer-vs-replica behavior is unchanged. |
||
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Daniel Sutton
|
315baf2e54 | refactor(run-engine,webapp): route TaskRun writes through a new RunStore adapter (#3981) | ||
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nicktrn
|
7aa871f37b |
feat(webapp): plan-aware compute migration (#3957)
Adds an opt-in mechanism to route a configurable percentage of organizations onto the compute (MicroVM) backing of their region at trigger time, without changing their stored region settings. Routing is gated by three global feature flags - `computeMigrationEnabled`, `computeMigrationFreePercentage`, `computeMigrationPaidPercentage` - plus a per-org `computeMigrationEnabled` override that wins in both directions. A region's compute backing is resolved from a new `WorkerInstanceGroup.region` column: a container group and its MicroVM group share one geo `region`, so the migration swaps the resolved worker queue to the backing group's queue. Orgs are bucketed deterministically by id, so ramping a percentage down keeps a strict subset rather than reshuffling, and a region with no compute backing is never touched. Everything is off by default - behaviour is unchanged unless the flags are set. The flags and the worker-region groups are read on the trigger hot path from in-memory snapshots rather than the database: a small `createReloadingRegistry` helper loads each at startup and refreshes them on an interval, so no per-trigger query is added and a percentage or kill-switch change propagates within the reload interval. A cold replica whose snapshot hasn't loaded yet reads as not-migrated (the container path) and self-corrects on the next load - the same cold-start contract as the datastore / LLM-pricing registries, with a `reloading_registry_loaded` metric so a never-loaded registry is alertable. The same migration decision is consulted at deploy-time template creation so a migrated org gets a compute template built ahead of its first run. This runs in shadow mode (best-effort, never fails the deploy) by default, or - when the `computeMigrationRequireTemplate` flag is on - in required mode, built synchronously at deploy so the first run never builds on-demand and template errors surface at deploy time. So operators keep "which runs ran where" while customers only see geography: the run's actual worker queue is stored raw, and the geo region is stamped separately on `TaskRun.region` (and a new ClickHouse `region` column) at trigger time. Read surfaces - the dashboard, the API, and the Query/Logs page - show the geo region, falling back to the worker queue for runs written before the column existed. Minor follow-ups left out of scope: the percentage flags render as text inputs on the admin flags page (the catalog UI has no numeric control type yet), and `createReloadingRegistry` could later gain pub/sub for sub-second cross-replica propagation if the reload interval proves too slow. |
||
|
Eric Allam
|
f9d57d3bd5 |
feat(webapp): add a new backend for the realtime runs feed (#3864)
## Summary Adds a second backend for the realtime runs feed (`useRealtimeRun`, `subscribeToRunsWithTag`, `subscribeToBatch`), built to stay healthy when a single busy environment has many subscribers watching many runs at once. It is gated behind a feature flag with the existing backend as the default, so nothing changes for users until it is enabled per environment. ## Design A run change is published once, as a small self-describing record, to a single per-environment channel. Every feed is then a predicate over that one stream rather than owning a channel: - A per-instance router indexes the currently-held feeds by run, tag, and batch. When a run changes it hydrates the affected rows once and serializes them once, then fans the result to every matching feed. One hot shared tag watched by many subscribers costs a single database query and serialize, not one per subscriber. - Feeds that don't match a change are never woken, wake delivery per environment is coalesced on a leading edge (250ms default) so a burst of changes costs one wake, and cold reads coalesce onto a single short-TTL-cached resolve. - An admission gate bounds how many cold ClickHouse resolves run concurrently, so a mass reconnect across many distinct filters queues instead of stampeding the database. - Changes that land while a client is between long-polls are delivered on its next poll instead of waiting for the periodic backstop: each environment buffers its recent change records, subscriptions linger briefly after the last feed closes, and a newly-armed poll replays exactly the connection's gap. - The per-connection replay cursors behind that are shared across instances via Redis (a single timestamp each), so a poll landing on a different instance behind the load balancer still reads the connection's true gap instead of falling back to a cold resolve. Cursor reads have a bounded deadline and degrade to the cold-read path on any Redis trouble. - Tag subscriptions with multiple tags match runs carrying all of the tags, mirroring the existing backend's filter semantics, and live long-polls hold for about 20 seconds to match its cadence. - The per-environment channel supports Redis Cluster sharded pub/sub, so the wake path scales horizontally across shards by environment. - The backend reports its health through OpenTelemetry metrics (delivery lag, poll resolution paths, backstop outcomes, replay and cursor-store activity), with a provisioned Grafana dashboard for local development. Everything is behind the feature flag and tunable via env vars; the existing backend remains the default. |
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Daniel Sutton
|
6afc9bfa4c |
fix(run-engine): retry getSnapshotsSince on the replica then primary when the read replica lags (#3889)
## Summary
When `RUN_ENGINE_READ_REPLICA_SNAPSHOTS_SINCE_ENABLED` is on,
`RunEngine.getSnapshotsSince` reads from the read replica. During write
spikes the replica can briefly lag, so the snapshot id a runner just
learned from the writer isn't visible there yet: the lookup threw, the
worker route returned a 500, and the runner waited for its next poll —
turning sub-second snapshot notifications into poll-interval latency
exactly when things are busiest. This PR makes the flag safe to enable:
a replica miss of the since snapshot gets one jittered retry on the
replica (most lag windows are shorter than the ~50–200ms wait, so the
writer is never touched), then falls back to the primary, observed via a
new `run_engine.snapshots_since.replica_miss` counter with an `outcome`
attribute (`replica_retry` vs `primary`). Only genuine misses — absent
on the primary too — remain errors.
## Design
- `getExecutionSnapshotsSince` now throws a typed
`ExecutionSnapshotNotFoundError` so the engine can distinguish the
expected lag miss from real failures. The message string is unchanged
and the error never leaves the engine.
- The recovery path only engages when the flag is on, a distinct replica
client is configured, and no transaction client was passed. With the
flag off, the path is behaviorally identical to before.
- Retry delay bounds are configurable
(`RUN_ENGINE_SNAPSHOTS_SINCE_REPLICA_RETRY_MIN_MS`/`MAX_MS`, default
50/200; `MAX_MS=0` skips the replica retry and goes straight to the
primary).
- The warn log fires only when the primary serves the read (the writer
spill is the operationally interesting event); replica-retry recoveries
are counted but quiet. A permanently-missing snapshot id stays an
error-level failure with a `failedDuring` field, so lag metrics aren't
polluted by bogus ids.
