Fixes an intermittent `Test timed out in 30000ms` in the
`streamBatchItems` suite. Not a logic hang — the 30s budget covers
container setup, and each case boots its own per-test Redis container +
a full `RunEngine`, so under CI Docker contention a cold boot can cross
30s (which is why the failure moved between tests).
- New `containerTestWithIsolatedRedisNoClickhouse` fixture (Postgres
clone + per-test Redis, no ClickHouse) — this suite never uses
ClickHouse, but the old fixture's auto `resetClickhouse` forced a
ClickHouse boot + migration onto the cold-start test.
- Raised `testTimeout` 30s → 120s, matching the run-engine convention
for this footprint.
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.
## Summary
Scheduled runs and their descendants can now be routed to a dedicated
per-region worker queue, processed by a separate worker fleet, so a
burst of scheduled crons no longer competes with standard and agent runs
for the same queue and inflates their startup latency. It is off by
default and enabled per organization via a feature flag (with a global
default), so nothing changes until it is turned on.
## Design
At trigger time, any run whose lineage originates from a schedule
(`rootTriggerSource === "schedule"`, which already propagates from a
scheduled run down to all of its children) gets its worker queue
suffixed with `:scheduled`. The worker queue name is an opaque string
persisted on the run and used verbatim by enqueue and dequeue, so this
needs no Lua, message-envelope, or concurrency changes. Concurrency
stays keyed by environment and queue, not by worker queue.
On the consumer side, the dequeue endpoint gains an optional
`queueClass` selector. A supervisor sends `queueClass: "scheduled"` and
the server derives the actual queue from the worker's own group, so a
token can only ever reach its own region's queues. A fleet picks its
class with the `TRIGGER_WORKER_QUEUE_CLASS` env var (`default` or
`scheduled`), so a dedicated scheduled fleet can run alongside the
standard one.
Verified end to end against a local managed-worker setup: scheduled runs
route to the dedicated queue, are drained only by the scheduled fleet,
and standard runs are left untouched.
## Summary
`concurrencyKey` validation accepted only `z.string().optional()` on the
single-trigger and V2/V3 batch endpoints, and the Phase-2 streaming
NDJSON endpoint accepted `z.record(z.unknown()).optional()` for the
entire `options` field. Callers passing `concurrencyKey: someNumericId`
(e.g. `payload.userId`) either failed schema validation on the first two
paths or sailed through on Phase-2 and then failed downstream at
`prisma.taskRun.create` with `Argument concurrencyKey: Expected String
or Null, provided Int`.
The schema now accepts `string | number` for `concurrencyKey` and
stringifies on the way in, across all three paths. The Phase-2 NDJSON
`options` is tightened to reuse the strict
`BatchTriggerTaskItem.options` shape so it validates identically to the
V2/V3 batch endpoints.
A defensive `typeof === "number"` coercion at the `engine.trigger` call
site in `RunEngineTriggerTaskService` covers in-flight Redis-stored
batch items enqueued before the schema fix — those items are rebuilt
from a `Record<string, unknown>` shape that bypasses the new schema and
would otherwise continue failing for up to their TTL.
## Test plan
- [x] `packages/core/src/v3/schemas/batchItemNDJSON.test.ts` — unit
tests covering numeric→string coercion, string passthrough, no-options,
and rejection of non-string/non-number shapes across
`TriggerTaskRequestBody`, `BatchTriggerTaskItem`, and `BatchItemNDJSON`.
- [x] `apps/webapp/test/engine/triggerTask.test.ts` — `containerTest`
simulating the in-flight Redis batch-item shape (numeric
`concurrencyKey` via `Record<string, unknown>`), verifies the run is
created with `concurrencyKey: "51262"`. Without the worker coercion, the
test reproduces the production stack at `prisma.taskRun.create`.
- [x] `pnpm run typecheck --filter webapp` clean.
- [x] `pnpm run build --filter @trigger.dev/core --filter
@trigger.dev/sdk --filter trigger.dev` clean.
