How Often Do You Ship Changes
Est Reading Time: 5 min
May 14, 2020
Deployment frequency is the easiest of the four key DevOps metrics to track, and the one we started our DevOps journey off with monitoring. The end objective is being able to deploy on-demand so that you can deploy hotfixes as soon as they are ready and get new experiments in front of customers as quickly as you can create new functionality.
Deployment frequency is what it sounds like, the number of releases you've deployed over a given time frame. According to Google's 2019 State of DevOps Report teams have the following deployment rates:
|Tier||Deployment Frequency||% of Teams in Tier|
|Elite||On-demand (multiple deploys per day)||20%|
|High||Between once per day and once per week||23%|
|Medium||Between once per week and once per month||44%|
|Low||Between once per month and once every six months||12%|
Depending on your team and industry, some of these figures may come across as impossible. To achieve elite performance, just having a continuous deployment ecosystem setup doesn't always cut it, and you'll need to get creative on how you can help your team to get changes to customers fast.
Releasing changes to cars, medical equipment, aircraft, and banking systems have standards and compliance requirements from years of failures and theorizing of possible problems. These regulations tend to drive longer cycle times as a large amount of manual testing is required with slow reporting and long turn around times. A lot of these processes serve as a potential legal shield or are used in the absence of metrics to determine if the methods make an impact on making changes more safe, secure, or robust.
When working towards achieving high performance, start measuring what the impact of these processes are and what your baselines are with them in place. That way, as you work around them in the short term, you can build up evidence on whether they are valid and should be kept or are just checking a box.
Since some of these requirements are required by law, you might be stuck with them unless you have some legit legal teams. Just because you cannot deploy new changes to all of your customers without complying with regulations does not mean you can't still release modifications to a production-like environment to gather feedback from control segments.
In the automotive industry, its been a long-standing tradition to have fleets of 10s to hundreds of vehicles built for development and validation teams. Some of the durability vehicles in the fleet are driven hundreds of miles a day on massive enclosed tracks. Some tracks in Michigan, like the American Center for Mobility Test Track at Willow Run, have custom-built tunnels to simulate live disconnecting of cellular signals within a shielded area. These cars also live in labs for electromagnetic compatibility testing and power cycling.
Having these fleets enables automotive manufactures to deploy changes to their test fleets on demand so that developers can gather real-world feedback from stakeholders without endangering customers.
You can apply this concept in most IoT spaces such as medical equipment, manufacturing systems, as well as other transport industries. The downside to the approach is that there is a significant investment cost, a high maintenance cost, and delays in feedback from larger percentages of use cases.
Multiple automotive, healthcare and aerospace companies already have a digital twin of their products and systems. These enable them to run new changes on-demand within virtualized duplicates of their real-world platforms. NASA has been benefiting from the concept of digital twins since at least the 1970s when they used replica systems on earth to debug and validate solutions to the oxygen problem faced by the Apollo 13 crew.
Teams like unlearn.ai are starting to take digital twins to the next level, creating digital twins of individuals to reduce lead time to verification on clinical trials. These twins are still in rudimentary states, but with the help of advances in artificial intelligence, our understanding of DNA, and the sharing of personal medical information, it's possible to start building virtual customers who behave like their real-world twin.
Depending on the complexity of your product and the other units, you need to build digital twins for this approach can require significant initial investments as well. The unlearn.ai team just raised a few million to help build out their human digital twin. Once you have your initial twin, it's much quicker to make modifications to the twins and replicate them to simulate larger percentages of use cases.
Deploying changes without exposing them to customers to see how they would perform in contract to the existing deployment is another solution being used by various IoT product teams to hit elite performance deployment frequencies. This approach can take two forms. One is recording all of the inputs coming into your production devices and replaying those inputs through your latest change to see how it compares.
Alternatively, if you have enough horsepower at the edge, you can deploy your changes and have them run in parallel to your primary system. This way, you can stream the same data being received in real-time through your change to see how it performs.
If it performs better, then you can switch it to the primary role.
You can use these approaches with teams working on products with lower potential for bodily harm as well. Having them assists with increasing deployment frequency while reducing change failure rate, mean time to resolution, and lead time to change.
For many companies writing test scripts, adding static code checks, and conducting peer reviews will be more manageable both from an overhead perspective and a budgetary one. With these in place and a decent way to recover from failures, you and your team should be able to achieve on-demand deployments.
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