- Stale-tail lag (replica has the since snapshot but not newer rows)
deliberately still returns the replica's view; the next poll catches up.
- The since-snapshot anchor lookup is now scoped to the polled run
(`where: { id, runId }`), so a snapshot id from a different run raises
not-found instead of silently anchoring a too-wide window of the run's
snapshots.
## Test plan
All vitest + testcontainers, no mocks. A new `schemaOnlyPrisma` fixture
(migrated-but-empty clone database) simulates a replica that hasn't
caught up, and a real in-memory OTel meter pins the counter semantics
per outcome.
- [x] Replica catches up during the jittered retry window → served by
the replica, `outcome=replica_retry` = 1, primary never consulted
- [x] Replica permanently missing the since snapshot → served by the
primary, `outcome=primary` = 1
- [x] Snapshot missing on both replica and primary → null, counter = 0
- [x] Replica has the since snapshot but lags by one → the replica's
view is served, no fallback (verified discriminating power: the test
fails if reads secretly hit the primary)
- [x] Flag off with a replica configured → primary serves the read
- [x] Transaction client provided → bypasses the replica entirely
- [x] Since snapshot belonging to a different run → null
- [x] Existing getSnapshotsSince + waitpoints suites green; run-engine,
testcontainers, and webapp typechecks pass
🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
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nicktrn
|
fa15438e42 |
perf(ci): speed up unit tests with LPT sharding + container scoping (#3855)
Speeds up and de-flakes the unit-test suite: testcontainers booted once per vitest worker (per-test isolation kept only where a test runs background redis work that outlives it), a duration-weighted shard sequencer so each shard does roughly equal work, the slowest suites split, two genuine flakes fixed (`streamBatchItems` shared-redis leak; run-engine waits that relied on fixed sleeps), and transient DockerHub pulls retried. **Timings (CI, per-shard wall):** worst unit-test shard ~771s → ~294s; packages/webapp shards ~250-270s, most internal ~190-240s. All 25 shards green. A shard breaks down as ~70s fixed setup (install / image-pull / generate) + ~70s cold `^build` + the actual container tests. So the remaining cost is mostly the tests themselves plus that fixed setup. **Next (separate, timings):** - **typecheck (~6m24s)** — the slowest check overall; bound by full-graph `tsc`, not the TS version (a TS6 branch is still ~6m17s). The real lever is **tsgo** (the Go compiler). - Possible later: turbo CI caching could trim the ~70s cold build on *warm* runs, but it's conditional (cold runs rebuild anyway) and doesn't touch setup or test time — secondary. `cli-v3` e2e and `sdk-compat` are path-gated (don't run on test-infra changes) and already comfortably fast. |
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nicktrn
|
707bf1adb4 |
ci: reduce unit test flakiness and shard re-run cost (#3844)
A unit-test shard recently failed on a timing race rather than a real regression - a run-engine waitpoint test sleeps 1250ms waiting on a 1000ms timeout that's processed by a ~1000ms worker poll, so on a CPU-starved shard the margin evaporates and the whole matrix goes red. Because `fail-fast` defaults on, that one flake cancels the sibling shards, and the only recovery is re-running the entire matrix "just to be sure" - which is itself slow. This is the low-risk first pass at that pain: - `fail-fast: false` on the webapp and internal shard matrices, so one flaky shard no longer cancels its siblings. "Re-run failed jobs" now re-runs just the failed shard instead of the whole matrix. - CI-scoped `retry: process.env.CI ? 2 : 0` on the timing-sensitive packages (`run-engine`, `redis-worker`, `schedule-engine`). Flakes self-heal in CI; local runs stay at `retry: 0` so they still surface in dev. A stopgap until the timing tests are made deterministic. - `fetch-depth: 1` on the unit-test checkouts - they don't use git history, so the full clone was wasted setup time across ~20 jobs. - Reconcile the pre-pull image tags with what testcontainers actually pulls (`redis:7-alpine` -> `redis:7.2`, `ryuk:0.11.0` -> `ryuk:0.14.0`) and add `minio/minio:latest` to the webapp pre-pull. Otherwise those images pull unauthenticated at test time and risk Docker Hub rate-limit flakes (worst on fork PRs, where the authenticated pre-pull is skipped entirely). Deeper follow-ups - bigger runners, turbo remote cache, runtime-weighted sharding, and the real root-cause fix (container reuse / template-DB isolation + deterministic timing tests) - are tracked under TRI-10484. |
||
|
Dan
|
cd252801eb |
feat: dashboard agent - package upgrades (#3793)
1. in webapp folder update ai-sdk to 6.x.x 2. update vitest to 4.xx |
||
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Daniel Sutton
|
4745754a7a |
feat(webapp,run-engine): mollifier drainer replay + stale sweep + cancelled-run engine API (#3754)
## Summary The replay side of the mollifier: - `DrainerHandler`: reads buffered snapshots and replays them through `engine.trigger` to materialise PG rows. - `RunEngine.createCancelledRun`: new public method the handler uses to write CANCELED rows directly from snapshots (bypass queue + waitpoint, emit `runCancelled`). Tolerates the cjson empty-table tags edge case found during validation. - Drainer fairness: org → env rotation so a heavy env doesn't starve light ones in the same org. - Stale-entry sweep + telemetry + alertable gauge so a stuck/offline drainer surfaces in alerts. Both the drainer and sweep default-off; nothing fires unless flagged on (`TRIGGER_MOLLIFIER_DRAINER_ENABLED`, `TRIGGER_MOLLIFIER_STALE_SWEEP_ENABLED`). Stacked on the trigger-time decisions PR. ## Test plan - [x] \`pnpm run typecheck --filter webapp\` passes - [x] \`pnpm run test --filter webapp test/mollifierDrainerHandler.test.ts\` passes - [x] \`pnpm run test --filter webapp test/mollifierStaleSweep.test.ts\` passes - [x] \`pnpm run test --filter @internal/run-engine src/engine/tests/createCancelledRun.test.ts\` passes - [x] \`pnpm run test --filter @trigger.dev/redis-worker packages/redis-worker/src/mollifier/drainer.test.ts\` passes --- ## Ship-gate follow-up fix **Drainer writes SYSTEM_FAILURE on max-attempts exhaustion.** Adds an `onTerminalFailure` callback on `MollifierDrainerOptions` so the customer's run lands a SYSTEM_FAILURE PG row even when the drainer exhausts `MAX_ATTEMPTS` on a retryable PG error (previously `buffer.fail()` was called with no row written → silent data loss). The callback runs before `buffer.fail()` on every terminal path (non-retryable AND max-attempts-exhausted), and re-throwing a retryable error from the callback causes the drainer to requeue rather than fail. Bumps `@trigger.dev/redis-worker` to a **minor** changeset (additive option + new exported types). Includes 5 unit tests covering both terminal causes plus the requeue-on-retryable-callback-failure path and no-callback back-compat. --------- Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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|
Eric Allam
