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Practical Log Viewers with Sanic and Elasticsearch - Designing CI/CD Systems

One of the critical pieces in a build system is the ability to view build and test output. Not only does it track progress as the build transitions through the various phases, it’s also an instrument for debugging.

This chapter in the continuous builds series covers how to build a simple log viewer. You’ll find details on retrieving log entries from Docker containers, serving them through Python, linking from a GitHub pull request, and highlighting the data for easy reading.

Awesome Webhook Handling with Sanic - Designing CI/CD Systems

After covering how to design a build pipeline and define build directives in the continuous builds series, it’s time to look at handling events from a code repository.

As internet standards evolved over the years, the HTTP protocol has become more prevalent. It’s easier to route, simpler to implement and even more reliable. This ubiquity makes it easier for applications that traverse or live on the public internet to communicate with each other. As a result of this, the idea of webhooks came to be as an “event-over-http” mechanism.

With GitHub as the repository management platform, we have the advantage of using their webhook system to communicate user actions over the internet and into our build pipeline.

Effortless Parsing of Build Specifications - Designing CI/CD Systems

Every code repository is different. The execution environment, the framework, the deliverables, or even the linters, all need some sort of customization. Creating a flexible build system requires a mechanism that specifies the steps to follow at different stages of a pipeline.

As the next chapter in the Comprehensive CI/CD Pipeline and System Design series, this article examines which instructions you’ll want to convey to your custom system and how to parse them. The focus is around a common solution, adding a file into the repository’s root directory that’s read by your execution engine when receiving new webhooks.

Integrating Pytest Results with GitHub

When joining a new engineering team, one of the first things I do is familiarize myself with the dev and test processes. Especially the tools used to enforce them. In the past 5 years or so, I’ve noticed that a lot of organizations still use older tools that haven’t yet evolved to support modern practices. Even teams that purely develop software can find themselves working around cumbersome systems that hinder instead of enable.

What do I mean by that? Very few of these tools include useful interfaces to leverage integrations with other systems (like REST APIs). Most have no concept of modern dev practices like continuous integration or containerization. Almost all of them want to record pass / fail at a step by step basis as if you’re executing manually. The vast majority are built around a separation between test and dev (some even emphasize it). And a lot of them require the organization to hire “specialists” for the purpose of “customizing” the tool to the team. In my opinion, these types of systems coerce the organization to emphasize blame over quality and team boundaries over productivity.

I’ve been very successful at building long-lived alternatives to these systems in several organizations. I’ve done it enough to know which features are worth including, and which to leave to the test / dev engineers, especially after the advent of continuous integration and delivery.