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Have you ever run into a situation where Git just refused to obey your commands? No, I’m not talking about that time you “typo-ed” git commit and ended up git reset --hard-ing your repository back to the dawn of the universe, I’m talking about it really, truly, ignoring you.

I have, so let me tell you a story about what happened and how I fixed it so that you can avoid future hair-loss and avoid questioning the nature of your reality.

Organizing your Development Directory

As an engineer, I like to think that I help fix problems. That’s what I’ve tried to do most of my life and career and I love doing so to this day. It struck me, though, that there was one problem which has followed me around for years without due attention: the state of my development directories.

That’s not to say that they are disorganized, I’ve spent hours deliberating over the best way to arrange them such that I can always find what I need, yet I often end up having to resort to some dark incantation involving find to locate the project I was certain sat under my Work folder.

No more, I’ve drawn the line and decided that if I can’t fix the problem, automation damn well better be able to!

I’d like to introduce you to my new, standardized (and automated), development directory structure and the tooling I use to maintain it. With any luck, you’ll find it useful and it will enable you to save time, avoid code duplication and more easily transition between machines.

Patterns for APIs

If you’ve built a production API before, you’ll know that they tend to evolve over time. This evolution is not only unavoidable, it is a natural state that any active system will exist in until it is deprecated.

Realizing and designing to support this kind of evolution in a proactive way is one of the aspects that differentiates a mature API from the thousands that litter the Wall of Shame.

At the same time, it is important that your API remains easy to use and intuitive, maximizing the productivity of developers who will make use of it.

Scheduled Backups with Kubernetes

It’s a poorly hidden fact that I love Kubernetes. After spending months running everything from Marathon DCOS and CoreOS to Rancher and Docker Swarm in production, Kubernetes is the only container orchestration platform that has truly struck me as truly “production ready” and I have been running it for the past year as a result.

While functionality when I first started using it (v1.4) was somewhat patchy and uninteresting, some of the more recent updates have been making sizeable strides towards addressing the operations challenges we face on a daily basis.

With v1.8, Kubernetes has introduced the CronJob controller to batch/v1beta1, making it generally available for people to play with. Sounds like the perfect time to show you how we use CronJobs to manage automated, scheduled, backups within our environments.

Relational and Document DBs

One of the most interesting discussions to have with people, notably those with traditional database experience, is that of the relationship between an off the shelf RDBMS and some modern NoSQL document stores.

What makes this discussion so interesting is that there’s invariably a lot of opinion driven from, often very valid, experience one way or another. The truth is that there simply isn’t a silver-bullet database solution and that by better understanding the benefits and limitations of each, one can make vastly better decisions on their adoption.

Out of the Box Docker

Docker is become an incredibly prevalent tool in the development and operations realms in recent months. Its combination of developer friendly configuration and simple operational management make it a very attractive prospect for companies and teams looking to adopt CI and CD practices.

In most cases, you’ll see Docker used to deploy applications in much the same way as a zip file or virtual machine image. This is certainly the most common use case for Docker, but by no means the extent of its functionality.

In this post I’m going to discuss some of the more interesting problems we’ve used Docker to solve and why it serves as a great solution to them.

Dockerizing Aurelia

Aurelia is a modern web application framework in the spirit of Angular, with an exceptionally concise and accessible developer experience and standards compliant implementation. It is hands down my favorite web framework right now and one I’d strongly recommend for most projects.

One of Aurelia’s greatest claims to fame is the incredible productivity you can achieve, enabling you to build a full web application in just days, if not hours.

When building the application becomes that fast, spending a day putting together your deployment pipelines to roll out your application becomes incredibly wasteful, so how can we avoid that?

Well, Docker offers us a great way to deploy and manage the life-cycle of production applications. It enables us to deploy almost anywhere, with minimal additional effort and in a highly reproducible fashion.

In this post I’ll go over the process of Dockerizing an existing Aurelia web application built with WebPack, however the same process applies to those built using SystemJS.

Signing Git Commits using Keybase

With the increasing popularity of Git as a tool for open source collaboration, not to mention distribution of code for tools like Go, being able to verify that the author of a piece of code is indeed who they claim to be has become absolutely critical.

This requirement extends beyond simply ensuring that malicious actors cannot modify the code we’ve published, something GitHub and its kin (usually) do a very good job of preventing. The simple fact is that by adopting code someone else has written, you are entrusting your clients’ security to them - you best be certain that trust is wisely placed.

Using Git’s built in support for PGP signing and pairing it with Keybase provides you with a great framework on which to build and verify that trust. In this post I’ll go over how one sets up their development environment to support this workflow.

Feeling Lucky

Anybody who has worked in the development world for a significant portion of time will have built up a vast repertoire of abbreviations to describe how they solve problems. Everything from TDD to DDD and, my favourites, FDD and HDD. There are so many in fact that you’ll find a website dedicated to naming and shaming them.

I’m not one to add another standard to the mix… Oh who am I kidding, let me introduce you to Chance Driven Development.

Inki

Inki is a small proof of concept project I’ve been working on which is designed to manage transient, single-use, SSH keys for an automated remediation tool our team is in the process of building.

In this blog post I’ll go over some of the design decisions motivating a tool like Inki, some of its interesting implementation details and the questions we’re hoping it will allow us to answer.