jpl.pipedreams 1.0.1

Pipe Dreams: API for publication of scientific data

🔬 Pipe Dreams

This is an application programmer interface (API) to support the construction and processing of data pipes for scientific data, primarily for the Laboratory Catalog and Archive System, but open-ended for other systems.

🚗 Starting Redis

The Pipe Dreams API requires Redis (although apparently not—try it without, it seems to work) to run. To start Redis, run:

$ docker container run \
    --name labcas-redis \
    --publish 5002:5002 \
    --detach \
    redis:6.2.4-alpine

💿 Installing Pipe Dreams

Pipe Dreams is an open source, installable Python packge. It requires Python 3.7 or later. Typically, you'd install it into Python virtual environment, but you can also put it into a Conda or—if you must—your system's Python.

To use a virtual environment, run:

$ python3 -m venv venv
$ venv/bin/pip install --upgrade setuptools pip wheel
$ venv/bin/pip install jpl.pipedreams

Once this is done, you can run venv/bin/python as your Python interpreter and it will have the Pipe Dreams API (and all its dependencies) ready for use.

👩‍💻 Customizing the Workflow

The next step is to create a workflow to define the processing steps to publish the data. As an example, see the demo.py which is available from the GitHub release of this package.

In summary you need to

1. Create an Operation instance.
2. Add pipes to the instance.
3. Run the instance's graph.

📗 Publishing the Data

Finally, with Redis running (or not—seems to work in any case) and a custom workflow defined, you can then execute the publication. For example, using the demo.py the GitHub release:

$ python3 -m venv venv
$ venv/bin/pip install --upgrade setuptools pip wheel
$ venv/bin/pip install jpl.pipedreams
$ curl -LO https://github.com/EDRN/jpl.pipedreams/releases/download/v1.0.1/demo.py
$ curl -L https://github.com/EDRN/jpl.pipedreams/releases/download/v1.0.1/test-data.tar.gz | tar xzf -
$ venv/bin/python demo.py
Adding Node: hello_world_read|+|mydata0.txt
…
num nodes in task graph: 7
num task completed: 7
time taken: 0:00:00.306140

That's it 🥳