Important

On June 26 2024, Linux Foundation announced the merger of its financial services umbrella, the Fintech Open Source Foundation (FINOS), with OS-Climate, an open source community dedicated to building data technologies, modeling, and analytic tools that will drive global capital flows into climate change mitigation and resilience; OS-Climate projects are in the process of transitioning to the FINOS governance framework; read more on finos.org/press/finos-join-forces-os-open-source-climate-sustainability-esg

osc-dm-monitor-srv - Ecosystem Platform Monitor

Ecosystem Platform Monitor is a service for to monitor components in Broda Group Software's Ecosystem Platform.

Full documentation is available in in the osc-dm-mesh-doc repo.

This application interacts with other applications. You can run the full set of applications by following instructions in the osc-dm-mesh-doc repo.

The remaining sections explain how to Dockerize the application as well as providing a few developer notes.

Prerequisites

Python must be available, preferably in a virtual environment (venv).

Setting up your Environment

Some environment variables are used by various source code and scripts. Setup your environment as follows (note that "source" is used)

source ./bin/environment.sh

It is recommended that a Python virtual environment be created. We have provided several convenience scripts to create and activate a virtual environment. To create a new virtual environment using these convenience scripts, execute the following (this will create a directory called "venv" in your current working directory):

$PROJECT_DIR/bin/venv.sh

Once your virtual enviornment has been created, it can be activated as follows (note: you must activate the virtual environment for it to be used, and the command requires "source" to ensure environment variables to support venv are established correctly):

source $PROJECT_DIR/bin/vactivate.sh

Install the required libraries as follows:

pip install -r requirements.txt

Note that if you wish to run test cases then you will need to also install "pytest" (it is not installed by default as it is a development rather than product dependency).

pip install pytest

Creating a Docker Image

A Dockerfile is provided for this service. A docker image for this service can be creating using the following script, which will create but not publish the image:

$PROJECT_DIR/bin/dockerize.sh

In order to publish this image the DOCKER_TOKEN environment variable must be set to a dockerhub token that is associated with the username set in the DOCKER_USERNAME environment variable. Additionally, the DOCKER_REGISTRY environment variable must be set if publishing to a custom registry.

Then the below command can be executed to create and publish an image, with the --publish argument controlling whether the image is published, and where it is published to. The --latest argument controls whether a specific version is published, or whether this version will also be published as "latest". The --version argument controls what specific version number the image will have when published.

$PROJECT_DIR/bin/dockerize.sh --publish [false|custom|dockerhub] [--latest] [--version <version>]

Starting the Service

This service is designed to work with other services and can be started with the full set of Data Mesh components. Information about starting the full set of components can be found here

A standalone server can be started for testing purposes using the following command:

$PROJECT_DIR/app/start.sh