Thanks to the latest CI/CD tools and services, software companies can now deliver multiple releases in a week. Over the release process, it's expected that DevOps professionals keep their eyes on the monitoring dashboard and roll-back the deployment on the first suspicion. Since the use of feature flags gets more and more traction, it seems like a good idea to connect those releases and the monitoring tools.
We tend to move quickly from release to release and forget to honor the magnitude of our achievements during the year. Let this post be here as a milestone for the ConfigCat team to stop for a second, look back, and celebrate the preposterous performance we did.
Total config.json downloads by 28 Dec, 2020
Separating your customers into distinct segments will help your product in all sorts of ways. It can help you track the usage of your app in a more meaningful and granular way. It can also reveal how specifically different segments behave differently, which will help you prioritize future feature development as well as focus your marketing efforts.
Last July, the Privacy Shield, which had been so useful for companies doing business on both sides of the Atlantic, became ineffective. It took us all by surprise, since it was quite a new program (only 4 years old) building the framework for exchange of information and data between the United States (US) and the European Union (EU) and it had eased a lot the business between the two markets.
Let's say you've just built a new feature, but it's not ready for a full release just yet. So, you decide to test it with a small group of people.
You can go about it in two ways - deterministic or random. The first way lets you specify people by name, email, company or any other attribute you know about them. The latter uses fancy math and probability to randomly assign users into groups. Let's see how you'd accomplish both using ConfigCat.
When I implement a new feature to my application, I would like to manage it after the release (enable, target, tune and obviously in urgent cases roll it back). The feature/release toggles design pattern can solve my issue, but how can I answer the business needs? For example:
When we designed ConfigCat, our main purpose was to create an architecture that is scalable and resilient to short interruptions, so you don't have to worry about latency, service outages, and unwanted glitches in the system.
Do you know what's this? Can you recognize multiple layers of load balancing working here?
Do you know why you see distinct group of lines fluctuating around different trends? It's there to achieve four contradictory goals at the same time. Let me explain that.
ConfigCat's validation phase was a success in our eyes, so we had to step ahead. Above making a great product with great features we had to provide a really stable and reliable system.
When it comes to feature flags, teams with 5-20 engineers face similar challenges to teams with hundreds of developers. What you never want is one engineer flipping a feature toggle to unintentionally affect the work of another engineer.
This is a conceptual problem with feature flags. It doesn't matter if you are using an open-source feature flag tool, or a feature flag service. Let's see best practices ConfigCat recommends.