jaws-libp

Reusable Python classes for JAWS plus admin scripts to setup and manage a deployment.

• Quick Start with Compose
• Install
• API
• Configure
• Build
• Develop
• Test
• Release
• See Also

Quick Start with Compose

1. Grab project

git clone https://github.com/JeffersonLab/jaws-libp
cd jaws-libp

2. Launch Compose

docker compose up

3. Monitor active alarms

docker exec -it cli list_activations --monitor

4. Trip an alarm

docker exec cli set_activation alarm1

Note: The docker-compose services require significant system resources - tested with 4 CPUs and 4GB memory.

See: Docker Compose Strategy

Install

Requires Python 3.9+

pip install jaws-libp

Note: Using newer versions of Python may be problematic because the dependency confluent-kafka uses librdkafka, which often does not have a wheel file prepared for later versions of Python, meaning setuptools will attempt to compile it for you, and that often doesn't work (especially on Windows). Python 3.9 does have a wheel file for confluent-kafka so that's your safest bet. Wheel files also generally only are prepared for Windows, MacOS, and Linux. Plus only for architectures x86_64 and arm64, also only for glibc. If you use with musl libc or linux-aarch64 then you'll likely have to compile librdkafka yourself from source.

API

Sphinx Docs

Configure

Environment variables are used to configure jaws-libp:

The Docker container can optionally handle the following environment variables as well:

Build

This Python 3.9+ project is built with setuptools and may be run using the Python virtual environment feature to isolate dependencies. The pip tool can be used to download dependencies.

git clone https://github.com/JeffersonLab/jaws-libp
cd jaws-libp
python -m venv .venv_dev --upgrade-deps

Activate the virtual env using your shell specific command, then install in editable mode with dev deps and run build:

# Windows
.venv_dev\Scripts\activate.bat
# UNIX (SH Shell)
source .venv_dev/bin/activate
# UNIX (CSH Shell)
source .venv_dev/bin/activate.csh


pip install -e ."[dev]"
python -m build
pylint --recursive=y src/*

Note for JLab On-Site Users: Jefferson Lab has an intercepting proxy

See: Python Development Notes

Develop

Set up the build environment following the Build instructions.

In order to iterate rapidly when making changes it's often useful to run the Python scripts directly on the local workstation, perhaps leveraging an IDE. In this scenario run the service dependencies with Docker Compose:

docker compose -f deps.yaml up

Note: The environment variable defaults work in this scenario and are defined as: BOOTSTRAP_SERVERS=localhost:9094 and SCHEMA_REGISTRY=http://localhost:8081

Test

The integration tests depend on a running Kafka instance, generally in Docker. The tests run automatically via the CI GitHub Action on every commit (unless [no ci] is included in the commit message). The tests can be run locally during development. Set up the development environment following the Develop instructions. Then with the deps.yaml Docker containers running and the build virtual environment activated run:

pytest

Release

1. Bump the version number in the VERSION file and commit and push to GitHub (using Semantic Versioning).
2. The CD GitHub Action should run automatically invoking:
   • The Create release GitHub Action to tag the source and create release notes summarizing any pull requests. Edit the release notes to add any missing details.
   • The Publish artifact GitHub Action to create a deployment artifact on PyPi.
   • The Publish docs GitHub Action to create Sphinx docs.
   • The Publish docker image GitHub Action to create a new demo Docker image.

See Also

• jaws-libj (Java)
• Developer Notes