|
61ca40b4b1 |
perf(run-engine,webapp): look up PENDING_VERSION runs via ClickHouse (#3707)
## Summary When a background worker registers, the engine resolves runs that were queued before the worker was ready (status `PENDING_VERSION`). That lookup used to scan a Postgres status index on `TaskRun`. Move it to ClickHouse: query candidate run ids from `task_runs_v2`, then refetch the actual rows from Postgres by primary key with a `status = 'PENDING_VERSION'` guard for idempotency. ## Design The lookup is a pluggable interface on the run engine (`PendingVersionRunIdLookup`). The webapp wires a ClickHouse-backed implementation through the org-scoped `clickhouseFactory` using a new `"engine"` client type, configured by `RUN_ENGINE_CLICKHOUSE_*` env vars. The URL falls back to `CLICKHOUSE_URL` when unset, so self-hosted deployments don't need new config to keep working. When the lookup returns no candidates, one bounded retry is scheduled ~5s later to cover ClickHouse replication lag against `task_runs_v2`. The Postgres status guard on both the candidate refetch and the inner `updateMany` prevents double-promotion when a retry races with a concurrent deploy. Tests cover three existing PENDING_VERSION cases via a small Postgres-backed test adapter; new ClickHouse-backed integration tests will follow. |
||
|
Eric Allam
|
e8ef374fe0 |
fix(webapp,run-engine): honor per-queue length cap on concurrency-key queues (#3558)
## Summary Queues that use concurrency keys can no longer bypass the per-queue length cap, and the "Queued | Running" columns in the dashboard now show the true total across all CK variants instead of 0. The cap and the dashboard both relied on `ZCARD` of the base queue key, but CK-keyed runs live under `<base>:ck:<variant>` keys. Any queue that used concurrency keys read 0 — letting a single CK variant grow unbounded past the user's configured cap. ## Fix Two per-base-queue counters are maintained inside the CK Lua scripts: `<base>:lengthCounter` and `<base>:runningCounter`. Non-CK enqueue/dequeue paths are untouched. Counters are lazy-initialized the first time a CK enqueue (or nack) lands on a queue: the Lua script sums `ZCARD` across the variants tracked by `ckIndex`, sets the counter, then `INCR`s. Pre-existing CK backlog on already-populated queues is captured automatically — no batch migration required. `INCR`/`DECR` is gated on `ZADD`/`SADD` returning 1 (a new entry vs an idempotent no-op), so duplicate enqueues or re-dequeues don't inflate the counter. The counter is `SET` with a 24-hour TTL on init. `INCR`/`DECR` do not extend the TTL, so the counter expires daily and the next CK operation re-seeds it from `ckIndex`. This bounds any drift that accumulates during the rolling-deploy overlap window — where old (un-Tracked) and new (Tracked) webapp instances briefly coexist — to ≤24 hours, with no admin sweep or background reconciler needed. Read paths pipeline `ZCARD`/`SCARD` on the base key + `GET` on the counter and sum. A missing counter is treated as 0, so pure non-CK queues see the same answer as before. The counter-aware scripts ship alongside the originals with a `Tracked` suffix for rolling-deploy safety; a follow-up PR will drop the originals once this has rolled out. ## Test plan - [ ] `pnpm run test --filter @internal/run-engine` — 116 tests pass, including a new `ckCounters.test.ts` covering lazy init from pre-existing backlog, churn, floor-at-zero, the non-CK regression case, mixed CK + non-CK on the same base queue, idempotent re-enqueue (ZADD-already-exists), 24h TTL on the counter, and nack re-seeding after counter expiry. - [ ] Verified end-to-end against a live local environment: - Triggered 24 CK enqueues across 4 variants → `lengthCounter=16`, `runningCounter=8`, dashboard showed Queued=16 / Running=8 for the CK queue. - Set the env queue cap to 16, triggered 12 more enqueues → 8 succeeded, 4 rejected with `QueueSizeLimitExceededError`. - Deleted the counter on a queue with 31 messages already sitting in CK variants, triggered one more enqueue → counter materialized to 31 from the `ckIndex` sum, then INCR'd. |
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|
Matt Aitken
|
e4981d1b11 |
feat(webapp): consolidate auth path + add comprehensive auth tests (#3499)
## Summary
Consolidates the webapp's authentication and authorization into a small
set of route helpers, replacing the ad-hoc `requireUser` /
`requireUserId` / `authenticatedEnvironmentForAuthentication` calls
scattered across routes. Same security model, but the per-request flow
(authenticate → authorize → load) now lives in one place per route
family.
Introduces a plugin seam (`@trigger.dev/plugins`) that lets the cloud
build install a richer RBAC implementation without touching webapp code.
The OSS fallback keeps the pre-RBAC permissive behaviour intact, so
self-hosted deployments work unchanged.
Adds a comprehensive end-to-end auth test suite that didn't exist before
— 193 `it()` blocks (vitest reports ~199 after `it.each` expansion)
covering API key, PAT and JWT auth across the public API surface, plus
dashboard session auth for admin pages.
## Changes
### Plugin contract — `@trigger.dev/plugins`
`RoleBaseAccessController` interface authoritative for both OSS
(fallback) and cloud (enterprise plugin):
- `authenticateBearer(request, { allowJWT? })` — API-key / public-JWT
auth, returns env + ability
- `authenticateSession(request, { userId, organizationId?, projectId?
})` — dashboard auth, caller resolves `userId` from the session cookie
and passes it in (no `helpers.getSessionUserId` callback — decouples the
plugin host from session-cookie code)
- `authenticatePat(request, { organizationId?, projectId? })` — PAT
auth, returns identity + `lastAccessedAt` so the host can throttle the
per-request update
- `authenticateAuthorize*` variants for the auth-and-check-in-one-call
cases
- `isUsingPlugin(): Promise<boolean>` — capability flag for UI /
branching where plugin-present-ness matters; replaces the
sentinel-string coupling that had `personalAccessToken.server` matching
`"RBAC plugin not installed"` literally
### Dashboard auth (started, partial rollout)
Admin and settings pages migrated to a unified `dashboardLoader` /
`dashboardAction` helper that authenticates the session, runs an
authorization check, and exposes the result to the route. Other
dashboard routes still on the old pattern; remaining migration tracked
in TRI-8730.
