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Technical Guidelines and Practical Advice for DevOps

The workflow of deploying code is almost as old as code itself. There are many use cases associated with the deployment process, including evaluating resource requirements, designing a production system, provisioning and configuring production servers, and pushing code to name a few. In this blog post I focus on a use case for configuring a remote server with the packages and software necessary to execute your code.

In a computing system, a context switch occurs when an operating system stores the state of an application thread before stopping the thread and restoring the state of a different (previously stopped) thread so its execution can resume. The overhead incurred by a context switch managing the process of storing and restoring state negatively impacts operating system and application performance. This blog post describes how DevOps ameliorates the negative impacts that "context switching" between projects can have on a software engineering team's performance.

The DevOps movement is clearly taking the IT world by storm. Technical feats, such as continuous integration (CI), comprehensive automated testing, and continuous delivery (CD) that at one time could only be mastered by hip, trendy startups incapable of failure, are now being successfully performed by traditional enterprises who have a long history of IT operations and are still relying on legacy technologies (the former type of enterprises are known in the DevOps community as "unicorns," the latter as "horses"). In this post, I explore the experience of a fictional horse, Derrick and Anderson (D&A) Lumber, Inc., a company that hit some bumps in the road on its way to DevOps. As D&A finds out, a DevOps transformation is not a product that can be purchased from the outside, but rather a competency that must be grown from within.

When building and delivering software, DevOps practices, such as automated testing, continuous integration, and continuous delivery, allow organizations to move more quickly by speeding the delivery of quality software features, that increase business value. Infrastructure automation tools, such as Chef, Puppet, and Ansible, allow the application of these practices to compute nodes through server provisioning using software scripts. These scripts are first-class software artifacts that benefit from source code version control, automated testing, continuous integration, and continuous delivery.

Regular readers of this blog will recognize a recurring theme in this series: DevOps is fundamentally about reinforcing desired quality attributes through carefully constructed organizational process, communication, and workflow. When teaching software engineering to graduate students in Carnegie Mellon University's Heinz College, I often spend time discussing well known tech companies and their techniques for managing software engineering and sustainment. These discussions serve as valuable real-world examples for software engineering approaches and associated outcomes, and can serve as excellent case studies for DevOps practitioners. This posting will discuss one of my favorite real-world DevOps case studies: Amazon.

In the post What is DevOps?, we define one of the benefits of DevOps as "collaboration between project team roles." Conversations between team members and the platform on which communication occurs can have a profound impact on that collaboration. Poor or unused communication tools lead to miscommunication, redundant efforts, or faulty implementations. On the other hand, communication tools integrated with the development and operational infrastructures can speed up the delivery of business value to the organization. How a team structures the very infrastructure on which they communicate will directly impact their effectiveness as a team. ChatOps is a branch of DevOps focusing on the communications within the DevOps team. The ChatOps space encompasses the communication and collaboration tools within the team: notifications, chat servers, bots, issue tracking systems, etc.

When Agile software development models were first envisioned, a core tenet was to iterate more quickly on software changes and determine the correct path via exploration--essentially, striving to "fail fast" and iterate to correctness as a fundamental project goal. The reason for this process was a belief that developers lacked the necessary information to correctly define long-term project requirements at the onset of a project, due to an inadequate understanding of the customer and an inability to anticipate a customer's evolving needs.

Development with Docker

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In our last post, DevOps and Docker, I introduced Docker as a tool to develop and deploy software applications in a controlled, isolated, flexible, and highly portable infrastructure. In this post, I am going to show you how easy it is to get started with Docker. I will dive in and demonstrate how to use Docker containers in a common software development environment by launching a database container (MongoDB), a web service container (a Python Bottle app), and configuring them to communicate forming a functional multi-container application. If you haven't learned the basics of Docker yet, you should go ahead and try out their official tutorial here before continuing.