## Summary
Each scheduled-task tick previously issued **3 Prisma `UPDATE`s**
against
`TaskSchedule.lastRunTriggeredAt`,
`TaskScheduleInstance.lastScheduledTimestamp`,
and `TaskScheduleInstance.nextScheduledTimestamp`. All three were pure
denormalization — every value can be derived without persisting.
After this PR `TaskSchedule` and `TaskScheduleInstance` become **near
read-only**:
writes happen only on schedule create / update / delete (rare admin
actions),
so the per-tick autovacuum churn on these hot tables disappears.
## Design
The previous fire time travels forward through the **schedule worker
payload**,
not through the database. Concretely:
- The `schedule.triggerScheduledTask` worker payload gains an optional
`lastScheduleTime: z.coerce.date().optional()` field.
- When the engine fires a schedule, it re-enqueues the next tick with
`lastScheduleTime = scheduleTimestamp` (the just-fired time).
- When the next tick dequeues, `payload.lastTimestamp` is sourced from
`params.lastScheduleTime` directly. No DB round-trip, no cron-derivation
drift across DST boundaries, no caveats around recently-edited cron
expressions.
`payload.lastTimestamp` keeps its `Date | undefined` SDK shape.
First-ever
fires still report `undefined`, so customer `if
(!payload.lastTimestamp)`
first-run patterns keep working.
For Redis jobs that were enqueued **before** this change (which lack
`lastScheduleTime` in their payload), the engine falls back to
`instance.lastScheduledTimestamp` once. Once those drain, the column is
never read again. Revert is code-only; the columns stay in place and can
be dropped in a follow-up once the rollout is stable.
## Files
- `internal-packages/schedule-engine/*` — engine refactor,
`workerCatalog`
schema field, `TriggerScheduleParams` extension, tests updated to assert
on the worker-payload flow rather than DB readbacks.
- `internal-packages/database/prisma/schema.prisma` — `/// @deprecated`
triple-slash docstrings on the three columns. No migration.
- `apps/webapp/app/presenters/v3/ScheduleListPresenter.server.ts` —
drops
the `lastRunTriggeredAt` Prisma select; "Last run" cell is approximated
from the cron expression's previous slot, gated on `schedule.createdAt`
so brand-new schedules show "–". UI is best-effort; the runs page is the
source of truth.
- `apps/webapp/app/v3/utils/calculateNextSchedule.server.ts` — adds a
`previousScheduledTimestamp` helper for the UI cell above. Public API
responses (`api.v1.schedules.*`) already compute `nextRun` from cron and
don't expose `lastTimestamp` — no public API change.
- `references/scheduled-tasks/` — new reference project with declarative
schedules at multiple cadences and three throw-on-fail validators
(`first-fire-detector`, `interval-validator`, `upcoming-validator`) for
E2E-verifying the worker-payload flow.
Refs TRI-8891
## Test plan
- [x] `pnpm run typecheck --filter @internal/schedule-engine --filter
webapp`
- [x] `pnpm run build --filter @trigger.dev/core`
- [x] `pnpm run test --filter @internal/schedule-engine` — integration
test
asserts first-fire `lastTimestamp === undefined`, second fire carries
the previous fire's timestamp exactly.
- [x] E2E against local webapp via `references/scheduled-tasks`:
- Fresh schedules attached → all three deprecated columns stay `NULL`
after
multiple fires.
- Redis payload at second fire contains
`"lastScheduleTime":"<previous fire timestamp>"`.
- `TaskRun.payload` and the every-minute task's returned output both
confirm
`lastTimestamp = null` on first fire and `lastTimestamp = <prev fire>`
on
second fire, exactly 60s apart.
- All three throw-on-FAIL validators completed successfully on every
non-first fire.
- [x] Schedules REST API end-to-end (`POST` / `GET` / `PUT` / `activate`
/
`deactivate` / `DELETE`) — `nextRun` recomputed live from cron + tz on
every response, no reads of deprecated columns.
## Summary
Stamp every Prisma span with `db.datasource: "writer" | "replica"` so
traces can distinguish which client the query went through.
Both `PrismaClient` instances share the same global
`@prisma/instrumentation`, so their spans come out with identical names
and attributes today. This makes them trivially filterable.
## How
Two pieces in `apps/webapp/app/`:
1. **`v3/tracer.server.ts`** — a `DatasourceAttributeSpanProcessor`
reads an OTel context key in `onStart` and calls
`span.setAttribute("db.datasource", value)`. Registered as the first
span processor.