Migrated routes:
- `admin.*` (14 admin / back-office / feature-flags / LLM-models /
notifications / orgs / concurrency pages)
- `_app.orgs.$organizationSlug.settings.team`
- `_app.orgs.$organizationSlug.settings.roles`
### API / realtime / engine auth (complete for the migrated families)
71 routes migrated to a unified `apiBuilder` that centralizes Bearer /
PAT / Public-JWT authentication and applies the per-route authorization
check before the handler runs. Includes:
- `api.v1.*` and `api.v2.*` and `api.v3.*` — tasks, runs, batches,
queues, prompts, deployments, query, sessions, waitpoints, packets,
workers, idempotency keys
- `realtime.v1.*` — runs, batches, sessions, streams
- `engine.v1.*` — dev / worker-action protocols
29 routes still on the legacy `authenticateApiRequest*` helpers —
tracked as a post-deploy follow-up in TRI-9228.
Multi-resource auth direction is now explicit at the call site via
`anyResource(...)` (OR) and `everyResource(...)` (AND). Bare arrays no
longer typecheck — fixes a class of bug where a JWT scoped to one
resource could implicitly access others under OR semantics.
PAT auth path consolidated: was three DB queries per request (legacy
`authenticateApiRequestWithPersonalAccessToken` findFirst +
`rbac.authenticatePat` join + `lastAccessedAt` update). Now one query in
the steady state — plugin returns `lastAccessedAt`, host smart-skips the
update via JS-side throttle when fresh.
Side effect: action aliases preserved historic JWT scope semantics where
the new model is stricter (e.g. a `write:tasks` JWT now also satisfies
`trigger` / `batchTrigger` / `update` actions on the same resource —
matched at the auth boundary, not in the route handler).
### Backwards-compat fixes
The strict-match model regressed several real-world JWT shapes. Each
preserved via explicit `anyResource(...)` entries in the route's authz
block:
- **Batch retrieve routes** (`api.v1.batches.$batchId`, `api.v2.*`,
`realtime.v1.batches.*`) accept `read:runs` JWTs again (pre-RBAC
literal-match superScope behaviour)
- **Runs list routes** (`api.v1.runs`, `realtime.v1.runs`) accept
type-level `read:tasks` / `read:tags` on unfiltered queries (matched the
legacy `Object.keys` iteration semantic)
- **PAT/OAT auth shape** normalized through `toAuthenticated` so all
auth methods return the same slim `AuthenticatedEnvironment` (was:
API-key returned the slim shape but PAT/OAT returned raw Prisma
`Decimal` / no `orgMember`)
- **Scope `:` preservation** in resource ids — `read:tags:env:staging`
now correctly identifies the tag id as `env:staging`, not `env`
### Slim `AuthenticatedEnvironment`
Extracted to `@trigger.dev/core/v3/auth/environment` — a structural
shape independent of `@trigger.dev/database`. The plugin contract
returns this; webapp consumers import from there; the cloud plugin
(Drizzle) returns the same shape without Prisma's `Decimal` class
leaking into the public surface. Lets internal-packages (run-engine,
etc.) refer to `AuthenticatedEnvironment` without pulling Prisma in.
### Auth test suite (new — `*.e2e.full.test.ts`)
193 e2e tests run against a real spawned webapp + Postgres (no mocks).
Coverage matrix:
- **API key auth** — read / write / trigger / batchTrigger / deploy
actions across runs, batches, deployments, prompts, queues, query,
sessions, input-streams, waitpoints, tasks, idempotency keys; multi-key
resources (a run carries batch / tag / task identifiers — auth must
accept any matching scope)
- **Personal Access Token auth** — comprehensive matrix: scope match,
scope mismatch, missing scope, expired token, malformed token
- **Public JWT auth** — sub-vs-URL environment resolution, expired JWTs,
signature verification, scope checking, otu (one-time-use) token
semantics, branch-environment signing-key fallback
- **Dashboard session auth** — admin-only pages reject non-admins;
per-action gating
- **Cross-cutting edge cases** — revoked API key grace window, JWT
cross-environment isolation, MissingResource branch behaviour
### Hygiene cleanups
- Deleted dead `app/services/authorization.server.ts` (legacy
`checkAuthorization` + types — no live consumers post-migration) and its
orphaned test
- Dropped the never-populated `scopes` field from
`ApiAuthenticationResultSuccess`
- `scheduleEmail` moved out of `email.server.ts` into its own module —
breaks a `commonWorker → marqs/V1` import chain that was poisoning the
auth test graph
- OSS Roles page shows a deployment-aware empty state ("Roles aren't
available in this self-hosted deployment" vs the plan-upsell copy) via
`rbac.isUsingPlugin()`
- Team action handler: explicit per-intent ability gates
(`manage:billing` for purchase-seats, `manage:members` for set-role +
remove-member with self-leave carve-out)
### Cross-repo coordination
All public-package contract changes paired in `triggerdotdev/cloud#763`
(rbac-packages branch) — the enterprise plugin implements the same
`RoleBaseAccessController` interface against Drizzle.
## Test plan
- [x] `pnpm run typecheck --filter webapp` clean
- [x] `pnpm --filter webapp exec vitest run --config
vitest.e2e.full.config.ts` — 193/193 pass (requires Docker for
testcontainers)
- [x] Spot-check an authed API endpoint with a valid + invalid API key
against a local stack
- [x] Spot-check the migrated admin pages render and gate non-admins
---------
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
|
||
|
Eric Allam
|
2301ed608c |
refactor(run-engine): make taskIdentifier optional on run-queue messages (#3559)
## Summary Make `taskIdentifier` optional on the run-queue message schema. No behavior change in this PR; readers continue to accept payloads that include the field. A separate change will stop writing it on the wire to shrink the per-run payload that lives in Redis while runs wait to be dequeued. ## Design The field is written into every payload at enqueue time but no consumer reads it back on the dequeue path. Both the run-engine and supervisor derive `taskIdentifier` from the loaded `TaskRun` row instead. Relaxing the schema first means readers tolerate payloads that omit it, so the writer-side change can ship without producing schema-parse errors during a rolling deploy. `projectId` is left required: `WorkerQueueResolver.#getOverride` reads it for project-scoped runtime worker-queue overrides. ## Test plan - [x] `pnpm run typecheck --filter @internal/run-engine` - [x] `pnpm run typecheck --filter webapp` - [x] `pnpm run test ./src/run-queue/tests/enqueueMessage.test.ts ./src/run-queue/tests/workerQueueResolver.test.ts --run` (28/28 passing) |
||
|
Eric Allam
|
5f1a3e1653 |
refactor(run-engine): route TTL expiration through the batch path only (#3554)
## Summary
TTL expiration on queued runs was being scheduled twice: once via a
per-run `expireRun` worker job (the original implementation) and once
via the batch TTL system (added more recently). Both paths attempt to
flip the same run to `EXPIRED`. The per-run job almost always won the
race, leaving the batch consumer to observe runs already expired by the
older path.