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
## Summary
The trigger hot path's mollifier integration:
- `mollifyTrigger`: when the gate trips, write the engine.trigger
snapshot to the buffer and return a synthesised QUEUED response.
Postgres write is deferred to drainer-replay (next PR in the stack).
- Pre-gate idempotency-key claim: same-key triggers serialise through
Redis so a burst lands in PG / buffer exactly once.
- Read-fallback extensions: `findRunByIdWithMollifierFallback` for the
trigger-time idempotency lookup that must see buffered runs.
- Gate bypasses: `debounce`, `oneTimeUseToken`,
`parentTaskRunId`/`triggerAndWait` skip the mollify path entirely.
- `triggerTask` + `IdempotencyKeyConcern` wired to the above.
All behaviour gated by the master `TRIGGER_MOLLIFIER_ENABLED` switch;
off-state hot path is unchanged (the gate is not even consulted).
Stacked on the buffer extensions PR.
## Test plan
- [x] \`pnpm run typecheck --filter webapp\` passes
- [x] \`pnpm run test --filter webapp test/mollifierMollify.test.ts\`
passes
- [x] \`pnpm run test --filter webapp
test/mollifierIdempotencyClaim.test.ts\` passes
- [x] \`pnpm run test --filter webapp
test/mollifierReadFallback.test.ts\` passes
- [x] \`pnpm run test --filter webapp test/mollifierGate.test.ts\`
passes
- [x] \`pnpm run test --filter webapp test/engine/triggerTask.test.ts\`
passes
---
## Ship-gate follow-up fixes
- **Batch items bypass the mollifier gate** — fixes
`BatchTaskRunItem_taskRunId_fkey` FK violation on batch triggers when
the gate trips. End-state is a drainer-side `BatchTaskRunItem`
create-on-materialise; batch traffic passes through the gate until that
lands.
- **IdempotencyKeyConcern honours buffered-run TTL on expiry** —
buffered path now clears expired idempotency claims (read-side) and
resets the buffer's `mollifier:idempotency:*` SETNX binding (write-side)
so a re-trigger past the customer's TTL lands as a fresh run instead of
echoing the stale buffered runId.
---------
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
## Summary
Buffer-side data layer used by the rest of the mollifier phase-3 stack.
- `buffer.ts` gains entry inspection (`getEntry`), idempotency lookup
(`lookupIdempotency`), in-place snapshot mutation (`mutateSnapshot`),
and dwell tracking. All atomic via Lua.
- `mollifierSnapshot.server.ts`: shared `MollifierSnapshot` type plus
(de)serialise helpers.
- Drops the entry-TTL config and its env var. The drainer is the
recovery mechanism; an entry that survives the drainer should surface as
a stale-sweep alert, not silently TTL away.
Adds methods to the buffer interface; nothing consumes them yet.
Subsequent PRs in the stack wire trigger-time mollify, read-fallback,
and mutation paths against this surface.
## Test plan
- [x] \`pnpm run typecheck --filter webapp\` passes
- [x] \`pnpm run test --filter @trigger.dev/redis-worker
packages/redis-worker/src/mollifier/buffer.test.ts\` passes
---------
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
## Summary
- Introduce the Mollifier: a Redis-backed buffer for `trigger()` API
calls during traffic spikes, with a per-env trip evaluator and a drainer
ack-loop.
- Phase 1 is dual-write monitoring — every mollified trigger is buffered
to Redis AND continues to `engine.trigger`. No customer-facing behaviour
change.
- Telemetry events: `mollifier.would_mollify`, `mollifier.buffered`,
`mollifier.drained`, plus the `mollifier.decisions` counter.
- Gated behind a feature flag (default off).
