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Show HN: DBOS Python 1.0 – Lightweight Durable Workflows Built on Postgres

https://github.com/dbos-inc/dbos-python
6•KraftyOne•9mo ago
Hi HN – this is Peter from DBOS here with Qian (qianli_cs) and Jeremy (jedberg). We’re building an open-source, lightweight durable workflows library on top of Postgres.

Ever since we first launched on HN last year, we’ve been blown away by the support, feedback, and response we’ve received from the community. We've realized durable workflows are critical for everything from business processes to AI automation to data pipelines, but most existing durable orchestration tools are either too heavy or too complicated for most applications. Instead, we're building something lightweight, simple, and easy to drop into any existing app.

The core of DBOS is durable Postgres-backed workflows. The library checkpoints the state of your program (which workflows are running, what steps have completed) directly in Postgres. If your program fails, when it restarts your workflows use these checkpoints to automatically recover from the last completed step. What we really love about DBOS is how lightweight it is–you don’t need to deploy a separate workflow orchestrator or manage new workers, just install this open-source library, connect it to Postgres, and annotate workflows and steps in your program to make them durable.

Today, we’re releasing DBOS Python 1.0. This enhances workflows with many powerful features we’ve been building over the last few months, including:

- Durable queues. Postgres-backed queues with all the queuing features of BullMQ/Celery (concurrency limits, rate limits, timeouts, priority, deduplication, etc.). Plus, they integrate with durable workflows, so you can write a workflow that enqueues 1K tasks, waits for and processes their results, and automatically recovers from any interruption. - Programmatic workflow management. Your workflows are stored as rows in a Postgres table, so you have full programmatic control over them. Write scripts to query workflow executions, batch pause or resume workflows, or even restart failed workflows from a specific step. This makes it much easier to diagnose and recover from bugs and failures that affect thousands of workflows. - Full support for both sync and async Python–write your workflows and steps as code either synchronously or asynchronously, it all works out of the box. - Improved tooling, including dashboards, workflow graph visualization, workflow management via web UI, and more.

We’d love to hear your feedback and hope you can try DBOS out!

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