This collapses TTL expiration onto the batch path so every queued TTLed
run goes through a single Redis-backed sorted set + batch consumer
instead of also getting its own scheduled redis-worker job.
## Design
`engine.trigger` and `delayedRunSystem.enqueueDelayedRun` no longer call
`ttlSystem.scheduleExpireRun`. The remaining `enqueueSystem.enqueueRun({
includeTtl: true })` already adds the run to the TTL sorted set;
`TtlSystem.expireRunsBatch` flips it to `EXPIRED` when the TTL fires.
Delayed runs get the same coverage by passing `includeTtl: true` on
their post-delay enqueue, so the TTL is armed from the moment the run
enters the queue (matching how the old job behaved —
`parseNaturalLanguageDuration` is evaluated at enqueue time).
The new path explicitly does not re-expire runs once they have been
allocated a concurrency slot. That is intentional: TTL is for runs that
are queued and have never started. Once a run has a slot it is on its
way to executing.
## Test plan
- [x] `pnpm run test --filter @internal/run-engine
./src/engine/tests/ttl.test.ts` — 15 tests, including a new "Re-enqueued
runs are not expired by TTL once they have started" that locks in the
queued-and-never-started contract.
- [x] `pnpm run test --filter @internal/run-engine
./src/engine/tests/delays.test.ts` — 5 tests, including "Delayed run
with a ttl" which now also asserts the TTL is armed from queue-enter
time, not `createdAt`.
- [x] `pnpm run test --filter @internal/run-engine
./src/engine/tests/lazyWaitpoint.test.ts` — 12 tests.
- [x] `pnpm run typecheck --filter @internal/run-engine`.
|
||
|
Matt Aitken
|
62e006617e |
fix(cli): fail attempt on uncaught exception instead of hanging to maxDuration (TRI-9117) (#3529)
⚒️ Publish Worker (v4) / build (supervisor) (push) Has been cancelled
When a Node EventEmitter (e.g. node-redis) emits an "error" event with
no
listener attached, Node escalates it to process.on("uncaughtException")
in
the task worker. The worker reported the error via the
UNCAUGHT_EXCEPTION
IPC event but did not exit, and the supervisor-side handler in
taskRunProcess only logged the message at debug level — leaving the
run()
promise orphaned until maxDuration fired and producing empty attempts
(durationMs=0, costInCents=0).
The supervisor now rejects the in-flight attempt with an
UncaughtExceptionError and gracefully terminates the worker (preserving
the OTEL flush window) on UNCAUGHT_EXCEPTION. The attempt fails fast
with
TASK_EXECUTION_FAILED, surfacing the original error name, message, and
stack trace, and falls under the normal retry policy. This mirrors the
existing indexing-side behavior in indexWorkerManifest. Apply the same
handling to unhandled promise rejections, which Node already routes
through uncaughtException by default.
|
||
|
Eric Allam
|
386b4f65ff |
feat(webapp): per-org S2 basin migration (#3516)
## Summary Move from a single shared S2 basin to **per-org basins** with retention tied to the org's billing plan. Stops S2 from deleting streams out from under live chat sessions when basin retention fires before the chat ends, and unlocks per-org cost attribution. OSS / s2-lite installs are unaffected: provisioning is gated by `REALTIME_STREAMS_PER_ORG_BASINS_ENABLED` (default `false`), and the read precedence falls back to the global basin env var when an entity has no stamped basin. ``` basin = run.streamBasinName ?? session.streamBasinName ?? env.REALTIME_STREAMS_S2_BASIN ``` ## Design Three nullable `streamBasinName` columns (`Organization`, `TaskRun`, `Session`) plus a provisioner that idempotently creates the basin and reconfigures retention on plan changes. The trigger and session-create paths stamp the org's basin onto new rows; the realtime read path picks the basin from the entity context. Admin routes back-fill existing orgs and force-reconfigure a single org. ## Test plan - [x] `pnpm run typecheck --filter webapp --filter @internal/run-engine` - [x] Backfill admin route end-to-end (provision + DB stamp + S2 basin config). - [x] Reconfigure on plan change (all retention tiers). - [x] chat.agent multi-turn drives streams into the per-org basin. - [x] Legacy fallback when entity has no stamped basin. - [x] Provisioner is a no-op when the flag is off. |
||
|
Eric Allam
|
dac9c83bdc |
chore(webapp,run-engine): downgrade boundary log noise to warn (#3462)
## Summary
Several boundary catches and customer-input validation paths were
logging at `error` level for failures the system already handles
gracefully — disconnect on auth failure, return undefined, skip retries,
etc. This batch routes them to `warn` (which stays in stdout) or counts
them as OTel metrics, so visibility is preserved without surfacing them
as alerts.
## Changes
**New helper / pattern:**
- `apiBuilder.server.ts` — `logBoundaryError(message, error, url)`
inspects the inner error type at loader/action boundary catches;
downgrades to `warn` for `AbortError`, `ServiceValidationError`, and
`EngineServiceValidationError`.
- `platform.v3.server.ts` — `platform_client.failures_total` OTel
counter with `{function, kind}` labels; helper
`recordPlatformFailure(fn, kind)` replaces the previous error-level
logging across all `BillingClient` wrappers.
**Log-level downgrades:**
- `handleSocketIo.server.ts` — `Worker authentication failed` → warn
(system disconnects on failure; refs TRI-8863)
- `waitpointSystem.ts` — when `runStatus === "CANCELED"` in the
suspended-without-checkpoint branch, skip the throw and warn instead
(benign cancel-vs-resume race, nothing to resume)
- `runAttemptSystem.ts` — `flushedMetadata` parse/validate failures →
warn (customer-side data shape, system returns gracefully)
- `batch-queue/index.ts` — final-attempt failures with
`result.skipRetries` → warn (callbacks already opted out of retry, e.g.
queue size limit hit)
- `queryPerformanceMonitor.server.ts` — slow queries → warn
(observability signal, not an application error)
- `timeoutDeployment.server.ts` — deployment-state mismatch in the
timeout job → warn (timeout-vs-completion race)
**Inner error preservation:**
- `waitpointCompletionPacket.server.ts` — `logger.error(uploadError)`
before throwing the `ServiceValidationError` wrapper, so the underlying
upload error stays visible.
