Eric Allam
32e647e020
## Summary Listing schedules could block the event loop for seconds. A page of 100 timezone-aware schedules spent over two seconds on cron arithmetic alone, after the database work was already done, which stalls every other request on that process. The same page now resolves in tens of milliseconds. ## Root cause and fix `cron-parser` walks the calendar unit by unit, and under a named timezone every step goes through luxon. Parsing an expression is cheap (single-digit microseconds); *stepping* it is not, ranging from a couple of hundred microseconds for a common expression to several milliseconds for a sparse one like `0 0 29 2 *`. The presenter did three independent walks per row, one backwards for "last run" and two forwards (re-parsing each time) for the next run and the occurrence after it. At 100 rows that is 300 calendar walks in one uninterrupted tick. Run times now resolve for the whole page in one pass, in a new `resolveScheduleTimings` that takes plain values rather than Prisma rows so it can be tested and benchmarked on its own. - **Nominal times are cached per `(cron, timezone)`** against a single `now` pinned for the batch, so cost scales with the number of distinct expressions instead of the number of rows. Rows in one response also stop disagreeing about the current time. - **The backwards walk is opt-in.** It is the most expensive of the three and only the dashboard renders the column; the public API never returned it at all. - **Windowless schedules take one step instead of two.** The second step only measures the interval to the following occurrence, and that interval reaches the result solely through `min(intervalMs, max(MINIMUM_SCHEDULE_RANGE_MS, windowMs))`. With no window `windowMs` is 0, and `CronPattern` rejects expressions with a seconds field, so occurrences are always at least `MINIMUM_SCHEDULE_RANGE_MS` apart and that `min` can never bind. It is also the costlier step, since it walks a whole period rather than the remainder of the current one. - **`nextScheduledTimestamps` steps one parsed expression** instead of re-parsing per step, which also helps the single-schedule callers. Behaviour is unchanged, error semantics included: a malformed expression still throws for the next run and still degrades to an undefined last run. ## Verification Measured inside a real request against a live environment, 100 schedules: sparse expressions went from 2250-2652 ms to 23-30 ms, and five distinct timezone expressions from 463-500 ms to 9.7-10.6 ms. The new suite checks the optimized code against an inline copy of the previous implementation across eleven cron and timezone combinations plus five DST transitions, so the rewrite is verified as behaviour-preserving rather than just faster. Separate tests pin the invariant the single-step path depends on, so if sub-minute crons are ever allowed they fail loudly instead of the timings quietly going wrong. Worth knowing for later: `cron-parser` v5 is a much faster rewrite on exactly this workload (`prev()` under a timezone drops from roughly 2700 to 60 microseconds), but it is a breaking API change across several call sites including the schedule engine, so it belongs on its own. The differential test added here is the tool to de-risk it.
Build and deploy fully‑managed AI agents and workflows
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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.
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Long-running without timeouts: Execute your tasks with absolutely no timeouts, unlike AWS Lambda, Vercel, and other serverless platforms.
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Durability, retries & queues: Build rock solid agents and AI applications using our durable tasks, retries, queues and idempotency.
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True runtime freedom: Customize your deployed tasks with system packages – run browsers, Python scripts, FFmpeg and more.
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Human-in-the-loop: Programmatically pause your tasks until a human can approve, reject or give feedback.
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Realtime apps & streaming: Move your background jobs to the foreground by subscribing to runs or streaming AI responses to your app.
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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.