2. **`db.server.ts`** — `tagDatasource(datasource, client)` wraps each
`PrismaClient` with `$extends({ query: { $allOperations } })`. The
middleware sets the context key around the query and directly tags the
active span (to catch `prisma:client:operation`, which Prisma creates
before the middleware fires).
### Context-propagation gotcha
`PrismaPromise` is lazy — `query(args)` returns a thenable that only
starts when someone `.then()`s it. The naive `context.with(ctx, () =>
query(args))` restores ALS synchronously, so when Prisma's internal code
awaits the thenable later, the engine spans fire with the original ALS.
Wrapping as `async () => await query(args)` forces the `.then()` inside
the `context.with` callback, so ALS stays on our context for the engine
spans.
### Coverage
- **Tagged**: all `prisma:engine:*` (`connection`, `db_query`,
`serialize`, `query`, etc.), `prisma:client:operation`,
`prisma:client:serialize`, `prisma:client:connect`
- **Not tagged**: `prisma:client:load_engine` — one-time startup, fires
before any query
Concurrent `Promise.all([writer.x, replica.y])` correctly tags each pool
separately (ALS isolates per-Promise chain).
### Performance
One `context.with` (~200ns) and one `setAttribute` per span (effectively
free per OTel JS benchmarks) per Prisma op. Negligible against a query
path measured in milliseconds.
## Test plan
- [ ] Verify `db.datasource` appears on `prisma:engine:connection` spans
after the webapp is restarted
- [ ] Spot-check a handful of real traces carry the attribute
Add TTL (time-to-live) defaults at task-level and config-level, with
precedence: per-trigger > task > config > dev default (10m).
Docs PR: #3200 (merge after packages are released)
- Full prompt management UI: list, detail, override, and version
management for AI prompts defined with `prompts.define()`
- Rich AI span inspectors for all AI SDK operations with token usage,
messages, and prompt context
- Real-time generation tracking with live polling and filtering
## Prompt management
Define prompts in your code with `prompts.define()`, then manage
versions and overrides from the dashboard without redeploying:
```typescript
import { task, prompts } from "@trigger.dev/sdk";
import { generateText } from "ai";
import { openai } from "@ai-sdk/openai";
import { z } from "zod";
const supportPrompt = prompts.define({
id: "customer-support",
model: "gpt-4o",
variables: z.object({
customerName: z.string(),
plan: z.string(),
issue: z.string(),
}),
content: `You are a support agent for Acme SaaS.
Customer: {{customerName}} ({{plan}} plan)
Issue: {{issue}}
Respond with empathy and precision.`,
});
export const supportTask = task({
id: "handle-support",
run: async (payload) => {
const resolved = await supportPrompt.resolve({
customerName: payload.name,
plan: payload.plan,
issue: payload.issue,
});
const result = await generateText({
model: openai(resolved.model ?? "gpt-4o"),
system: resolved.text,
prompt: payload.issue,
...resolved.toAISDKTelemetry(),
});
return { response: result.text };
},
});
```
The prompts list page shows each prompt with its current version, model,
override status, and a usage sparkline over the last 24 hours.
From the prompt detail page you can:
- **Create overrides** to change the prompt template or model without
redeploying. Overrides take priority over the deployed version when
`prompt.resolve()` is called.
- **Promote** any code-deployed version to be the current version
- **Browse generations** across all versions with infinite scroll and
live polling for new results
- **Filter** by version, model, operation type, and provider
- **View metrics** (total generations, avg tokens, avg cost, latency)
broken down by version
## AI span inspectors
Every AI SDK operation now gets a custom inspector in the run trace
view:
- **`ai.generateText` / `ai.streamText`** — Shows model, token usage,
cost, the full message thread (system prompt, user message, assistant
response), and linked prompt details
- **`ai.generateObject` / `ai.streamObject`** — Same as above plus the
JSON schema and structured output
- **`ai.toolCall`** — Shows tool name, call ID, and input arguments
- **`ai.embed`** — Shows model and the text being embedded
For generation spans linked to a prompt, a "Prompt" tab shows the prompt
metadata, the input variables passed to `resolve()`, and the template
content from the prompt version.
All AI span inspectors include a compact timestamp and duration header.