## Test plan
- [x] `pnpm run test --filter @trigger.dev/redis-worker`
- [x] `pnpm run test --filter webapp -- mollifier`
- [x] Manual: with flag off, no behaviour change vs main
- [x] Manual: with flag on + threshold lowered, observe
`mollifier.buffered` + `mollifier.drained` log pairs with matching
`runId`
---------
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
## Summary
The trigger-task hotpath used to early-return without a DB query when a
caller passed both a queue override and a per-trigger TTL — the hottest
configuration on the trigger API. Adding `triggerSource` to the resolver
so the runs-list "Source" filter could distinguish STANDARD / SCHEDULED
/
AGENT runs removed those early-returns, costing +2 DB queries per
trigger
on non-locked calls and +1 on locked calls.
This change caches `BackgroundWorkerTask` metadata (`ttl`,
`triggerSource`,
`queueId`, `queueName`) in Redis so the resolver can satisfy every
caller
configuration with a single `HGET` on the warm path. PG fallback on miss
back-fills the cache.
Follow-up to #3542.
## Design
Two key spaces:
- `task-meta:env:{envId}` — the "current worker" view, refreshed at
every
deploy promotion. 24h safety TTL.
- `task-meta:by-worker:{workerId}` — used for `lockToVersion` triggers.
Immutable post-create. 30d sliding TTL so historical workers age out.
Cache writes use Lua scripts via `defineCommand` so `DEL` + `HSET` +
`EXPIRE` land atomically — concurrent readers never see the empty
intermediate state of a naive pipeline. Read-path back-fill uses
single-field upserts so concurrent back-fills don't wipe each other's
siblings.
The cache lives behind its own `TASK_META_CACHE_REDIS_*` env-var prefix
that falls back to the default `REDIS_*` set, so operators can route the
cache to a dedicated Redis instance if they want.
The service/instance file split (`taskMetadataCache.server.ts` for the
pure class, `taskMetadataCacheInstance.server.ts` for the env-wired
singleton) mirrors the existing `runsReplicationService` /
`runsReplicationInstance` pattern.
## Test plan
- [ ] `pnpm run typecheck --filter webapp`
- [ ] `pnpm run test ./test/engine/triggerTask.test.ts --run` — 8
existing tests untouched + 5 new tests covering warm cache, cold
miss with back-fill, queue + ttl path, by-worker vs env keyspace,
and the promotion cache write
- [ ] End-to-end against a dev worker: registering writes both keyspaces
with the expected TTLs, and `redis-cli HGETALL
"tr:task-meta:env:<envId>"`
returns the cached entries
## Benchmark
Measured `DefaultQueueManager.resolveQueueProperties` against a real
Postgres + Redis (vitest `containerTest`, single-host docker). 500
sequential calls and 2,000 parallel calls (concurrency=50) per scenario,
request shaped as `{ taskId, queue: "bench-queue", ttl: "5m" }` — the
hot path this PR restores.
```
sequential (one in flight at a time):
[noop cache (baseline)] n=500 mean=1.423ms p50=1.394ms p95=1.735ms p99=2.629ms max=11.100ms
[redis cache, cold ] n=500 mean=1.346ms p50=1.283ms p95=1.688ms p99=2.463ms max=5.058ms
[redis cache, warm ] n=500 mean=0.084ms p50=0.078ms p95=0.105ms p99=0.156ms max=1.129ms
speedup (warm vs baseline, sequential): 16.95x
parallel (concurrency=50):
[noop cache (baseline)] n=2000 mean=10.069ms p50=8.850ms p95=14.718ms p99=31.887ms total=405ms ops/s=4,940
[redis cache, warm ] n=2000 mean=0.614ms p50=0.568ms p95=1.189ms p99=1.432ms total=25ms ops/s=80,389
throughput speedup (warm vs baseline, parallel): 16.27x
```
Read:
- **Warm cache cuts resolver latency 17×** at p50 — from ~1.4 ms to ~78
µs per call.
- **Cold cache is on par with baseline** — the extra `HGET` miss adds
<50 µs against the two Postgres queries that follow, so the worst case
is not worse than today.
