Whether you try to measure developer productivity by LOC or Function Points or Feature Points or S tory Points or velocity or some other measure of how much code is written, less coding gets done because developers are spending more time on ops work and dealing with interruptions, and less time writing code. Time learning about the infrastructure and the platform and understanding how it is setup and making sure that it is setup right.
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Building Continuous Delivery and Continuous Deployment pipelines and keeping them running. Developers need to be responsive to operations and customers, react to feedback from production, jump on problems and help detect and resolve failures as quickly as possible. This means everyone, especially your most talented developers, need to be available for ops most if not all of the time.
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Which means developers will miss their commitments more often. Then why make commitments at all? Why bother planning or estimating? Use just-in-time prioritization instead to focus in on the most important thing that ops or the customer need at the moment, and deliver it as soon as you can — unless something more important comes up and pre-empts it. As developers take on more ops and support responsibilities, multi-tasking and task switching — and the interruptions and inefficiency that come with it — increase, fracturing time and destroying concentration.
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After a developer checks in code, running unit tests in Continuous Integration is supposed to be fast, a few seconds or minutes, so that they can keep moving forward with their work. But to deploy immediately to production means running through a more extensive set of integration tests and systems tests and other checks in Continuous Delivery more tests and more checks takes more time , then executing the steps through to deployment, and then monitoring production to make sure that everything worked correctly, and jumping in if anything goes wrong.
Optimizing the flow of work in and out of operations means sacrificing developer flow, and slowing down development work itself. In Devops, the way that developers and ops work change, and the way that they need to be managed changes. Devops success is measured by operational IT metrics , not on meeting project delivery goals of scope, schedule and cost, not on meeting release goals or sprint commitments, or even meeting product design goals.
As more software is delivered earlier and more often to production, development turns into maintenance. Project management is replaced by incident management and task management. Planning horizons get much shorter — or planning is replaced by just-in-time queue prioritization and triage. With Infrastructure as Code Ops become developers, designing and coding infrastructure and infrastructure changes, thinking about reuse and readability and duplication and refactoring , technical debt and testability and building on TDD to implement TDI Test Driven Infrastructure.
They become more agile and more Agile, making smaller changes more often, more time programming and less on paper work. And developers start to work more like ops.
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Taking on responsibilities for operations and support, putting operational risks first, caring about the infrastructure, building operations tools, finding ways to balance immediate short-term demands for operational support with longer-term design goals. None of this will be a surprise to anyone who has been working in an online business for a while. Once you deliver a system and customers start using it, priorities change, everything about the way that you work and plan has to change too.
This way of working isn't better for developers, or worse necessarily. But it is fundamentally different from how many developers think and work today. More frenetic and interrupt-driven. At the same time, more disciplined and more Lean. More transparent. More responsibility and accountability.
Less about development and more about release and deployment and operations and support. Developers — and their managers — will need to get used to being part of the bigger picture of running IT, which is about much more than designing apps and writing and delivering code. Before DevOps, I used to spew out messages to log files for nearly every line of code I wrote. I found that helpful when trying to debug my own code.
But let's take a step back and look at the bigger picture of a CI server that builds code committed by many developers. If every developer does what I did, the log files will eventually become completely unmanageable. The developer who wrote code that broke a nightly build may not necessarily be the one who has to debug it. If the log is filled with a load of useless messages, it's going to be much harder to analyze and deduce where the problem lies.
The answer is to log useful messages in strategic places in the code. Relevant messages mean less noise, which means quicker fixes. Another useful method to use with log files is to connect the stack trace to files committed on your source control. A stack trace indicates the exact point where the code failed. Linking each class in the stack trace to its corresponding file in source control exposes which developer last modified the code and is therefore familiar with the change that broke the build. In our early DevOps days, life was relatively simple. Our CI server ran a series of tests on a single server, then shut down.
If anything went wrong, it was easy to ferret out where the problem laid. Today, we have progressed and things are much more complex with many processes running concurrently, which can create concurrency issues. For example, one issue I encountered was data cropping up in unexpected parts of the code. After analysis, I found that the cause was stateful Spring beans exposing their data while being accessed by other concurrent threads.
Yes, concurrency issues are very hard to debug. It's not that I didn't know this before, it's just that I didn't really consider it when writing code. Today, after dealing with enough issues during my year in DevOps, I can pick out the parts of my code that may be vulnerable to concurrency issues and make sure I log messages accordingly. I'm generally more "concurrency aware" and also more adept at debugging concurrency issues in particular. A year in DevOps gave me much more intimate knowledge of CI, and namely, a better understanding of what happens to all those build modules that different developers commit to GitHub.
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The way one module uses dependencies can create issues for another module, such as circular dependencies, classpath problems, or all-around dynamic-link library DLL hell. I use this knowledge to construct projects wisely to minimize the number of dependencies I expose, and I only publish those parts of code that I have to. My year in DevOps had a profound effect on how I develop code and on my life as a developer in general.
Exposure to the many tools and types of infrastructures that DevOps uses gave me a more forward-thinking attitude that looks far past my own machine. Now I see how my code needs to work with surrounding developers' code. But more than that, it gave me a new set of skills I could apply in my daily work as a developer.
Not everyone can develop a Jenkins plugin to solve a problem. Not everyone understands your customer's viewpoint as they integrate newly purchased infrastructure. These are invaluable lessons I learned while in DevOps. For any developer out there, I strongly recommend making this foray into the unknown, even if just for a short time.
An even stronger recommendation is for team leaders to send at least one team member on a journey through DevOps. Image source: Flickr. Skip to main content. Our Contributors About Subscribe.
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Think "automation" I used to happily commit code once it had passed a prescribed set of unit tests on my machine. Write tests for continuous integration CI Tests that pass on a developer's machine often fail when running on the CI server. Log messages wisely and connect the stack trace to Git Before DevOps, I used to spew out messages to log files for nearly every line of code I wrote.
Get "concurrency aware" In our early DevOps days, life was relatively simple.