Comment by qianli_cs
Comment by qianli_cs 6 months ago
Hello! I'm a co-founder at DBOS here and I'm happy to answer any questions :)
Comment by qianli_cs 6 months ago
Hello! I'm a co-founder at DBOS here and I'm happy to answer any questions :)
Hi there, I think I might have found a typo in your example class in the github README. In the class's `workflow` method, shouldn't we be `await`-ing those steps?
It's not recommended--the assumed model is that every workflow finishes on the code version it started. This is managed automatically in our hosted version (DBOS Cloud) and there's an API for self-hosting: https://docs.dbos.dev/typescript/tutorials/development/self-...
That said, we know sometimes you have to do surgery on a long-running workflow, and we're looking at adding better tooling for it. It's completely doable because all the state is stored in Postgres tables (https://docs.dbos.dev/explanations/system-tables).
I know this this might sound scripted or can be considered cliche but what is the use case for DBOS.
The main use case is to build reliable programs. For example, orchestrating long-running workflows, running cron jobs, and orchestrating AI agents with human-in-the-loop.
DBOS makes external asynchronous API calls reliable and crashproof, without needing to rely on an external orchestration service.
How do you persist execution state? Does it hook into the Python interpreter to capture referenced variables/data structures etc, so they are available when the state needs to be restored?
That work is done by the decorators! They wrap around your functions and store the execution state of your workflows in Postgres, specifically:
- Which workflows are executing
- What their inputs were
- Which steps have completed
- What their outputs were
Here's a reference for the Postgres tables DBOS uses to manage that state: https://docs.dbos.dev/explanations/system-tables
All of this seems it would fit any transactional key value structure.
Each workflow is tagged by the executor ID that runs it. You can command each new executor to handle a subset of the pending workflows. This is done automatically on DBOS Cloud. Here's the self-hosting guide: https://docs.dbos.dev/typescript/tutorials/development/self-...
Yeah, DBOS Cloud automatically (horizontally) scales your apps. For self-hosting, you can spin up multiple instances and connect them to the same Postgres database. For fan-out patterns, you may leverage DBOS Queues. This works because DBOS uses Postgres for coordination, rate limiting, and concurrency control. For example, you can enqueue tasks that are processed by multiple instances; DBOS makes sure that each task is dequeued by one instance.
Docs for Queues and Parallelism: https://docs.dbos.dev/typescript/tutorials/queue-tutorial
Hi! How does it perform under heavy load and with thousands of workflows trying to run concurrently since it relies on Postgres for a lot of things (including using a transaction)? In the end it seems that if I have an application with lots of distributed workers trying to run workflows, I'll still be limited by the CPU/memory of the DB.