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https://github.com/fabienarcellier/oautom
oautom is educational workflow engine implementation able to run step by step treatment using directed acyclic graph (dag).
https://github.com/fabienarcellier/oautom
Last synced: 26 days ago
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oautom is educational workflow engine implementation able to run step by step treatment using directed acyclic graph (dag).
- Host: GitHub
- URL: https://github.com/fabienarcellier/oautom
- Owner: FabienArcellier
- License: mit
- Created: 2020-05-17T14:28:50.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2021-03-20T04:05:55.000Z (almost 4 years ago)
- Last Synced: 2024-08-10T07:51:12.265Z (5 months ago)
- Language: Python
- Homepage:
- Size: 50.8 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# OAutom
`oautom` is **educational** workflow engine implementation able to
run step by step treatment using directed acyclic graph (dag).
The purpose of a workflow engine is to manage long running tasks (few hours to few days).
It allow to have an overview of serie of tasks.
OAutom is design to be embedded as a library in command line application or in a single-thread
webapplication.
OAutom is not designed to be use on a production environment because :
* this workflow engine is not safe because all the state are stored `in-memory`
* this workflow engine does not support `Flow` execution concurrency
* this workflow engine does not support to give parameter to an execution
* this workflow engine does not implement variable forwarding between execution step
* this workflow engine does not implement dag integrity checking
if you are looking for a mature workflow engine, you should take a look to
[`airflow`](https://airflow.apache.org/docs/stable/tutorial.html#setting-up-dependencies) from
which oautom reuse the declarative API.
## Getting started
```python
oautom = OAutom(mode=OAutomMode.background)
flow = Flow('flow 1', app=oautom)
step1 = BashExecution('execution 1', flow=flow, command='touch /tmp/file1')
step2 = BashExecution('sleep', flow=flow, command='sleep 60')
step3 = BashExecution('execution 2', flow=flow, command='touch /tmp/file2')
step2.depends(step1)
step3.depends(step2)
```
more examples in [oautom/examples](oautom/examples)
## Concepts
* `Execution` should run async command in `run` and check completion through `check`
* `BashExecution` allows to run shell command
* a `Flow` is a directed acyclic graph of steps
* a `Vect` is a running instance of a `Flow`
* only one instance of each `Flow` may run in same time
### System requirements
The following requirements has to be setup on your host before running the command
from this repository.
* `python 3.6` at least
* [pipenv](https://pipenv.pypa.io/en/latest/)
### Install the python dependencies
```bash
make install_requirements_dev
make start
```
## The latest version
You can find the latest version to ...
```bash
git clone https://github.com/FabienArcellier/oautom.git
```
more information on how to use oautom in [oautom/examples](oautom/examples)
## Usage
```bash
pip install oautom
```
## Contributing
### Install development environment
Use make to instanciate a python virtual environment in ./venv3 and install the
python dependencies.
```bash
make install_requirements_dev
```
### Freeze the library requirements
If you want to freeze all the packages, use
this procedure
```bash
make freeze_requirements
```
### Activate the python environment
When you setup the requirements, a `venv3` directory on python 3 is created.
To activate the venv, you have to execute /
```bash
make activate
```
### Run the linter and the unit tests
Before commit or send a pull request, you have to execute pylint to check the syntax
of your code and run the unit tests to validate the behavior.
```bash
make lint
make tests
```
## Contributors
* Fabien Arcellier
## License
A short snippet describing the license (MIT, Apache, etc.)