## Other improvements
- Resizable panel sizes now persist across page refreshes (patched
`@window-splitter/state` to fix snapshot restoration)
- Run page panels also persist their sizes
- Fixed `<div>` inside `<p>` DOM nesting warnings in span titles and
chat messages
- Added Operations and Providers filters to the AI metrics dashboard
## Screenshots
<img width="3680" height="2392" alt="CleanShot 2026-03-21 at 10 14
17@2x"
src="https://github.com/user-attachments/assets/f3e59989-a2fa-4990-a9d0-3cacda431868"
/>
<img width="3680" height="2392" alt="CleanShot 2026-03-21 at 10 15
37@2x"
src="https://github.com/user-attachments/assets/2f2d02df-2d2b-44fb-ac6f-9153f6a6c387"
/>
<img width="3680" height="2392" alt="CleanShot 2026-03-21 at 10 15
54@2x"
src="https://github.com/user-attachments/assets/baa161e0-ef91-4fa4-a55f-986b71cccdf0"
/>
Queues with concurrency keys now appear as a single entry in the master
queue instead of one entry per key. This prevents high-CK-count tenants
from consuming the entire `parentQueueLimit` window and starving other
tenants on the same shard.
A new per-queue **CK index** (sorted set) tracks active concurrency key
sub-queues. The master queue gets one `:ck:*` wildcard entry per base
queue. Dequeuing from that entry round-robins across sub-queues,
maintaining per-CK concurrency tracking and fairness.
All existing operations (enqueue, dequeue, ack, nack, DLQ, TTL expiry)
are CK-index-aware and keep the index consistent. Old-format entries
drain naturally during rollout — no migration step needed, single
deploy.
Input streams enable sending typed data to executing tasks from external
callers — backends, frontends, or other tasks. This unlocks interactive
use cases like approval UIs, cancel buttons, chat interfaces, and
human-in-the-loop AI workflows where the task needs to receive data
while running.
Three consumption patterns inside a task:
* `.wait()` — Suspend the task until data arrives (process freed, most
efficient)
* `.once()` — Wait for the next message (process stays alive)
* `.on()` — Subscribe to a continuous stream of messages
One send pattern from outside:
* `.send(runId, data)` — Send typed data to a specific run's input
stream
## User-facing API
### Define a typed input stream
```ts
import { streams, task } from "@trigger.dev/sdk";
const approval = streams.input<{ approved: boolean; reviewer: string }>({ id: "approval" });
```
### Consume inside a task
```ts
export const myTask = task({
id: "my-task",
run: async () => {
// Pattern 1: Suspend until data arrives (most efficient — frees the process)
const result = await approval.wait({ timeout: "5m" });
// Pattern 2: Wait for next message (process stays alive)
const data = await approval.once().unwrap();
// Pattern 3: Subscribe to multiple messages
approval.on((data) => { /* handle each message */ });
},
});
```
### Send from outside
```ts
// From a backend (using secret API key)
await approval.send(runId, { approved: true, reviewer: "alice" });
// From a frontend (using public JWT token from trigger response)
const { send } = useInputStreamSend("approval", runId, { accessToken });
send({ approved: true, reviewer: "alice" });
```
---------
Co-authored-by: Claude <noreply@anthropic.com>
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 an end-to-end OTEL metrics pipeline: task workers collect and
export metrics via OpenTelemetry, the webapp ingests them into
ClickHouse, and they're queryable through the existing dashboard query
engine
- Workers emit process CPU/memory metrics (via
`@opentelemetry/host-metrics`) and Node.js runtime metrics (event loop
utilization, event loop delay, heap usage)
- Users can create custom metrics in their tasks via
`otel.metrics.getMeter()` from `@trigger.dev/sdk`
- Metrics are automatically tagged with run context (run ID, task slug,
machine, worker version) so they can be sliced per-run, per-task, or
per-machine
- The TSQL query engine gains metrics table support with typed attribute
columns, `prettyFormat()` for human-readable values, and per-schema time
bucket thresholds
- Includes reference tasks
(`references/hello-world/src/trigger/metrics.ts`) demonstrating
CPU-intensive, memory-ramp, bursty workload, and custom metrics patterns
## What changed
### Metrics collection (packages/core, packages/cli-v3)
- **Metrics export pipeline** — `TracingSDK` now sets up a
`MeterProvider` with a `PeriodicExportingMetricReader` that chains
through `TaskContextMetricExporter` (adds run context attributes) and
`BufferingMetricExporter` (batches exports to reduce overhead)
- **Host metrics** — Enabled `@opentelemetry/host-metrics` for process
CPU, memory, and system-level metrics
- **Node.js runtime metrics** — New `nodejsRuntimeMetrics.ts` module
using `performance.eventLoopUtilization()`, `monitorEventLoopDelay()`,
and `process.memoryUsage()` to emit 6 observable gauges
- File system and diskio metrics
- **Custom metrics** — Exposed `otel.metrics` from `@trigger.dev/sdk` so
users can create counters, histograms, and gauges in their tasks
- **Machine ID** — Stable per-worker machine identifier for grouping
metrics
- **Dev worker** — Drops `system.*` metrics to reduce noise, keeps
sending metrics between runs in warm workers
### Metrics ingestion (apps/webapp)
- **OTEL endpoint** — `otel.v1.metrics.ts` accepts OTEL metric export
requests (JSON and protobuf), converts to ClickHouse rows
- **ClickHouse schema** — `017_create_metrics_v1.sql` with 10-second
aggregation buckets, JSON attributes column, 60-day TTLs
### Query engine (internal-packages/tsql, apps/webapp)
- **Metrics query schema** — Typed columns for metric attributes
(`task_identifier`, `run_id`, `machine_name`, `worker_version`, etc.)
