Skip to main content

Development and test environments - on demand at the press of a button (That actually work!)

“Works on my machine!”

“Fails most epicly on my test system!”

“Oh, wait… it works on CI but fails in Test env 3.”

Sound familiar?

These sorts of conversations are thankfully a thing of the past. 

Wait, hold on - are you still having these sorts of conversations?

That's probably because you are working somewhere where the development, test, production & CI servers are being created by people, painfully, once.


Alexander the Great cutting through the Gordian knot of a particularly gnarly micro-service deployment.

You set up your laptop, you pray to the god of operating system patches and upgrades and hope that nothing ever changes (ever). You're gonna be the last person in the team to take that new Mac OS upgrade - let the rest of the team run through those mine fields first.

And the test systems? Last time you asked for a new one of those your programme manager ended up on new & stronger heart meds.

Luckily, there are tools that can help. 

Gitpod, for example, allows you to create a development environment every time you log on, or in fact whenever you want.

Gitpod has a number of useful features that together can make your lives easier. They are:

  • It provides an easy to use development IDE (it's a web version of VS Code, one of the most popular IDEs in the world).
  • A cloud based workspace with a command line terminal (Yes, Bash), and even a linux desktop if you need one.
  • Configure your whole development/test environment with standard Docker & Docker Compose commands to be built and deployed anytime.
  • Smooth integration with Github, GitLab or BitBucket. (Or you could even self host it)

Now that's a lot to digest. What it means in practice is that your team can create one template of how to set up your app.

You can then store that template in the code repository, along with the app and test code.

And when you need to fix a bug, code review a pull request or test the app you can just build the app and the whole supporting environment in the cloud and on demand (it takes seconds).

Tools like Gitpod have the potential to save teams a massive amount of time lost to misconfiguration, confusion and the laborious work of creating and keeping environments in sync. You don’t realise how much time is lost in the setup and untangling of the Gordian knot that constitutes the average development environment - until it disappears.


Comments

Popular posts from this blog

What possible use could Gen AI be to me? (Part 1)

There’s a great scene in the Simpsons where the Monorail salesman comes to town and everyone (except Lisa of course) is quickly entranced by Monorail fever… He has an answer for every question and guess what? The Monorail will solve all the problems… somehow. The hype around Generative AI can seem a bit like that, and like Monorail-guy the sales-guy’s assure you Gen AI will solve all your problems - but can be pretty vague on the “how” part of the answer. So I’m going to provide a few short guides into how Generative (& other forms of AI) Artificial Intelligence can help you and your team. I’ll pitch the technical level differently for each one, and we’ll start with something fairly not technical: Custom Chatbots. ChatBots these days have evolved from the crude web sales tools of ten years ago, designed to hoover up leads for the sales team. They can now provide informative answers to questions based on documents or websites. If we take the most famous: Chat GPT 4. If we ignore the

Can Gen-AI understand Payments?

When it comes to rolling out updates to large complex banking systems, things can get messy quickly. Of course, the holy grail is to have each subsystem work well independently and to do some form of Pact or contract testing – reducing the complex and painful integration work. But nonetheless – at some point you are going to need to see if the dog and the pony can do their show together – and its generally better to do that in a way that doesn’t make millions of pounds of transactions fail – in a highly public manner, in production.  (This post is based on my recent lightning talk at  PyData London ) For the last few years, I’ve worked in the world of high value, real time and cross border payments, And one of the sticking points in bank [software] integration is message generation. A lot of time is spent dreaming up and creating those messages, then maintaining what you have just built. The world of payments runs on messages, these days they are often XML messages – and they can be pa

Is your ChatBot actually using your data?

 In 316 AD Emperor Constantine issued a new coin,  there's nothing too unique about that in itself. But this coin is significant due to its pagan/roman religious symbols. Why is this odd? Constantine had converted himself, and probably with little consultation -  his empire to Christianity, years before. Yet the coin shows the emperor and the (pagan) sun god Sol.  Looks Legit! While this seems out of place, to us (1700 years later), it's not entirely surprising. Constantine and his people had followed different, older gods for centuries. The people would have been raised and taught the old pagan stories, and when presented with a new narrative it's not surprising they borrowed from and felt comfortable with both. I've seen much the same behaviour with Large Language Models (LLMs) like ChatGPT. You can provide them with fresh new data, from your own documents, but what's to stop it from listening to its old training instead?  You could spend a lot of time collating,