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https://github.com/dbos-inc/dbos-transact-py
Ultra-Lightweight Durable Execution in Python
https://github.com/dbos-inc/dbos-transact-py
dbos microservice-orchestration orchestration postgresql python workflow workflow-engine
Last synced: 6 days ago
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Ultra-Lightweight Durable Execution in Python
- Host: GitHub
- URL: https://github.com/dbos-inc/dbos-transact-py
- Owner: dbos-inc
- License: mit
- Created: 2024-07-19T15:53:44.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2024-12-20T00:02:56.000Z (16 days ago)
- Last Synced: 2024-12-20T00:29:50.123Z (16 days ago)
- Topics: dbos, microservice-orchestration, orchestration, postgresql, python, workflow, workflow-engine
- Language: Python
- Homepage: https://docs.dbos.dev
- Size: 815 KB
- Stars: 369
- Watchers: 5
- Forks: 9
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
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README
# DBOS Transact: Ultra-Lightweight Durable Execution
#### [Documentation](https://docs.dbos.dev/) • [Examples](https://docs.dbos.dev/examples) • [Github](https://github.com/dbos-inc) • [Discord](https://discord.com/invite/jsmC6pXGgX)
---
DBOS Transact is a Python library providing **ultra-lightweight durable execution**.
For example:
```python
@DBOS.step()
def step_one():
...
@DBOS.step()
def step_two():
...
@DBOS.workflow()
def workflow()
step_one()
step_two()
```
Durable execution means your program is **resilient to any failure**.
If it is ever interrupted or crashes, all your workflows will automatically resume from the last completed step.
If you want to see durable execution in action, check out [this demo app](https://demo-widget-store.cloud.dbos.dev/) (source code [here](https://github.com/dbos-inc/dbos-demo-apps/tree/main/python/widget-store)).
No matter how many times you try to crash it, it always resumes from exactly where it left off!
Under the hood, DBOS Transact works by storing your program's execution state (which workflows are currently executing and which steps they've completed) in a Postgres database.
So all you need to use it is a Postgres database to connect to—there's no need for a "workflow server."
This approach is also incredibly fast, for example [25x faster than AWS Step Functions](https://www.dbos.dev/blog/dbos-vs-aws-step-functions-benchmark).
Some more cool features include:
- Scheduled jobs—run your workflows exactly-once per time interval.
- Exactly-once event processing—use workflows to process incoming events (for example, from a Kafka topic) exactly-once.
- Observability—all workflows automatically emit [OpenTelemetry](https://opentelemetry.io/) traces.
## Getting Started
Install and configure with:
```shell
pip install dbos
dbos init --config
```
Then, try it out with this simple program (requires Postgres):
```python
from fastapi import FastAPI
from dbos import DBOS
app = FastAPI()
DBOS(fastapi=app)
@DBOS.step()
def step_one():
print("Step one completed!")
@DBOS.step()
def step_two():
print("Step two completed!")
@DBOS.workflow()
def dbos_workflow():
step_one()
for _ in range(5):
print("Press Control + \ to stop the app...")
DBOS.sleep(1)
step_two()
@app.get("/")
def fastapi_endpoint():
dbos_workflow()
```
Save the program into `main.py`, edit `dbos-config.yaml` to configure your Postgres connection settings, and start it with `fastapi run`.
Visit `localhost:8000` in your browser to start the workflow.
When prompted, press `Control + \` to force quit your application.
It should crash midway through the workflow, having completed step one but not step two.
Then, restart your app with `fastapi run`.
It should resume the workflow from where it left off, completing step two without re-executing step one.
To learn how to build more complex workflows, see our [programming guide](https://docs.dbos.dev/python/programming-guide) or [examples](https://docs.dbos.dev/examples).
## Documentation
[https://docs.dbos.dev](https://docs.dbos.dev)
## Examples
- [**AI-Powered Slackbot**](https://docs.dbos.dev/python/examples/rag-slackbot) — A Slackbot that answers questions about previous Slack conversations, using DBOS to durably orchestrate its RAG pipeline.
- [**Widget Store**](https://docs.dbos.dev/python/examples/widget-store) — An online storefront that uses DBOS durable workflows to be resilient to any failure.
- [**Earthquake Tracker**](https://docs.dbos.dev/python/examples/earthquake-tracker) — A real-time earthquake dashboard that uses DBOS to stream data from the USGS into Postgres, then visualizes it with Streamlit.
More examples [here](https://docs.dbos.dev/examples)!
## Community
If you're interested in building with us, please star our repository and join our community on [Discord](https://discord.gg/fMwQjeW5zg)!
If you see a bug or have a feature request, don't hesitate to open an issue here on GitHub.
If you're interested in contributing, check out our [contributions guide](./CONTRIBUTING.md).