- **Under burst load (50 concurrent triggers)**, the baseline's p99
jumps to ~32 ms as Postgres connections queue up; warm stays at ~1.4 ms.
The cache moves the saturation point from ~5k ops/s (PG pool) to ~80k
ops/s (single-client Redis pipelining).
Caveats: single-host docker, local Postgres + Redis, resolver-only
measurement (excludes the rest of the trigger transaction). Prod adds
region-local Redis RTT (~0.3–0.8 ms) which shifts warm absolute numbers
up but keeps the ratio intact.
## Problem
When `batchTrigger()` is called with large payloads, each item's payload
is uploaded to R2 server-side during the streaming loop before being
enqueued. This makes the loop slow — around 3 seconds per item. Workers
pick up and execute each item as it's enqueued, running concurrently
with the ongoing stream.
For the last item in the batch, a race exists between the streaming loop
finishing and the batch completion cleanup:
1. The loop enqueues the last item and returns from `enqueueBatchItem()`
2. A waiting worker picks up the item almost instantly and executes it
3. `recordSuccess()` fires, `processedCount` hits the expected total,
`finalizeBatch()` runs
4. `cleanup()` deletes all Redis keys for the batch, including
`enqueuedItemsKey`
5. The streaming loop exits and calls `getBatchEnqueuedCount()` — reads
the now-deleted key — returns 0
The count check finds `enqueuedCount (0) !== batch.runCount`, falls
through to a Postgres fallback, but the fallback only checked `sealed`.
The BatchQueue completion path sets `status = COMPLETED` in Postgres
without setting `sealed = true` (that's the streaming endpoint's job),
so the fallback misses it too.
This causes the endpoint to return `sealed: false`. The SDK treats this
as retryable and retries up to 5 times with exponential backoff. Each
retry calls `enqueueBatchItem()`, which reads the batch meta key from
Redis — also deleted by `cleanup()` — and throws "Batch not found or not
initialized" (500). The final retry gets a 422 because the batch is
already COMPLETED, which the SDK does not retry, causing an `ApiError`
to be thrown from `await batchTrigger()` in the parent run — even though
all child runs completed successfully.
## Fix
In the Postgres fallback inside `StreamBatchItemsService`, also check
`status === "COMPLETED"` alongside `sealed`. This covers the
fast-completion path where the BatchQueue finishes all runs before the
streaming endpoint gets to seal the batch normally.
Also switches `findUnique` to `findFirst` per webapp convention.
---------
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Replace the expensive DISTINCT query for task filter dropdowns with a
dedicated TaskIdentifier registry table backed by Redis. Environments
migrate automatically on their next deploy, with a transparent fallback
to the legacy query for unmigrated environments. Also fixes duplicate
dropdown entries when a task changes trigger source, and adds
active/archived grouping for removed tasks. Moves BackgroundWorkerTask
reads in the trigger hot path to the read replica.
A single "fetch failed" from the object store was aborting the entire
batch stream with no retry. Added p-retry (3 attempts, 500ms-2s backoff)
around ploadPacketToObjectStore so transient network errors self-heal
server-side instead of propagating to the SDK.
Gracefully handle oversized batch items instead of aborting the stream.
When an NDJSON batch item exceeds the maximum size, the parser now emits
an error marker instead of throwing, allowing the batch to seal
normally. The oversized item becomes a pre-failed run with
`PAYLOAD_TOO_LARGE` error code, while other items in the batch process
successfully. This prevents `batchTriggerAndWait` from seeing connection
errors and retrying with exponential backoff.
Also fixes the NDJSON parser not consuming the remainder of an oversized
line split across multiple chunks, which caused "Invalid JSON" errors on
subsequent lines.
This PR implements a new run TTL system and queue size limits to prevent
unbounded queue growth which should help prevent situations where queues
enter a "death spiral" where the queue will never be able to catch up.