extracted from the JSON attributes column
- **`prettyFormat()`** — TSQL function that annotates columns with
format hints (`bytes`, `percent`, `durationSeconds`) for frontend
rendering without changing the underlying data
- **Per-schema time buckets** — Different tables can define their own
time bucket thresholds (metrics uses tighter intervals than runs)
- **AI query integration** — The AI query service knows about the
metrics table and can generate metric queries
- **Chart improvements** — Better formatting for byte values,
percentages, and durations in charts and tables
### Reference project
- **`references/hello-world/src/trigger/metrics.ts`** — 6 example tasks:
`cpu-intensive`, `memory-ramp`, `bursty-workload`, `sustained-workload`,
`concurrent-load`, `custom-metrics`
## Test plan
- [ ] Build all packages and webapp
- [ ] Start dev worker with hello-world reference project
- [ ] Run `cpu-intensive`, `memory-ramp`, and `custom-metrics` tasks
- [ ] Verify metrics in ClickHouse: `SELECT DISTINCT metric_name FROM
metrics_v1`
- [ ] Query via dashboard AI: "show me CPU utilization over time"
- [ ] Verify `prettyFormat` renders correctly in chart tooltips and
table cells
- [ ] Confirm dev worker drops `system.*` metrics but keeps `process.*`
and `nodejs.*`
Summary
- Add API endpoint to run TRQL queries
- Implement SDK function for executing queries
## SDK
Added `query.execute()` which lets you query your Trigger.dev data using
TRQL (Trigger Query Language) and returns results as typed JSON rows or
CSV. It supports configurable scope (environment, project, or
organization), time filtering via `period` or `from`/`to` ranges, and a
`format` option for JSON or CSV output.
```typescript
import { query } from "@trigger.dev/sdk";
import type { QueryTable } from "@trigger.dev/sdk";
// Basic untyped query
const result = await query.execute("SELECT run_id, status FROM runs LIMIT 10");
// Type-safe query using QueryTable to pick specific columns
const typedResult = await query.execute<QueryTable<"runs", "run_id" | "status" | "triggered_at">>(
"SELECT run_id, status, triggered_at FROM runs LIMIT 10"
);
typedResult.results.forEach(row => {
console.log(row.run_id, row.status); // Fully typed
});
// Aggregation query with inline types
const stats = await query.execute<{ status: string; count: number }>(
"SELECT status, COUNT(*) as count FROM runs GROUP BY status",
{ scope: "project", period: "30d" }
);
// CSV export
const csv = await query.execute(
"SELECT run_id, status FROM runs",
{ format: "csv", period: "7d" }
);
console.log(csv.results); // Raw CSV string
```
## ✅ 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
Tested the migration on test env and locally.
Tested query and merge performance.
Generated tasks and observed the ingested data and searches.
---
## Changelog
* New ClickHouse table & MV (task_events_search_v1): A search-optimized
materialized view that filters out debug events, partial spans, and
empty span events at ingestion time.
* ClickHouse client updates: New getLogsSearchListQueryBuilder and
taskEventsSearch accessor on the ClickHouse class.
* LogsListPresenter: Switches to the new search table, uses
triggered_timestamp for cursor pagination instead of unixTimestamp.
* Spans route: Also switches to the new search query builder.
* Seed spanSpammer task: Adds a 10s trace with events and metadata
operations for testing.
Moving usage updates into the run engine to prevent inefficient &
additional incremental updates to the TaskRun table. Read/Modify/Write
pattern is safe inside of the run engine because of the run lock. We can
also now cap the usageDurationMs value from overflowing and causing an
error.
