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Hatchet – open-source, event-based workflow engine

Hacker News

Hatchet – open-source, event-based workflow engine

Hey HN - over the past few months, I’ve been working on Hatchet, which is a workflow engine meant for event-driven applications. Workflows are triggered by user-defined events (along with schedules and cron jobs). It bundles a web UI for observability and has several integrations built out (Sentry, OpenTelemetry, Slack). It’s very similar to using an SQS + EventBridge + AWS Lambda stack, but uses worker pools to process functions instead of isolated function invocations. Some features that Hatchet offers: 1. Publish, react to, and query events using the Events API [1] 2. You can define services via `worker.NewService` which can run a set of methods. For example, you can define a `users` service which schedules user notifications, creates a user sandbox environment, processes user events, etc. 3. It’s designed to be modular, so you can plug in your own task queue implementation or event storage repository. I built it using Postgres and RabbitMQ, but would love to support better event storage via Clickhouse and other message queues. 4. Workflows are declarative, which means you define the structure of your workflow when registering them with the Hatchet engine. This means that each workflow run is predictable and executes an immutable workflow version. It currently only supports a Go SDK but Typescript and Python are on the near-term roadmap. Looking forward to hearing any questions or feedback you have! [1] https://docs.hatchet.run/go-sdk/pushing-events

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Product HuntOn track for Day 1 leaderboard · Strong signals: slack, user, new · Missing: mac, agents, macos
96%96% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsStrong engagement from HN community · Strong signals: clickhouse, io · Missing: https docs, excited, just released
82%82% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Strong signals: supports · Missing: reddit linkedin, podcasting, created
67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month, users · Missing: mobile apps, ios, personal
31%31% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
22%22% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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