The main/correct way to battle this situation is to enforce a maximum
TTL on all runs (e.g. up to 14 days) where runs that have been queued
for that maximum TTL will get auto-expired, making room for newer runs
to execute. This required creating a new TTL system that can handle
higher workloads and is now deeply integrated into the RunQueue. When
runs are enqueued with a TTL, they are added to their normal queue as
well as to the TTL queue. When runs are dequeued, they are removed from
both their normal queue and the TTL queue. If runs are dequeued by the
TTL system, they are removed from their normal queue. Both these
dequeues happen automatically so there is no race condition.
The TTL expiration system is also made reliable by expiring runs via a
Redis worker, which is enqueued to atomically inside the TTL dequeue lua
script.
### Optional associated waitpoints
Additionally, this PR implements an optimization where runs that aren't
triggered with a dependent parent run will no longer create an
associated waitpoint. Associated waitpoints are then lazily created if a
dependent run wants to wait for the child run post-facto (via debounce
or idempotency), which is a rare situation but is possible. This means
fewer waitpoint creations but also fewer waitpoint completions for runs
with no dependencies.
### Environment Queue Limits
Prevents any single queue growing too large by enforcing queue size
limits at trigger time.
- Queue size checks happen at trigger time - runs are rejected if queue
would exceed limit
- Dashboard UI shows queue limits on both the Queues page and a new
Limits page
- In-memory caching for queue size checks to reduce Redis load
### Batch trigger fixes
Currently when a batch item cannot be created for whatever reason (e.g.
queue limits) the run will never get created, which means a stalled run
if using `batchTriggerAndWait`. We've updated the system to handle this
differently: now when a batch item cannot be triggered and converted
into a run, we will eventually (after retrying 8 times up to 30s) we
will create a "pre-failed" run with the error details, correctly
resolving the batchTriggerAndWait.
Adds support for **debounced task runs** - when triggering a task with a
debounce key, subsequent triggers with the same key will reschedule the
existing delayed run instead of creating new runs. This continues until
no new triggers occur within the delay window.
## Usage
```typescript
await myTask.trigger({ userId: "123" }, {
debounce: {
key: "user-123-update",
delay: "5s",
mode: "leading", // default
}
});
```
- **key**: Scoped to the task identifier
- **delay**: How long to wait before executing (supports duration
strings like `"5s"`, `"1m"`)
- **mode**: Either `"leading"` or `"trailing"`. Leading debounce will
use the payload and options from the first run created with the debounce
key. Trailing will use payload and options from the last run.
### "trailing" mode overrides
When using `mode: "trailing"` with debounce, the following options are
updated from the **last** trigger:
- **`payload`** - The task input data
- **`metadata`** - Run metadata
- **`tags`** - Run tags (replaces existing tags)
- **`maxAttempts`** - Maximum retry attempts
- **`maxDuration`** - Maximum compute time
- **`machine`** - Machine preset (cpu/memory)
## Behavior
- **First run wins**: The first trigger creates the run, subsequent
triggers push its execution time later
- **Idempotency keys take precedence**: If both are specified,
idempotency is checked first
- **Max duration**: Configurable via `DEBOUNCE_MAX_DURATION_MS` env var
(default: 10 minutes)
Works with `triggerAndWait` - parent runs correctly block on the
debounced run.
New batch trigger system with larger payloads, streaming ingestion,
larger batch sizes, and a fair processing system.
This PR introduces a new `FairQueue` abstraction inspired by our own
`RunQueue` that enables multi-tenant fair queueing with concurrency
limits. The new `BatchQueue` is built on top of the `FairQueue`, and
handles processing Batch triggers in a fair manner with per-environment
concurrency limits defined per-org. Additionally, there is a global
concurrency limit to prevent the BatchQueue system from creating too
many runs too quickly, which can cause downstream issues.
For this new BatchQueue system we have a completely new batch trigger
creation and ingestion system. Previously this was a single endpoint
with a single JSON body that defined details about the batch as well as
all the items in the batch.