## Why?
This is preventing at least one update per TaskRun and instead updating
these values piggybacking on other updates.
## Aurora PostgreSQL Reader Consistency Notes
### TL;DR
Aurora readers share the same storage as the writer, but maintain
separate in-memory page caches. This means:
- **Storage is always consistent** - writes are synchronously committed
to shared storage
- **Page cache can lag** - typically <100ms, but can cause stale reads
if data is cached
### How It Works
1. Writer commits to shared storage (synchronous 4/6 quorum)
2. Writer sends cache invalidation messages to readers (asynchronous)
3. If reader has data in cache → returns cached (potentially stale)
value
4. If reader has cache miss → fetches from shared storage (always
current)
### Monitoring
```sql
SELECT server_id,
CASE WHEN session_id = 'MASTER_SESSION_ID' THEN 'Writer' ELSE 'Reader' END AS role,
replica_lag_in_msec
FROM aurora_replica_status();
```
## Summary
- Store the original user-provided idempotency key and scope alongside
the hash
- Expose `ctx.run.idempotencyKey` as the user-provided key (not the
hash)
- Add `ctx.run.idempotencyKeyScope` to show the scope ("run", "attempt",
or "global")
<img width="539" height="450" alt="CleanShot 2026-01-19 at 11 40 46"
src="https://github.com/user-attachments/assets/b6f42991-697e-4314-a164-aef77b8fd25c"
/>
## Problem
Idempotency keys were hashed (SHA-256) before storage, making debugging
difficult since users couldn't see the value they originally set or
search for runs by idempotency key.
## Solution
Attach metadata to the `String` object returned by
`idempotencyKeys.create()` using a Symbol, extract it in the SDK before
the API call, and store it in the database alongside the hash.
```typescript
const key = await idempotencyKeys.create("my-key", { scope: "global" });
await childTask.triggerAndWait(payload, { idempotencyKey: key });
// In child task:
ctx.run.idempotencyKey // "my-key" (previously showed the hash)
ctx.run.idempotencyKeyScope // "global"
```
Test plan
- Trigger task with idempotencyKeys.create() using different scopes (run, attempt, global)
- Verify ctx.run.idempotencyKey returns user-provided key
- Verify ctx.run.idempotencyKeyScope returns correct scope
- Verify PostgreSQL stores idempotencyKeyOptions JSON
- Verify ClickHouse receives idempotency_key_user and idempotency_key_scope via replication
---------
Co-authored-by: James Ritchie <james@trigger.dev>
## Summary
- Upgrades Node.js from 20.19.0 to 20.20.0 (and 22.12.0 to 22.22.0 for
supervisor) to address the async_hooks stack overflow DoS vulnerability
- Adds `maxDepth` parameter (default 128) to `flattenAttributes` and
`unflattenAttributes` to prevent stack overflow on maliciously deep
nested structures
## Details
The vulnerability (patched in Node.js 20.20.0, 22.22.0, 24.13.0, 25.3.0)
causes unrecoverable crashes (exit code 7) when stack overflow occurs
during async_hooks callbacks. Since the webapp uses `AsyncLocalStorage`,
it was theoretically vulnerable.
### Changes
**Node.js version updates:**
- `docker/Dockerfile`: 20.11.1 → 20.20.0
- `apps/supervisor/Containerfile`: 22-alpine → 22.22.0-alpine
- `.nvmrc`: 20.19.0 → 20.20.0
- `apps/supervisor/.nvmrc`: 22.12.0 → 22.22.0
- `references/prisma-7/.nvmrc`: 20.19.0 → 20.20.0
- All GitHub workflows: 20.19.0 → 20.20.0
**Defense in depth:**
- Added `maxDepth` parameter to `flattenAttributes()` and
`unflattenAttributes()` in `packages/core` to prevent stack overflow on
deeply nested user input
## Test plan
- [x] All existing `flattenAttributes` tests pass (50 tests)
- [x] New tests for depth limiting added
- [x] Verify Docker builds work with new base images
Closes #<issue>
## ✅ 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 log detail view displays correctly with message, metadata,
and attributes
- Tested search highlighting functionality in log messages (escapes
special regex characters)
- Confirmed tabs (Details/Run) switch properly with keyboard shortcuts
(d/r)
- Verified run information loads via async fetcher in Run tab
- Tested close button and Escape key for dismissing the panel
- Verified log details display correct information: level badges, kind
badges, timestamps, trace IDs, span IDs
- Confirmed links to parent spans and run pages work correctly
- Tested with various log levels (ERROR, WARN, INFO, DEBUG, TRACE) and
kinds (SPAN, SPAN_EVENT, LOG_*)
- Verified admin-only fields display correctly when user has admin
access
- Tested data loading states and error states (log not found, run not
found)
---
## Changelog
Created new Logs page.
