supervisor VS starter-workflows

What I went with was having both a web server (Apache/Nginx) and PHP-FPM in the same container image, held together by Supervisor: http://supervisord.org/

In my case, the Dockerfile looks a bit like the following:

  # Whatever base web server image you want, Debian/Ubuntu based here

As you can see there are several methods of running devpi server including cron, launchd (OSX service), nginx, Windows service, and supervisord. It also has a systemd service file which we can use to manage the service easily as Ubuntu uses it for primary service management. First off though we're going to need a proxy script to ensure that devpi is running in the virtual environment:

If it's a linux box you can make it a systemctl service, or you could use http://supervisord.org/.

I used supervisord to start my server and the cloud SQL proxy within the same container. That should work for your use case too.

I convinced (previous) $dayjob to use it. It (nix) kind of hung around in the background with the team that used haskell for awhile, but became prime time when we needed to support a range of VMs running within client infrastructure that were in reality just running various python scripts under supervisord (http://supervisord.org/). The range of client machines (redhat, centos, debian, ubuntu all of different releases) with differing versions of python and supervisord were driving our support and devops teams crazy (but in a weird way - they thought they were being productive, and really enjoyed tweaking things to work with additional varieties of os...). Additionally, having to work around some minor pain points of supervisord (adding and removing config files and not interrupting running services) lead to the realisation that there was a perfectly good service manager at the bottom of the modern versions of these systems (systemd) and that nixos was just a nix wrapper around this systemd and it would only restart what actually changed...

Here's more information about the configuration file format, in case anyone is curious: http://supervisord.org/configuration.html

My problem is that it's not always immediately clear how software that would normally run as a systemd service could be launched in the foreground instead. It usually takes a bit of digging around.

This post is structured as follows: The first chapter Requirements and Design, describes the requirements for such an application, defines its processes, breaks it down into logical components, and proposes a data model. The second chapter Implementation, provides an introduction to key implementation issues, such as implementing asynchronous tasks and LDAP authentication. It also showcases the usage of Docker with Python SDK in the project, including network configuration, and describes the deployment configuration using supervisord. The final chapter summarizes the efforts and provides links to the code repositories.

I have set up supervisord running cron job. However based on the discussion in supervisord GitHub, it is not possible to redirect cron's spawned command output to supervisord.

The project MISP-Docker from Coolacid is providing a containerized version of the MISP solution. This all-in-one solution includes the frontend, background jobs, cronjobs and an HTTP Server (Nginx) all orchestrated by process manager tool called supervisor. External services such as the database and Redis aren’t part of the container but are necessary. We decided that this project is very a good starting point to scale the MISP on Kubernetes.

GitHub Actions is a modern CI/CD tool integrated natively on GitHub. Itenables the rapid automation of build, test, deployment, and other custom workflows on GitHub with no need for external tools.

GitHub Actions is GitHub's CI/CD solution. You can use it to run automated tasks each time you change your code. Although the platform lacks a built-in Kubernetes integration, third-party plugins such as Azure's Deploy to Kubernetes Cluster action can automate deployments and manage different rollout strategies.

GitHub Actions is a feature-rich CI/CD platform embedded within GitHub, enabling developers to automate, customize, and execute software development workflows directly in their repositories. An Action inside GitHub Actions is a discrete unit of automation that performs a specific task within a workflow. All the Actions are reusable, and there are many to choose from. You can even create your own reusable ones.

actions/starter-workflows

The real power of using PHP code-quality tools is when it’s added to your continuous integration process, which means it automatically checks the code every time someone makes a push or pull request to your project repo. In this section, we'll be looking at how to do just that. GitHub actions is available for free so we'll use it for demo purposes. Note that there are some limits to private repos, so set your test repo to public if you can.

You can even automate the running of this script — hence the directory name automation — to happen every time the data changes, using GitHub Actions.

# Based on https://github.com/actions/starter-workflows/blob/main/ci/node.js.yml name: CI on: pull_request: branches: - main jobs: ci: runs-on: ubuntu-latest steps: - uses: actions/checkout@v2 - uses: actions/setup-node@v2 with: node-version: lts/* cache: 'npm' - run: npm ci - run: npm run build --if-present - run: npm test

You might remember that I’ve been taking an interest in GitHub Actions for the last year or so (I even wrote a book on the subject). And at the Perl Conference in Toronto last summer I gave a talk called “GitHub Actions for Perl Development” (here are the slides and the video).

Preevy is designed to be easily run in CI/CD workflows, such as GH Actions, Circle CI and others.

I set out to add further development and contribution tools to my ez-txt2html converter. Specifically, I wanted to introduce an initial Continuous Integration workflow. For this, I used the “Python Application” workflow template in Github Actions to create a CI testing workflow that builds a Py v3.12 environments, builds the project dependencies, runs Flake8 linting and Black formatting and then subsequently runs PyTest using the test suites I’ve written previously.