## Why
The pattern across all of these is the same: a boundary log treated any
thrown/returned error as `error` regardless of cause, even when the
cause was an expected, system-handled condition (client disconnect,
customer quota, race condition, schema validation of customer data).
That made the logs noisy and made it harder to spot real bugs.
Where the underlying signal is still useful operationally (slow queries,
billing call failures), we route it to OTel metrics with low-cardinality
labels so dashboards and alerts can be tuned independently of error
logs.
## Test plan
- [ ] `pnpm run typecheck --filter webapp`
- [ ] `pnpm run build --filter @internal/run-engine`
- [ ] Trigger a run on hello-world and verify task lifecycle is
unaffected
- [ ] Cancel a suspended run and verify the cancel-while-suspended
branch in `waitpointSystem.ts` returns `{status: "skipped"}` instead of
throwing
- [ ] Confirm `platform_client.failures_total` counter shows up in
metrics with `{function, kind}` labels when the billing client errors
|
||
|
Eric Allam
|
e134da7306 |
fix(run-engine): debounce hot-key lock contention and 5xx feedback loop (#3453)
## Changes
Three changes in
`internal-packages/run-engine/src/engine/systems/debounceSystem.ts`, in
order of impact:
1. **Fast-path skip before the lock.** In `handleExistingRun`, do an
unlocked read of `delayUntil` (and `createdAt` for the max-duration
check) from the run row before entering `runLock.lock("handleDebounce",
...)`. If `newDelayUntil <= currentDelayUntil` and the run is still
within its max-duration window, return the existing run immediately
without taking the lock. Safe because debounce is monotonic-forward only
— a stale read either matches reality or undershoots, both of which
decay correctly (re-checked properly inside the lock by whichever caller
is actually pushing forward). Trailing-mode triggers carrying
`updateData` still take the lock so the data update is applied.
2. **Quantize `newDelayUntil`.** Round the computed `newDelayUntil` to
1-second buckets (configurable via `quantizeNewDelayUntilMs`, set to 0
to disable). Without quantization, every call has a slightly larger
`newDelayUntil` than the last and they all pass the fast-path check.
With it, concurrent callers on the same key share a target time and ~95%
short-circuit. User-visible effect: a debounced run might fire up to 1s
earlier than the strict spec — non-issue for typical debounce use cases
(chat summarization, batched notifications, etc.).
3. **Graceful lock-contention fallback.** Wrap the `runLock.lock(...)`
call so `LockAcquisitionTimeoutError` and Redlock `ExecutionError` /
`ResourceLockedError` return the existing run id with success instead of
propagating a 5xx. Debounce is best-effort: if we can't take the lock,
the herd is already updating it for us; fall in line. This kills the 5xx
→ SDK-retry feedback loop. With (1)+(2) this rarely fires; without them
it's the difference between 5xx and 200.
Defaults preserve current behaviour aside from quantization (1s) and
fast-path (on). Both are configurable via `RunEngineOptions.debounce`.
## ✅ 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
---
## Changelog
Reduce 5xx feedback loops on hot debounce keys by quantizing
`delayUntil`, adding an unlocked fast-path skip before the redlock, and
gracefully handling redlock contention in `handleDebounce` so the SDK no
longer retries into a herd.
---------
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
|
||
|
devin-ai-integration[bot]
|
4b28080ed4 |
feat: add isReplay to run context (#3454)
## Summary Adds `isReplay` boolean to the run context (`ctx.run.isReplay`), following the same pattern as the existing `isTest`. The value is derived from the existing `replayedFromTaskRunFriendlyId` database field, so no schema migration is needed. ## ✅ 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 - Verified `@trigger.dev/core` builds successfully - Verified `webapp` typechecks successfully - All new fields use `default(false)` for backwards compatibility --- ## Changelog - Added `isReplay` to `TaskRun` and `V3TaskRun` schemas in `common.ts` - Added `RUN_IS_REPLAY` semantic attribute and wired it in `taskContext` - Propagated `isReplay` through the dequeue system, run attempt system, and all execution context construction paths (V1 + V2) - Added `isReplay` to `DequeuedMessage` and `TaskRunExecutionLazyAttemptPayload` schemas - Added patch changeset for `@trigger.dev/core` - Updated docs: added `isReplay` to context reference, added "Detecting replays" section to replaying page --- 💯 Link to Devin session: https://app.devin.ai/sessions/1d6f1b3cc39a4623b72d05bf00f2d70c --------- Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> Co-authored-by: nick <55853254+nicktrn@users.noreply.github.com> |
||
|
Matt Aitken
|
f7aefb705a |
fix: disable RunQueue Worker in priority tests to prevent partial-batch race (#3440)
## Summary - The `processMasterQueueForEnvironment` call in the priority test was racing against background `processQueueForWorkerQueue` jobs scheduled 50ms after each trigger - With a 50ms debounce (`processWorkerQueueDebounceMs: 50`) and runs triggered sequentially, the RunQueue Worker could process those jobs mid-sequence, pushing partial batches to the worker queue in the wrong overall priority order - `masterQueueConsumersDisabled: true` only blocks the shard-level polling loops — it does not prevent the RunQueue's own Worker from processing these debounced jobs - Fix: add `worker.disabled: true` to the test 1 engine config, which propagates to `workerOptions.disabled` in the RunQueue constructor and prevents the Worker from starting ## Test plan - [x] Both priority tests pass: `pnpm run test ./src/engine/tests/priority.test.ts --run` - [x] Test 1 log confirms no `✅ Starting run engine worker` or worker loop messages — workers fully disabled - [x] Test 2 unaffected (uses master queue consumers for automatic promotion, no `disabled` flag added) 🤖 Generated with [Claude Code](https://claude.com/claude-code) |
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Eric Allam
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2d3b2e82e6 |
feat(run-engine): flag to route getSnapshotsSince through read replica (#3423)