The information shown is gathered from the spans from each run.
The feature supports all run filters with two new filters for level and
logs text search.
---
## Screenshots
<img width="2059" height="1196" alt="Logs page preview"
src="https://github.com/user-attachments/assets/70b667b4-98cc-4728-855a-2766dd5c1aa5"
/>
💯
---------
Co-authored-by: James Ritchie <james@trigger.dev>
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
```
This PR adds support for CLI deployments using the native build server.
**Background**
The deployment command currently does the following:
- bundles the code
- submits the build context to our external build provider and waits for
the build
- triggers deployment state transitions using the platform API
Upstream build provider outages cause issue with deployments,
potentially blocking deployments entirely. We recently introduced the
`--force-local-build` flag as a fallback to enable deployment without a
dependency on the upstream build provider, though it requires users to
have docker in their systems. This PR continues that work by providing a
remote build path which uses our own build server and does not rely on
the external provider.
**Changes in this PR**
Introduced the new `--native-build-server` flag, which does the
following:
- scans all files relevant for the Trigger deployment and evaluates
ignore rules
- packages it up in an archive and uploads it as a deployment artifact
- queues the deployment and triggers the build
- streams logs from the build server
This no longer relies on external build services. Also deployment state
transitions happen on the server-side, giving us more flexibility to
evolve the flow and schemas of related deployment API endpoints. In
general it gives us better control of the whole build and deployment
process. This path will eventually become the default.
The `--detach` flag is also new, allowing to trigger deployments without
waiting for the result.
The deployment artifacts are uploaded via pre-signed URLs to avoid
unnecessary load on the platform. The new `/artifacts` endpoint
generates the pre-signed URLs; size limits are enforced on s3. This
endpoint is deliberately generic, we could extend it in the future to
upload other artifacts client-side in a similar way, e.g., large payload
packets.
* prisma extension fixes WIP
* More prisma stuff
* more prisma stuff
* remove changelog
* upgrade github workflows to use node 20.19 because installing prisma@7 breaks with lower versions
* Don't use generate for the prisma reference projects
* make sure it works if no mode is passed in
* feat(queues): add ability to override concurrency limit via API and dashboard
* Updates the modal layout and tweaks copy
* Improves the dropdown menu item
* Popover supports both Button and LinkButton
* Right align the columns and fix the dropdown menu item styles
* Organize imports,
* Fix spinner icon in dropdown menu
* Remove unused props
* Adds a tooltip to the Concurrency override badge
* Fixes console error with popover menu
* typo
* Fixes incorrect className
* Minimal buttons to view runs
---------
Co-authored-by: Eric Allam <eallam@icloud.com>
* Cancel run events which then propogate cancellation status to span ancestors
* WIP
* convert closing cached run spans to new system
* converted expired complete span event to new method
* move v3 over to new methods
* Convert getDetailedTraceSummary to use the new ancestor override stuff
* remove debug logs
* Don't return UNSPECIFIED task events in getRunEvents
* fix the call site for cancelling run event in v3
* Add changeset
* remove methods
* Change the color to indigo
* Pro tier pricing information now matches the marketing site
* Update the button styles to secondary
* WIP adding separate links to Parent and Root runs
* TextLink now supports optional shortcuts
* Adds shortcut keys to the root and parent links + the shortcut help panel
* Adds new icons for root and parent
* root friendlyId works
* Updates icons for jump to root and parent
* Copy tweak
* Improve how the Free tier shows no preview branches
* Improve the wording in the tooltip
* Align the x icon better
* Show price for additional preview branches
* Change the shortcut key
* Fixes button alignment
* Adds nested dependencies task hello-world
* Fixes typo “Cancelled”
* Removes taskIdentifier, not needed
* Removes unused taskIdentifier
* Mo-Stashed changes
* fix(core): prettyPrintingPacket will now do a structuredClone on non-circular references instead of outputting [Circular]
This also fixes an issue with replaying of runs that include
non-circular references
* feat(engine): Improve execution stalls troubleshooting, align dev and prod behavior, adding heartbeats.yield utility
* A few improvements via the 🐇 review
* Allow treating EXECUTION stalls as OOM errors, improve the error message, add more information to the docs, improve resource monitor and add it to the docs
* Add changeset
* fix: external traces now respect parent sampling, and prevent broken traces when there is no external trace context
* Add changeset
* improve trace flag handling and better internal host checking
* the traceFlags are now being properly passed through as a number
* Map new allowedMasterQueues → allowedWorkerQueues
* ClickHouse worker_queue on task runs
* Added the Region to the run inspector
* Pass a region in when triggering
* Added a changeset
* Added triggering regions docs
* Added region to the ctx
* Fix for backfiller masterQueue/workerQueue
* Update zod package to version 3.25.76 across all modules
Update the zod library from version 3.23.8 to 3.25.76 in multiple package files to ensure compatibility and take advantage of new features or bug fixes introduced in recent releases. Keeping all modules synchronized with the latest version of zod helps maintain consistency across the project and reduces potential compatibility issues.
