Made Tech Blog

Continuous Delivery: Keeping A Clear Path To Production

Note: Article edited on the 4/12/2018

In order to enjoy the benefits of continuous delivery it is imperative to be able to deploy all the way to production at any time. Anything that prevents a deploy to production is a blocker, and keeping that pathway clear is the hardest part of implementing and maintaining a continuous delivery of software.

If you have been doing CI for a while, are not having success yet and are looking to improve, this article is for you! We are sharing the lessons we learnt from the mistakes we made first implementing this, many moons ago.

Broken builds

The first step of a Continuous Delivery pipeline is the build step. A typical build step will run your tests, check the style of your code, point out overly complex methods and more.

This means your build step has a lot of ways to fail. This is A Good Thing because it catches issues early, but it also means it’s a prime candidate for blocker status.

The solution to broken builds is obvious: fix them and fix them fast. We have a rule at Made Tech that you must own your build failure. As soon as the build breaks you tell your colleagues in Slack that you are looking into it. You drop whatever you are doing and try to fix it immediately. If you cannot fix the problem quickly you back your changes out with a git revert, or something similar.

Preventive measures can be taken to reduce the likelihood of broken builds, such as your developers running the build script locally. We usually have a rake task that our build server runs so this can be done easily.

Parity between environments is also important. Running your tests locally and seeing them pass to then see them fail when they run on the build server is never fun. We have found bugs can arise from using different databases in development from test, for example running SQLite in test but MySQL everywhere else. Where possible use the same database management system everywhere. Same goes for asset storage, if you use Amazon S3 for file storage in production you should use it in development and test too.

On flickering builds

In the past we have suffered from flickering tests, tests which sometimes pass and sometimes fail. Often, the cause will be a test that sets up state and does not clear it afterwards, therefore affecting the result of a subsequent test run. Another cause we’ve found are tests that run in headless browsers like Selenium, PhantomJS and the like. Sometimes expectations on page content will fail because an asynchronous request has not completed yet and therefore hasn’t changed the page content as expected yet.

The nature of flickering tests is corrosive to Continuous Delivery discipline. You will often find engineers just rerun tests several times in the hope they will pass, which obviously affects the clear path to production. Also, if your build could fail at any time, and if your build time is upwards of more than a few minutes (another problem for another time), a flickering test can really slow you down.

The cure to flickering tests is to either resolve their flickering nature as soon as you notice them or delete them. Removing the test is better than having it flicker due to its ability to disparage your engineers.

Work In Progress

We encourage our engineers to push early and often. This means committing small chunks of work and pushing them to your project’s master branch several times a day. We encourage this to avoid merge hell and also to encourage limited work in progress.

The problem with your engineers pushing code is that the tests may pass but the feature is not necessarily complete enough to be released to users. Incomplete work in your master branch that would affect a user is a blocker.

We practice dark launching of code regularly to circumvent this. Dark launching simply means the code is able to be deployed out to production but it is never accessed by users. The simplest way to dark launch a new feature is to release it under a new URL and not link to the URL from anywhere. This might seem crude, but it has worked very well for us.

Feature toggles are a more advanced way of dark launching. Simply wrap up your feature in a toggle and ensure it is disabled in production. We tend to use feature toggles if a feature is a change to an existing feature or is provided by a URL already accessible to users.

Unreviewed work

We have found that sometimes features can sit complete on staging, undeployable to production since the feature requires verification from a Customer, Product Owner or a colleague that simply hasn’t had time. The external dependency on a Customer can really block your pipeline.

If you have limited reviews by Customers before deployment to production you are in luck. In these situations, we encourage engineers to not move onto another task until the one they have just completed is reviewed. In fact, we consider it incomplete until it’s reviewed and deployed to production. This will lead the engineer to get up and get a colleague to review their work as soon as possible.

If the feature must be checked by a Customer then the solution is more nuanced. We try and send features over to clients via instant messaging as soon as they are ready to be reviewed. This is similar to our internal approach above. Failing that, if we need to deploy a hotfix to production we will back the changes out with git revert and deploy our changes up. This is cheeky, we know, but it often encourages you to change your policy of review or open up better lines of communication with your clients.

Dependency on other releases

We work jointly with other teams to deliver software on a regular basis. We try and integrate into a single team where possible, to work from the same codebase. However we have been in situations where the other team is delivering an API that our team will have to interact with.

Often we know the spec of the API before it is built so we can mock it out. Problems arise when a change to an API occurs and we update our code, but the API side has not implemented the change yet. A deployment to production would mean miscommunication with the API. This is a blocker.

Another variation of this is migrations not being run at the same time of deployment. If you push your code and it tries interacting with fields that do not exist yet, you’re going to have a bad time. Another blocker.

The first solution we jump to is hacking the setup. We try and get both our team to deliver the API in the same sprint and codebase as us, so we don’t deploy them separately. This way we can always keep in sync. We would always recommend running migrations on deployment and not having a siloed database administrator (DBA) run them instead. You can often convince the Ops Person or DBA to allow you to run migrations if you promise (and prove you are able) to only commit non-destructive migrations or get them to lock down DB permissions so you cannot drop tables, remove columns, etc.

Alternatively, if we cannot deliver as a single team, we get smart. Getting smart means using if statements. If this API call fails, fall back to using the older API call. If this field doesn’t exist in the database, don’t render it. This can get messy and should be cleaned up as soon as you can but it does unblock you.


The success of your Continuous Delivery practice depends on your ability to react and unblock your pipeline whenever issues arise. Broken builds occur and that’s okay, your team needs to be drilled to respond and fix them as soon as possible. Work in progress and work ready for review is part of the software delivery process, but must be dealt with sensibly.

Dependency on other releases should be avoided where possible but you can get smart too. Ultimately, if you develop a culture of responsibility for keeping the pathway clear, then you will truly start to benefit from Continuous Delivery.

About the Author

Luke Morton

Chief Technology Officer at Made Tech