## Summary Adds `RUN_ENGINE_READ_REPLICA_SNAPSHOTS_SINCE_ENABLED` (default `"0"`). When enabled, the Prisma reads inside `RunEngine.getSnapshotsSince` run against the read-only replica client instead of the primary. Offloads the snapshot-polling queries fired by every running task runner off the writer. ## Why `getSnapshotsSince` is called from the managed runner's fetch-and-process loop (once per poll interval, plus on every snapshot-change notification). It runs four sequential reads per call — one `findFirst` by snapshot id, one `findMany` on snapshots with `createdAt > X`, one raw SQL against `_completedWaitpoints`, and chunked `findMany` on `waitpoint`. Per concurrent run, every few seconds. It's read-only, tolerates a small amount of staleness, and is an obvious candidate for the replica. ## Replica-lag considerations - **Step 1 "since snapshot not found"**: if the runner just received a snapshot id from the primary and asks the replica before it replicates, the function throws and the caller treats the response as an error (runner falls back to a metadata refresh). Self-correcting, not silent. - **Step 2 missing newly-created snapshots**: the next poll's `createdAt > sinceSnapshot.createdAt` filter still picks them up once the replica catches up. - **Waitpoint junction race**: the riskiest path — if a latest snapshot is replicated but its `_completedWaitpoints` join rows aren't yet, the runner could advance past that snapshot with `completedWaitpoints: []`. WAL/storage-level replication replays commits in order, so in practice both should appear atomically on the reader, but the race window is why the flag ships disabled. Aurora reader shrinks all three windows to single-digit ms in typical conditions, and its storage-level replication gives atomic visibility of committed transactions on the reader. ## Test plan - [ ] Flip the flag on in a non-prod environment, confirm snapshot polling behaves normally and `getSnapshotsSince` errors in Sentry stay flat. - [ ] Verify writer query volume drops and reader query volume rises on the snapshot-polling queries. - [ ] Keep an eye on `AuroraReplicaLag` (or equivalent) during rollout. |
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nicktrn
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b570586899 |
fix(webapp): allow cancelling runs in DEQUEUED status from the runs list (#3421)
The cancel button was missing from the runs list for runs in `DEQUEUED` status. The runs list gates the button on `run.isCancellable`, which goes through `isCancellableRunStatus` -> `CANCELLABLE_RUN_STATUSES` = `NON_FINAL_RUN_STATUSES`. `DEQUEUED` was never added to that list when it was introduced in the run engine. The single run page uses a separate check (`!run.isFinished`, i.e. the inverse of `FINAL_RUN_STATUSES`), so cancellation already worked there - only the list was affected. Adding `DEQUEUED` to `NON_FINAL_RUN_STATUSES` also flips `isCrashableRunStatus` and `isFailableRunStatus`, but: - The crash path is the right behaviour - a `DEQUEUED` run (worker has claimed but not yet executing) can legitimately crash before `EXECUTING`, same as `PENDING`/`DELAYED` already do. - The fail path (`failedTaskRun.server.ts`) is only reached from V1 code paths (marqs consumers, v1 heartbeat handler). `DEQUEUED` is a V2-engine-only status, so V1 consumers never see it. When cancelling a `DEQUEUED` run the execution snapshot goes to `PENDING_CANCEL` (worker must ack) but `TaskRun.status` flips to `CANCELED` immediately - the UI reflects cancellation without waiting for the worker. Added an integration test in `run-engine/src/engine/tests/cancelling.test.ts` covering the full trigger -> dequeue -> cancel -> worker-ack flow. ## Stall safety The stall recovery path (PENDING_EXECUTING heartbeat miss -> nack-and-requeue -> back to QUEUED) lives entirely inside `@internal/run-engine` and never touches the webapp's `taskStatus.ts` helpers - the engine has zero imports from `~/v3/taskStatus` and doesn't know `CrashTaskRunService` / `FailedTaskRunService` exist. A stalled DEQUEUED run still goes back to the queue for retry; this change cannot cause stalls to crash or fail. The only realistic impact is the intended UI fix - the theoretical V1 crash/fail branches for DEQUEUED are unreachable in practice because V1 runs never have DEQUEUED status. |
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devin-ai-integration[bot]
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ff290dfe2f |
perf(run-engine): merge dequeue snapshot creation into taskRun.update transaction [TRI-8450] (#3395)
## Summary
Nests the `TaskRunExecutionSnapshot` creation inside the
`taskRun.update()` Prisma call in the dequeue flow, reducing **2 DB
commits → 1** per dequeue operation. This is the highest-volume of the
five unmerged flows identified in TRI-8450 (~9,200 commits/sec on the
engine service).
**Pattern**: Follows the same nested-write approach already used in the
completion path (`runAttemptSystem.ts:735`) and trigger path
(`engine/index.ts:674`).
**Changes**:
- `dequeueSystem.ts`: Moved snapshot creation into `executionSnapshots:
{ create: {...} }` within the existing `taskRun.update()`. Pre-generates
the snapshot ID via `generateInternalId()` (plain cuid, matching what
Prisma's `@default(cuid())` produces) so the event emission, heartbeat
enqueue, and return value can all be constructed from data already in
scope — **no extra DB read needed** after the merged write.
`SnapshotId.toFriendlyId()` is used only for the return value's
`friendlyId` field, matching the original `createExecutionSnapshot`
behavior.
- `executionSnapshotSystem.ts`: Added public
`enqueueHeartbeatIfNeeded()` method that exposes the heartbeat
scheduling logic (previously only available internally via
`createExecutionSnapshot`). This is needed because `PENDING_EXECUTING`
requires a heartbeat, unlike the `FINISHED` status in the completion
reference pattern. This method is reusable by future merge targets
(retry-immediate, checkpoint, cancel, requeue).
**Net DB change per dequeue**: eliminates 1 write transaction (the
separate `TaskRunExecutionSnapshot.create`). No extra reads added — the
snapshot ID is pre-generated and the `executionSnapshotCreated` event
payload is constructed inline from values already available in the
closure.
## Review & Testing Checklist for Human
- [ ] **Verify manually-constructed event payload matches DB state**:
The `executionSnapshotCreated` event is now built inline (not read back
from DB). Confirm the field values (`runStatus: "PENDING"`,
`attemptNumber`, `checkpointId`, `workerId`, `runnerId`,
`completedWaitpointIds`) match what Prisma actually writes. A mismatch
here would be silent — event consumers would get stale/wrong data.
- [ ] **Verify `attemptNumber` source is equivalent**: Old code used
`lockedTaskRun.attemptNumber` (post-update result). New code uses
`result.run.attemptNumber` (pre-update). The `taskRun.update()` data
payload does NOT include `attemptNumber`, so they should be identical —
but confirm this assumption holds for all dequeue scenarios (e.g.
retried runs).