- Modified zod version in apps/supervisor, webapp, and various internal packages.
- Updated zod references in pnpm-lock.yaml to reflect the new version.
- Ensure dependencies that rely on zod are using the updated version to avoid mismatches.
* Add changeset
* Add payload schema handling for task indexing
This change introduces support for handling payload schemas during task indexing. By incorporating the `payloadSchema` attribute into various components, we ensure that each task's payload structure is clearly defined and can be validated before processing.
- Updated the TaskManifest and task metadata structures to include an optional `payloadSchema` attribute. This addition allows for more robust validation and handling of task payloads.
- Enhanced several core modules to export and utilize the new `getSchemaToJsonSchema` function, providing easier conversion of schema types to JSON schemas.
- Modified the database schema to store the `payloadSchema` attribute, ensuring that the payload schema information is persisted.
- The change helps in maintaining consistency in data handling and improves the integrity of task data across the application.
* Refactor: Remove getSchemaToJsonSchema in favor of schemaToJsonSchema
The `getSchemaToJsonSchema` function was removed and replaced with `schemaToJsonSchema` across the codebase. This update introduces a new `@trigger.dev/schema-to-json` package to handle conversions of schema validation libraries to JSON Schema format, centralizing the functionality and improving maintainability.
- Removed `getSchemaToJsonSchema` exports and references.
- Added new schema conversion utility `@trigger.dev/schema-to-json`.
- Updated `trigger-sdk` package to utilize `schemaToJsonSchema` for payloads.
- Extensive testing coverage included to ensure conversion accuracy across various schema libraries including Zod, Yup, ArkType, Effect, and TypeBox.
- The update ensures consistent and reliable schema conversions, facilitating future enhancements and supporting additional schema libraries.
* Add support for Zod 4 in schema-to-json
This change enhances the schema-to-json package by adding support for Zod version 4, which introduces the native `toJsonSchema` method. This method facilitates a direct conversion of Zod schemas to JSON Schema format, improving performance and reducing reliance on the `zod-to-json-schema` library.
- Updated README to reflect Zod 4 support with native method and retained support for Zod 3 via existing library.
- Modified package.json to allow installation of both Zod 3 and 4 versions.
- Implemented handling for Zod 4 schemas in `src/index.ts` using their native method.
- Added a test case to verify the proper conversion of Zod 4 schemas to JSON Schema.
- Included a script for updating the package version based on the root package.json.
- Introduced a specific TypeScript config for source files.
* Revise schema-to-json for bundle safety and tests
The package @trigger.dev/schema-to-json has been revised to ensure bundle safety by removing direct dependencies on schema libraries such as Zod, Yup, and Effect. This change minimizes bundle size and enhances tree-shaking by allowing external conversion libraries to be utilized only at runtime if necessary. As a result, the README was updated to reflect this usage pattern.
- Introduced `initializeSchemaConverters` function to load necessary conversion libraries at runtime, keeping the base package slim.
- Adjusted test suite to initialize converters before tests, ensuring accurate testing of schema conversion capabilities.
- Updated `schemaToJsonSchema` function to dynamically check for availability of conversion libraries, improving flexibility without increasing the package size.
- Added configuration files for Vitest to support the new testing framework, reflecting the transition from previous test setups.
These enhancements ensure that only the schema libraries actively used in an application are bundled, optimizing performance and resource usage.
* Refine JSON Schema typing across packages
The changes introduce stricter typing for JSON Schema-related definitions, specifically replacing vague types with more precise ones, such as using `z.record(z.unknown())` instead of `z.any()` and `Record<string, unknown>` in place of `any`. This is part of an effort to better align with common practices and improve type safety in the packages.
