## Summary Adds `drainBatchSize` to `MollifierDrainer` (default `1` — preserves existing behaviour) and wires `TRIGGER_MOLLIFIER_DRAIN_BATCH_SIZE` through the webapp (default `50`). Each tick the drainer now pops up to `drainBatchSize` from each chosen env, then dispatches every popped entry through the shared `concurrency`-bounded `pLimit`. Per-org/per-env fairness is unchanged — only the in-env pop count grows. Pre-existing behaviour was one pop per env per tick. For a single-env burst that single-flighted the drain at the per-tick floor of `pop + engine.trigger ≈ 50–60 ms`. With buffer entries piling up under a real-world tenant burst that's tens of minutes of tail latency to fully materialise — even though PG itself could comfortably sustain the writes. ## Why this matters — heavy-tail illustration Scenario: 100 customers in one window — 94 fire 20 triggers each, 5 fire 100, 1 fires 1000. Gate at `THRESHOLD=10/s`, `HOLD_MS=500`. First 10 of each burst hit PG directly; the rest buffer. | Customers | Triggers each | PG direct | Buffered each | Total buffered | |---|---|---|---|---| | 94 small | 20 | 10 | 10 | 940 | | 5 medium | 100 | 10 | 90 | 450 | | 1 heavy | 1000 | 10 | 990 | 990 | **With `DRAIN_BATCH_SIZE=50`, `DRAIN_CONCURRENCY=50`, ~50 ms `engine.trigger`:** | Tick | Pops | Dispatch waves | Wall-clock | |---|---|---|---| | 1 | 94×10 + 5×50 + 1×50 = 1 240 | 25 × 50 ms | ~1 300 ms (94 smalls done) | | 2 | 5×40 + 1×50 = 250 | 5 × 50 ms | ~300 ms (5 mediums done) | | 3–20 | heavy alone, 50/tick | 1 × 50 ms | ~100 ms each | | Customer class | Buffered fully drained | |---|---| | 94 small | **~1.3 s** | | 5 medium | **~1.6 s** | | 1 heavy | **~3.4 s** | **Without batching (one pop per env per tick — current behaviour):** | Customer class | Buffered fully drained | |---|---| | 94 small | ~500 ms | | 5 medium | ~4.5 s | | 1 heavy | **~49 s** | So the heavy single-tenant tail drops from ~49 s to ~3.4 s (~14× faster) without changing PG load characteristics. Smalls go up slightly in this scenario (500 ms → 1.3 s) because all 100 envs share one tick's dispatch queue — that's the trade we accept for the heavy tail; the worst-case small wait is still inside one tick. PG load is identical either way (50 concurrent inserts at a time, capped by `DRAIN_CONCURRENCY`). ## What changed **`packages/redis-worker`** - New `drainBatchSize` option (default 1 — full backward compat). - `runOnce()` refactored to pop per-env batches in parallel, then dispatch all popped entries through the existing global `pLimit`. Mid-batch pop failure aborts only that env's batch and counts as one failure (same semantic as the old per-env path). - Removed the now-unused `processOneFromEnv` helper. **`apps/webapp`** - `TRIGGER_MOLLIFIER_DRAIN_BATCH_SIZE` env var (default 50, matching `DRAIN_CONCURRENCY`). - Wired into `mollifierDrainer.server.ts`. **Test cloud config** (separate cloud PR): `TRIGGER_MOLLIFIER_DRAIN_BATCH_SIZE="50"` on the worker service. Production rollout deferred until we've watched it on test cloud. ## Test plan - [x] All 25 stub-based drainer tests pass (18 pre-existing + 7 new). 7 new tests under `MollifierDrainer.drainBatchSize`: - pops up to `drainBatchSize` across ticks - global `concurrency` cap still holds when batch > concurrency - mid-batch pop failure isolation - multi-env batch fan-out in one tick - **hierarchical org fairness preserved at `drainBatchSize > 1`** (load-bearing — guards against future regressions to per-env-instead-of-per-org rotation) - mixed success/failure accounting in a batched tick - bounded pops on empty queue (no Lua spam past `drainBatchSize`) - [x] All pre-existing tests still pass unchanged at default `drainBatchSize=1` → backward-compat locked. - [x] `pnpm run build --filter @trigger.dev/redis-worker` clean. - [x] `pnpm run typecheck --filter webapp` clean. - [x] `redisTest` block (real Redis via testcontainers) — couldn't run locally on this branch due to testcontainers runtime discovery; will validate in CI. - [ ] Test-cloud smoke after cloud PR lands: fire `burst 50` against a flagged env and confirm the 50th entry's drain time drops from ~2.5 s to <200 ms. ## Notes - Per-tick memory bound: `maxOrgsPerTick × drainBatchSize` entries can sit in the JS pLimit queue between pop and dispatch. At defaults that's `500 × 50 = 25 000` × ~5 KB snapshot ≈ ~125 MB worst case per worker — well within headroom. - The pre-batch model's strict per-env throughput cap of `1/tick` is documented as the fairness baseline elsewhere. Org-level fairness is what callers actually rely on; this change does not weaken that. 🤖 Generated with [Claude Code](https://claude.com/claude-code) --------- Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Build and deploy fully‑managed AI agents and workflows
Website | Docs | Issues | Example projects | Feature requests | Public roadmap | Self-hosting
About Trigger.dev
Trigger.dev is the open-source platform for building AI workflows in TypeScript. Long-running tasks with retries, queues, observability, and elastic scaling.
