Feature toggle: A software development technique allowing multiple feature branches to be maintained so features can be tested before completion.

When developers finish a new feature, they merge the feature branch back to the main and deploy.
Feature flags enable continuous delivery by allowing teams to practice trunk-based development without the threat of long-lived branches and the dreaded merge hell.
In addition they increase development velocity by making test in production simple so that you can continuously validate your changes on a subset of users while mitigating risks.
They are not only used by engineering teams but also by QA and also product and operations teams.
Feature flags may be used to isolate new changes while known, stable code remains in place.
This can help developers avoid long-running feature branches by committing frequently to the primary branch of a repository behind the feature toggle.
When the new code is ready there is no need for a disruptive collaborative merge and deploy scenario; the team can toggle the feature flag to enable the new system.

This ensures the branches never diverge too far from the primary codebase.
Let’s see how feature flagging & feature management streamline release processes.
Trunk-based development is a branching model where developers collaborate within a branch and make smaller changes more frequently.
The theory behind this practice is to limit long-lived branches that could lead to ‘merge hell’.

Exactly What Is A Feature Branch?

We analyzed the artifacts utilizing an open coding technique, a technique to analyze textual data by labeling concepts and identifying categories predicated on similarity and dissimilarity of codes saldana2015coding .
Then, we labeled the suggested recommendations, experiences, and implementation information regarding using feature toggles mentioned by practitioners in the textual artifacts and in the notes of non-textual artifacts.
In this guide we’ve found that feature flagging requires a simple concept – choosing between different code paths at runtime – and uses it to provide several benefits.
In old workflows, the continuous integration server commonly ran tests on the main branch only.
Developers had to make sure their code did not break the primary branch.
When using GitLab flow, developers create their branches out of this main branch, so it’s essential that it never breaks.

But if there’s an issue during this process, they can switch off the flag to reduce user exposure and repair the problem as soon as possible.
The primary key for a user in Flagsmith may be the first attribute of the identify method.

Company Overview

A lot of the feature flagging use cases we’ve viewed require some form of context to make a flagging decision.
Feature flags let you scientifically validate your product ideas.
A good feature flagging system allows a product manager to slice and dice their userbase, establishing treatment and control groups for a feature based on various demographics.
By then correlating user behavior with feature exposure, we are able to gain insight into the true impact of an attribute.
Because of this wide selection of use cases, it doesn’t seem sensible to treat all feature flags the same.

Without these three key considerations, the use of feature toggles could be counter-productive.
Using static files to manage toggle configuration can become cumbersome as soon as you reach a particular scale.

Implementing the decision points for a feature flag can result in messy code.
Conditional statements scattered throughout your codebase make it harder to comprehend the flow of the program, and multiple runtime code paths makes it harder to reconstruct the cause of an issue when debugging.
You can find two main methods to correlating flag state with metrics.
We can add metadata about flag state to the metrics that we’re recording in a separate analytics system, allowing us to observe how flag state affects those metrics.
Alternatively, we can push key metrics into the feature flagging system itself, performing correlation and causal analysis there.

• If your application gets too much traffic and the metrics show an increase in issues, you can switch off poorly performing or non-crucial features.
• When you have not pushed your commits to a shared location yet, you may also incorporate changes by rebasing on main or another feature branch.
• Learn why naming conventions are one of the essential best practices in terms of implementing feature flags.
• If you’ve reached your limit, it is advisable to remove an existing

This can allow us to write components without worrying too much about feature flags.
It will also allow us to change to a different feature flag provider without much hassle.
This capability to dynamically re-configure specific service instances is an extremely sharp tool.
If used inappropriately it can cause a lot of pain and confusion in a shared environment.
This facility should only ever be used by automated tests, and possibly within manual exploratory testing and debugging.
Now that we’ve a toggle categorization scheme we are able to discuss how those two dimensions of dynamism and longevity affect how we work with feature flags of different categories.
The team decides to go with a per-request Toggle Router since it gives them plenty of flexibility.

The first thing needed is really a simple, updated configuration YAML file for the Spring Cloud config server to consume.
Now, we get to your client side in the React UI, and it’s dead simple.

Pete Hodgson is really a consultant to Rollout.io, a software engineer and an architect.
Pete is a frequent podcast panelist, regular conference speaker in america and Europe, and a contributing author.
Flickr, Etsy, Github and Facebook are some better-known proponents of the technique.
Facebook’s use of feature flags is among the key enablers because of its famous ability to deploy facebook.com from the shared branch twice daily.
To summarize, feature branches allow teams to decouple streams of work such that they could be released independently.
However, they introduce a risk of large merge conflicts when parallel feature branches contain large sets of changes.
Remerging from master into feature branches only helps once parallel work has recently landed, and cross-merging between branches couples features together in the same way as focusing on a shared master.