- [ ] **Verify `isValid` defaults to `true` in schema**: The old
`createExecutionSnapshot` explicitly set `isValid: error ? false :
true`. The nested create omits `isValid` (no error in the dequeue happy
path). Confirm the Prisma schema default for
`TaskRunExecutionSnapshot.isValid` is `true`.
- [ ] **Verify `runStatus: "PENDING"` hardcoding matches the mapping**:
The old code passed `lockedTaskRun.status` ("DEQUEUED") to
`createExecutionSnapshot`, which mapped it to "PENDING" via `run.status
=== "DEQUEUED" ? "PENDING" : run.status`. The new code hardcodes
`"PENDING"` directly. This is correct but brittle if `status` ever
changes from "DEQUEUED" to something else upstream.
- [ ] **Spot-check `completedWaitpoints` connect + order logic**: The
nested create replicates the connect/order logic from
`createExecutionSnapshot` (lines 387-393). Verify the
`snapshot.completedWaitpoints` type provides `id` and `index` fields
compatible with this usage.
- [ ] **Verify `checkpoint` in return value**: The return now uses
`snapshot.checkpoint` (from the *previous* snapshot) instead of reading
the newly-created snapshot's checkpoint relation. Since `checkpointId`
is passed through unchanged, they should be identical — but worth a
sanity check.
**Recommended test plan**: deploy to staging, run the
`sample_pg_activity.py` sampler for a 5-minute window, and verify the
COMMIT count drop on the engine service + proportional `IO:XactSync`
reduction.
### Notes
- This only covers the **dequeue** flow (flow #1 from TRI-8450). The
remaining four flows (retry-immediate, checkpoint, requeue, cancel) are
separate follow-ups.
- The new `enqueueHeartbeatIfNeeded` method is deliberately designed for
reuse by those follow-up PRs.
- CI note: the `priority.test.ts` failure in shard 7 is a flaky ordering
assertion unrelated to this change (it compares `friendlyId` values in
dequeue order). The `audit` check is also pre-existing/unrelated.
Link to Devin session:
https://app.devin.ai/sessions/034fe0e7224f49278a2de260203e1377
Requested by: @ericallam
---------
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Co-authored-by: Eric Allam <eallam@icloud.com>
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Eric Allam
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7c95207486 | fix(run-engine): Stop querying for associated run tags during dequeue (#3379) | ||
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Eric Allam
|
73ea5865d8 |
fix(run-engine): distinguish oneTimeUseToken P2002 from idempotency key collision (#3374)
Prevent retrying when retrying won’t actually do any good |
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Eric Allam
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ed0c3e4f38 |
fix: stop creating TaskRunTag records and join table entries during triggering (#3369)
The TaskRun.runTags string array already stores tag names, making the TaskRunTag M2M relation redundant write overhead. Remove createTags calls, connect: tags, and join table writes from both V1 and V2 trigger paths. Simplify the add-tags API to just push to runTags directly. |
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Eric Allam
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417ab876e3 | fix(batch-queue): Batch items that hit the environment queue size limit now fast-fail (#3352) | ||
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Eric Allam
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a3407287c9 |
feat(engine): enqueue fast path; skip the queue under certain conditions (#3299)
## Summary Currently, every triggered run follows a two-step path through Redis: 1. **Enqueue** — A Lua script atomically adds the message to a queue sorted set (ordered by priority-adjusted timestamp) 2. **Dequeue** — A debounced `processQueueForWorkerQueue` job fires ~500ms later, checks concurrency limits, removes the message from the sorted set, and pushes it to a worker queue (Redis list) where workers pick it up via `BLPOP` This means every run pays at least ~500ms of latency between being triggered and being available for a worker to execute, even when the queue is empty and concurrency is wide open. ### What changed The enqueue Lua scripts now atomically decide whether to **skip the queue sorted set entirely** and push directly to the worker queue. This happens inside the same Lua script that handles normal enqueue, so the decision is atomic with respect to concurrency bookkeeping. A run takes the **fast path** when all of these are true: - **Fast path is enabled** for this worker queue (gated per `WorkerInstanceGroup`) - **No available messages** in the queue (`ZRANGEBYSCORE` finds nothing with score ≤ now) — this respects priority ordering and allows fast path even when the queue has future-scored messages (e.g. nacked retries with delay) - **Environment concurrency** has capacity - **Queue concurrency** has capacity (including per-concurrency-key limits for CK queues) When the fast path is taken: - The message is stored and pushed directly to the worker queue (`RPUSH`) - Concurrency slots are claimed (`SADD` to the same sets used by the normal dequeue path) - The `processQueueForWorkerQueue` job is **not scheduled** (no work to do) - TTL sorted set is skipped (the `expireRun` worker job handles TTL independently) When any condition fails, the existing slow path runs unchanged. ### Rollout gating - **Development environments**: Fast path is always enabled - **Production environments**: Gated by a new `enableFastPath` boolean on `WorkerInstanceGroup` (defaults to `false`), allowing region-by-region rollout ### Rolling deploy safety Each process registers its own Lua scripts via `defineCommand` (identified by SHA hash). Old and new processes never share scripts. The Redis data structures are fully compatible in both directions — ack, nack, and release operations work identically regardless of which path a message took. ## Test plan - [x] Fast path taken when queue is empty and concurrency available - [x] Slow path when `enableFastPath` is false - [x] Slow path when queue has available messages (respects priority ordering) - [x] Fast path when queue only has future-scored messages - [x] Slow path when env concurrency is full - [x] Fast-path message can be acknowledged correctly - [x] Fast-path message can be nacked and re-enqueued to the queue sorted set - [x] Run all existing run-queue tests (ack, nack, CK, concurrency sweeper, dequeue) to verify no regressions - [x] Typecheck passes for run-engine and webapp |
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Oskar Otwinowski
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efe24f9c2a |
feat(private-link): Add private links UI (#3264)
⚒️ Publish Worker (v4) / build (supervisor) (push) Has been cancelled
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Saadi Myftija
|
38559480c9 |
feat: replicate trigger_source, root_trigger_source, and is_warm_start to ClickHouse (#3274)
Adds three new top-level columns to the ClickHouse task_runs_v2 table primarily for analytics: - `trigger_source` / `root_trigger_source` - extracted from the existing TaskRun.annotations JSON during WAL replication - `is_warm_start` - new nullable boolean on TaskRun in Postgres, set in the existing taskRun.update() at attempt start (no additional write). null until the first attempt starts. Run region is already available via the existing `worker_queue` column in ClickHouse. |