The platform designed for building AI agents
Build AI agents using all the frameworks, services and LLMs you're used to, deploy them to Trigger.dev and get durable, long-running tasks with retries, queues, observability, and elastic scaling out of the box.
-
Long-running without timeouts: Execute your tasks with absolutely no timeouts, unlike AWS Lambda, Vercel, and other serverless platforms.
-
Durability, retries & queues: Build rock solid agents and AI applications using our durable tasks, retries, queues and idempotency.
-
True runtime freedom: Customize your deployed tasks with system packages – run browsers, Python scripts, FFmpeg and more.
-
Human-in-the-loop: Programmatically pause your tasks until a human can approve, reject or give feedback.
-
Realtime apps & streaming: Move your background jobs to the foreground by subscribing to runs or streaming AI responses to your app.
-
Observability & monitoring: Each run has full tracing and logs. Configure error alerts to catch bugs fast.
Key features:
- JavaScript and TypeScript SDK - Build background tasks using familiar programming models
- Long-running tasks - Handle resource-heavy tasks without timeouts
- Durable cron schedules - Create and attach recurring schedules of up to a year
- Trigger.dev Realtime - Trigger, subscribe to, and get real-time updates for runs, with LLM streaming support
- Build extensions - Hook directly into the build system and customize the build process. Run Python scripts, FFmpeg, browsers, and more.
- React hooks - Interact with the Trigger.dev API on your frontend using our React hooks package
- Batch triggering - Use batchTrigger() to initiate multiple runs of a task with custom payloads and options
- Structured inputs / outputs - Define precise data schemas for your tasks with runtime payload validation
- Waits - Add waits to your tasks to pause execution for a specified duration
- Preview branches - Create isolated environments for testing and development. Integrates with Vercel and git workflows
- Waitpoints - Add human-in-the-loop judgment at critical decision points without disrupting workflow
- Concurrency & queues - Set concurrency rules to manage how multiple tasks execute
- Multiple environments - Support for DEV, PREVIEW, STAGING, and PROD environments
- No infrastructure to manage - Auto-scaling infrastructure that eliminates timeouts and server management
- Automatic retries - If your task encounters an uncaught error, we automatically attempt to run it again
- Checkpointing - Tasks are inherently durable, thanks to our checkpointing feature
- Versioning - Atomic versioning allows you to deploy new versions without affecting running tasks
- Machines - Configure the number of vCPUs and GBs of RAM you want the task to use
- Observability & monitoring - Monitor every aspect of your tasks' performance with comprehensive logging and visualization tools
- Logging & tracing - Comprehensive logging and tracing for all your tasks
- Tags - Attach up to ten tags to each run, allowing you to filter via the dashboard, realtime, and the SDK
- Run metadata - Attach metadata to runs which updates as the run progresses and is available to use in your frontend for live updates
- Bulk actions - Perform actions on multiple runs simultaneously, including replaying and cancelling
- Real-time alerts - Choose your preferred notification method for run failures and deployments
Write tasks in your codebase
Create tasks where they belong: in your codebase. Version control, localhost, test and review like you're already used to.
import { task } from "@trigger.dev/sdk";
//1. You need to export each task
export const helloWorld = task({
//2. Use a unique id for each task
id: "hello-world",
//3. The run function is the main function of the task
run: async (payload: { message: string }) => {
//4. You can write code that runs for a long time here, there are no timeouts
console.log(payload.message);
},
});
Deployment
Use our SDK to write tasks in your codebase. There's no infrastructure to manage, your tasks automatically scale and connect to our cloud. Or you can always self-host.
Environments
We support Development, Staging, Preview, and Production environments, allowing you to test your tasks before deploying them to production.
Full visibility of every job run
View every task in every run so you can tell exactly what happened. We provide a full trace view of every task run so you can see what happened at every step.
Getting started
The quickest way to get started is to create an account and project in our web app, and follow the instructions in the onboarding. Build and deploy your first task in minutes.
Useful links:
- Quick start - get up and running in minutes
- How it works - understand how Trigger.dev works under the hood
- Guides and examples - walk-through guides and code examples for popular frameworks and use cases
Self-hosting
If you prefer to self-host Trigger.dev, you can follow our self-hosting guides:
- Docker self-hosting guide - use Docker Compose to spin up a Trigger.dev instance
- Kubernetes self-hosting guide - use our official Helm chart to deploy Trigger.dev to your Kubernetes cluster
Support and community
We have a large active community in our official Discord server for support, including a dedicated channel for self-hosting.
Development
To setup and develop locally or contribute to the open source project, follow our development guide.