We're introducing a two-phase batch trigger ingestion system. In the
first phase, the BatchTaskRun record is created (and possibly rate
limited). The second phase is another endpoint that accepts an NDJSON
body with each line being a single item/run with payload and options.
At ingestion time all items are added to a queue, in order, and then
processed by the BatchQueue system.
## New batch trigger rate limits
This PR implements a new batch trigger specific rate limit, configured
on the `Organization.batchRateLimitConfig` column, and defaults using
these environment variables:
- `BATCH_RATE_LIMIT_REFILL_RATE` defaults to 10
- `BATCH_RATE_LIMIT_REFILL_INTERVAL` the duration interval, defaults to
`"10s"`
- `BATCH_RATE_LIMIT_MAX` defaults to 1200
This rate limiter is scoped to the environment ID and controls how many
runs can be submitted via batch triggers per interval. The SDK handles
the retrying side.
## Batch queue concurrency limits
The new column `Organization.batchQueueConcurrencyConfig` now defines an
org specific `processingConcurrency` value, with a backup of the env var
`BATCH_CONCURRENCY_LIMIT_DEFAULT` which defaults to 10. This controls
how many batch queue items are processed concurrently per environment.
There is also a global rate limit for the batch queue set via the
`BATCH_QUEUE_GLOBAL_RATE_LIMIT` which defaults to being disabled. If
set, the entire batch queue system won't process more than
`BATCH_QUEUE_GLOBAL_RATE_LIMIT` items per second. This allows
controlling the maximum number of runs created per second via batch
triggers.
## Batch trigger settings
- `STREAMING_BATCH_MAX_ITEMS` controls the maximum number of items in a
single batch
- `STREAMING_BATCH_ITEM_MAXIMUM_SIZE` controls the maximum size of each
item in a batch
- `BATCH_CONCURRENCY_DEFAULT_CONCURRENCY` controls the default
environment concurrency
- `BATCH_QUEUE_DRR_QUANTUM` how many credits each environment gets each
round for the DRR scheduler
- `BATCH_QUEUE_MAX_DEFICIT` the maximum deficit for the DRR scheduler
- `BATCH_QUEUE_CONSUMER_COUNT` how many queue consumers to run
- `BATCH_QUEUE_CONSUMER_INTERVAL_MS` how frequently they poll for items
in the queue
### Configuration Recommendations by Use Case
**High-throughput priority (fairness acceptable at 0.98+):**
```env
BATCH_QUEUE_DRR_QUANTUM=25
BATCH_QUEUE_MAX_DEFICIT=100
BATCH_QUEUE_CONSUMER_COUNT=10
BATCH_QUEUE_CONSUMER_INTERVAL_MS=50
BATCH_CONCURRENCY_DEFAULT_CONCURRENCY=25
```
**Strict fairness priority (throughput can be lower):**
```env
BATCH_QUEUE_DRR_QUANTUM=5
BATCH_QUEUE_MAX_DEFICIT=25
BATCH_QUEUE_CONSUMER_COUNT=3
BATCH_QUEUE_CONSUMER_INTERVAL_MS=100
BATCH_CONCURRENCY_DEFAULT_CONCURRENCY=5
```
* WIP
* Make release concurrency system extremely simple, everything just releases all the time
* update the deadlock detection to use the new lockedQueueReleaseConcurrencyOnWaitpoint column
* WIP new release concurrency system
* Remove releaseConcurrency and releaseConcurrencyOnWaitpoint
Also removed deadlock detection, and added environment burst concurrency
* Added new DEQUEUED status
Cleaned up the API run statuses, including now detecting new clients and not breaking older clients by adding an API version header to all requests
* Introduce the new "current dequeued concurrency set"
* Remove QUEUED_EXECUTING because we no longer "eagerly" release before checkpointing
* Remove waitpoint test for QUEUED_EXECUTING
* Add isWaiting
* Add changeset
* Use createdAt for ordering realtime runs instead of number
* Clarify the envCurrentDequeuedKey usage
* mock the db.server file to fix the tests
* Updated changset "EXECUTED" -> "EXECUTING"
---------
Co-authored-by: Matt Aitken <matt@mattaitken.com>
* v4: current concurrency sweeper
* Fix webapp tests
* Ensure only a single instance performs concurrency sweeping by using redis-worker cron jobs
* Improved the mark phase
* Ensure cron jobs get rescheduled even if the handler throws an error