- Updated the `payloadSchema` in several files to use `z.record(z.unknown())`, enhancing the type strictness and consistency with JSON Schema Draft 7 recommendations.
- Added `@types/json-schema` as a dependency, utilizing its definitions for improved type clarity and adherence to best practices in TypeScript.
- Modified various comments to explicitly mention JSON Schema Draft 7, ensuring developers are aware of the JSON Schema version being implemented.
- These adjustments are informed by research into how popular libraries and tools handle JSON Schema typing, aiming to integrate best practices for improved maintainability and interoperability.
* Add JSON Schema examples using various libraries
The change introduces extensive examples of using JSON Schemas in the 'references/hello-world' project within the 'trigger.dev' repository. These examples utilize libraries like Zod, Yup, and TypeBox for JSON Schema conversion and validation. The new examples demonstrate different use cases, including automatic conversion with schemaTask, manual schema provision, and schema conversion at build time. We also updated the dependencies in 'package.json' to include the necessary libraries for schema conversion and validation.
- Included examples of processing tasks with JSON Schema using libraries such as Zod, Yup, TypeBox, and ArkType.
- Showcased schema conversion techniques and type-safe JSON Schema creation.
- Updated 'package.json' to ensure all necessary dependencies for schema operations are available.
- Created illustrative scripts that cover task management from user processing to complex schema implementations.
* Refactor SDK to encapsulate schema-to-json package
The previous implementation required users to directly import and initialize functions from the `@trigger.dev/schema-to-json` package, which was not the intended user experience. This change refactors the SDK so that all necessary functions and types from `@trigger.dev/schema-to-json` are encapsulated within the `@trigger.dev/*` packages.
- The examples in `usage.ts` have been updated to clearly mark `@trigger.dev/schema-to-json` as an internal-only package.
- Re-export JSON Schema types and conversions in the SDK to improve developer experience (DX).
- Removed unnecessary direct dependencies on `@trigger.dev/schema-to-json` from user-facing code, ensuring initialization and conversion logic is handled internally.
- Replaced instances where users were required to manually perform schema conversions with automatic handling within the SDK for simplification and better maintainability.
* Add JSONSchema type for payloadSchema in tasks
The change was necessary to improve type safety by using a proper JSONSchema type definition instead of a generic Record<string, unknown>. This enhances the developer experience and ensures that task payloads conform to the JSON Schema Draft 7 specification. The JSONSchema type is now re-exported from the SDK for user convenience, hiding internal complexity and maintaining a seamless developer experience.
- Added JSONSchema type based on Draft 7 specification
- Updated task metadata and options to use JSONSchema type
- Hid internal schema conversion logic from users by re-exporting types from SDK
- Improved bundle safety and dependency management
* Add JSON schema testing and revert package dependencies
This commit introduces a comprehensive set of JSON schema testing within the monorepo, specifically adding a new test project in `references/json-schema-test`. This includes a variety of schema definitions and tasks utilizing multiple validation libraries to ensure robust type-checking and runtime validation.
Additionally, the dependency versions for `@effect/schema` have been adjusted from `^0.76.5` to `^0.75.5` to maintain compatibility across the project components. This ensures consistent behavior and compatibility with existing code bases without introducing breaking changes or unexpected behavior due to version discrepancies.
Key updates include:
- Added new test project with extensive schema validation tests.
- Ensured type safety across various task implementations.
- Reverted dependency versions to ensure compatibility.
- Created multiple schema tasks using libraries like Zod, Yup, and others for thorough testing.
* Refactor JSON Schema test files for clarity
Whitespace and formatting changes were applied across the `json-schema-test` reference project to enhance code readability and cohesion. This included removing unnecessary trailing spaces and ensuring consistent indentation patterns, which improves maintainability and readability by following the project's code style guidelines.
- Renamed JSONSchema type annotations to adhere to TypeScript conventions, ensuring that all schema definitions properly satisfy the JSONSchema interface.
- Restructured some object declarations for improved clarity, especially within complex schema definitions.
- These adjustments are crucial for better future maintainability, reducing potential developer errors when interacting with these test schemas.
* Fixed some stuff
* WIP
* we now convert schema to jsonSchema on the CLI side via the indexing
* Remove the json-schema-test reference project
* Improve schema-to-json peer deps and fix effect schema
* Explain the casting and match the version numbers
* Fixed a bunch more schema stuff
* Don't clean files that might be written to
* Don't use a custom version of vitest in the new package
* fix attw in schema-to-json
Eric Allam