The story below details a very interesting & transformational project that I was a part of in 2014 and 2015, at a Dutch company. I’ve told this story before during a number of conference talks (slides and videos are available, if you’re interested), and I’ve now finally come around to writing it up as a series of blog posts!
I deploy on Fridays(and maybe you should too)
Have you ever heard someone say “Don’t deploy on Friday”? I used to say that too! Learn about resilience and operability, deployments, pipelines and continuous testing. Discover trunk based development, pair programming and best practices, and gain the confidence to deploy any day of the week!
In 2016 and 2017, I gave a series of talks titled “The Road to Continuous Deployment: a Case Study”, detailing some of the work I did in 2015 together with the team at De Persgroep Employment Solutions. At DPES, we significantly improved time to market, quality and delivery speed by implementing Continuous Delivery. In the talk I explain how the process took us to multiple production releases per day and how we significantly changed and improved our way of working. This blog post reviews a (short) version of that talk, as given during the first online All Day DevOps conference, in 2016.
Recently, I was asked on Twitter if I could give some pointers for reading material on (production) CI/CD pipelines – what they look like, and how they work. I decided to shed some light on this by describing a typical CI/CD pipeline (from my perspective), in a series of tweets. This article is an expansion of that thread.
The 2017 version of Puppet’s State of DevOps Report was just released.
To me, the most interesting takeaways from the report are:
- High performing teams have 46x more frequent deploys, 96x faster mean time to recover/repair and a 5x lower change failure rate.
- They also automate significantly more work (automation is a key ingredient of any successful DevOps strategy).
- A lower change failure rate and significant automation mean these teams spend 44% more time on new work (and 26% less time on unplanned work and rework).
- Developers in high performing teams generally work in small batches and practice Trunk Based Development. Low performing teams on the other hand use long-lived feature branches and merge infrequently to trunk or master (read on for my thoughts about feature branches).
In this post I’ll discuss my experiences with the strangler pattern and how it can be applied when rewriting a large, legacy code base.