* Better property names
* WIP
* Run queue now works with the worker queue / master queue split
* Acking should also cause the master queue to be processed
* Convert run engine tests and run engine to use runQueue changes
* Include the util files in the test tsconfig
* coordinator target should be es2020 as well
* providers target 2020
* Fix the triggerTask tests in the webapp
* v4 now working with the new worker queues, and added the legacy master queue migration stuff
* report worker queue lengths via opentelemetry metrics
* Adding lock metrics
* Release concurrency bucket metrics
* • Updated RunQueue.removeEnvironmentQueuesFromMasterQueue() method signature to take runtimeEnvironmentId instead of masterQueue parameter
• Added automatic master queue shard calculation using this.keys.masterQueueKeyForEnvironment(runtimeEnvironmentId, this.shardCount)
• Updated RunEngine wrapper method to use new runtimeEnvironmentId parameter
• Updated DeleteProjectService to call the method once per environment instead of once per master queue
• Simplified API by encapsulating master queue sharding logic within RunQueue class
* metrics now working, configure the run queue settings, additional metrics for run engine and redis-worker
* Fix CodeRabbit suggestions
* return undefined from dequeueFromWorkerQueue, not null
* Remove message from worker queue in certain circumstances when acking
* Update log
* Ensure master queue consumers cannot stop from a processing error, and make the consumer interval configurable via an env var
* Change how the run queue master queue consumers are disabled internally
* Fixed tests
* process the queue on nack
* Fix more tests
* Fix priority tests
* Fixed dequeueing test
* WIP clickhouse package with test containers setup
* More clickhouse client setup now with otel and real tests, and the v1 of raw run events
* Add some additional columns to raw_run_events_v1
* WIP runs dashboard service
* Create a new run engine event bus event for the runs dashboard to hook into
* Track run events in the run engine
* make sure engine v1 runs get synced to CH
* Update the attemptNumber of v3 task runs
* Restructure the run events to be more sparse
* emit more stuff
* Setup replication package
* scaffold the replication package
* replication wip
* resolve conflicts
* more replication stuff
* Add ability to drop the replication slot completely on teardown
* Use the new single replacingmergetree task events table for replication
* get it working
* insert payloads into their own table only on insert and then join
* prepare for using clickhouse cloud and now running ch migrations during boot in the entrypoint.sh
* Handover WIP and tests
* Testing the replication service
* Remove the runs dashboard stuff that we aren't using anymore
* Added a test for large payloads
* hacky typecheck fix
* Fix new internal package typecheck issues and start adding telemetry to the replication service
* tracing over spans, some other improvements
* Improvements to the runs replication service, now ready for testing
* Some fixes and cleanups
* Don't need this code anymore
* move transaction types into the runs replication service
* only send spans where there are transaction events
* A couple of suggested tweaks
* Locked task runs will now require queues and tasks to be in the locked version
* Client errors caught in a run function now will skip retrying
* Extracted out the trigger queues logic
* extract validation, idempotency keys, payloads to concerns
* Extracted out a bunch of more stuff and getting trigger tests to work
* Add queue and locked version tests
* Deadlock detection WIP
* more deadlock detection
* Only detect deadlocks when the parent run is waiting on the child run
* Improve the error experience around deadlocks
* A couple tweaks to make CodeRabbit happy and fixing the tests in CI
* Fixed failing test
* Changeset
* wip
* Make sure to scope queries to the runtime env
Daniel Sutton