This chapter is from the book
This chapter is from the book
This chapter is from the book
Implementing a Deployment Pipeline
Whether you're starting a new project from scratch or trying to create an automated pipeline for an existing system, you should generally take an incremental approach to implementing a deployment pipeline. In this section we'll set out a strategy for going from nothing to a complete pipeline. In general, the steps look like this:
- Model your value stream and create a walking skeleton.
- Automate the build and deployment process.
- Automate unit tests and code analysis.
- Automate acceptance tests.
- Automate releases.
Modeling Your Value Stream and Creating a Walking Skeleton
As described at the beginning of this chapter, the first step is to map out the part of your value stream that goes from check-in to release. If your project is already up and running, you can do this in about half an hour using pencil and paper. Go and speak to everybody involved in this process, and write down the steps. Include best guesses for elapsed time and value-added time. If you're working on a new project, you will have to come up with an appropriate value stream. One way to do this is to look at another project within the same organization that has characteristics similar to yours. Alternatively, you could start with a bare minimum: a commit stage to build your application and run basic metrics and unit tests, a stage to run acceptance tests, and a third stage to deploy your application to a production-like environment so you can demo it.
Once you have a value stream map, you can go ahead and model your process in your continuous integration and release management tool. If your tool doesn't allow you to model your value stream directly, you can simulate it by using dependencies between projects. Each of these projects should do nothing at first—they are just placeholders that you can trigger in turn. Using our "bare minimum" example, the commit stage should be run every time somebody checks in to version control. The stage that runs the acceptance tests should trigger automatically when the commit stage passes, using the same binary created in the commit stage. Any stages that deploy the binaries to a production-like environment for manual testing or release purposes should require you to press a button in order to select the version to deploy, and this capability will usually require authorization.
You can then make these placeholders actually do something. If your project is already well under way, that means plugging in your existing build, test, and deploy scripts. If not, your aim is to create a "walking skeleton" [bEUuac], which means doing the smallest possible amount of work to get all the key elements in place. First of all, get the commit stage working. If you don't have any code or unit tests yet, just create the simplest possible "Hello world" example or, for a web application, a single HTML page, and put a single unit test in place that asserts true. Then you can do the deployment—perhaps setting up a virtual directory on IIS and putting your web page into it. Finally, you can do the acceptance test—you need to do this after you've done the deployment, since you need your application deployed in order to run acceptance tests against it. Your acceptance test can crank up WebDriver or Sahi and verify that the web page contains the text "Hello world."
On a new project, all this should be done before work starts on development—as part of iteration zero, if you're using an iterative development process. Your organization's system administrators or operations personnel should be involved in setting up a production-like environment to run demos from and developing the scripts to deploy your application to it. In the following sections, there's more detail on how to create the walking skeleton and develop it as your project grows.
Automating the Build and Deployment Process
The first step in implementing a pipeline is to automate the build and deployment process. The build process takes source code as its input and produces binaries as output. "Binaries" is a deliberately vague word, since what your build process produces will depend on what technology you're using. The key characteristic of binaries is that you should be able to copy them onto a new machine and, given an appropriately configured environment and the correct configuration for the application in that environment, start your application—without relying on any part of your development toolchain being installed on that machine.
The build process should be performed every time someone checks in by your continuous integration server software. Use one of the many tools listed in the "Implementing Continuous Integration" section on page 56. Your CI server should be configured to watch your version control system, check out or update your source code every time a change is made to it, run the automated build process, and store the binaries on the filesystem where they are accessible to the whole team via the CI server's user interface.
Once you have a continuous build process up and running, the next step is automating deployment. First of all, you need to get a machine to deploy your application on. For a new project, this can be the machine your continuous integration server is on. For a project that is more mature, you may need to find several machines. Depending on your organization's conventions, this environment can be called the staging or user acceptance testing (UAT) environment. Either way, this environment should be somewhat production-like, as described in Chapter 10, "Deploying and Releasing Applications," and its provisioning and maintenance should be a fully automated process, as described in Chapter 11, "Managing Infrastructure and Environments."
Several common approaches to deployment automation are discussed in Chapter 6, "Build and Deployment Scripting." Deployment may involve packaging your application first, perhaps into several separate packages if different parts of the application need to be installed on separate machines. Next, the process of installing and configuring your application should be automated. Finally, you should write some form of automated deployment test that verifies that the application has been successfully deployed. It is important that the deployment process is reliable, as it is also used as a prerequisite for automated acceptance testing.
Once your application's deployment process is automated, the next step is to be able to perform push-button deployments to your UAT environment. Configure your CI server so that you can choose any build of your application and click a button to trigger a process that takes the binaries produced by that build, runs the script that deploys the build, and runs the deployment test. Make sure that when developing your build and deployment system you make use of the principles we describe, such as building your binaries only once and separating configuration from binaries, so that the same binaries may be used in every environment. This will ensure that the configuration management for your project is put on a sound footing.
Except for user-installed software, the release process should be the same process you use to deploy to a testing environment. The only technical differences should be in the configuration of the environment.
Automating the Unit Tests and Code Analysis
The next step in developing your deployment pipeline is implementing a full commit stage. This means running unit tests, code analysis, and ultimately a selection of acceptance and integration tests on every check-in. Running unit tests should not require any complex setup, because unit tests by definition don't rely on your application running. Instead, they can be run by one of the many xUnit-style frameworks against your binaries.
Since unit tests do not touch the filesystem or database (or they'd be component tests), they should also be fast to run. This is why you should start running your unit tests directly after building your application. You can also then run static analysis tools against your application to report useful diagnostic data such as coding style, code coverage, cyclomatic complexity, coupling, and so forth.
As your application gets more complex, you will need to write a large number of unit tests and a set of component tests as well. These should all go into the commit stage. Once the commit stage gets over five minutes, it makes sense to split it into suites that run in parallel. In order to do this, you'll need to get several machines (or one machine with plenty of RAM and a few CPUs) and use a CI server that supports splitting up work and running it in parallel.
Automating Acceptance Tests
The acceptance test phase of your pipeline can reuse the script you use to deploy to your testing environment. The only difference is that after the smoke tests are run, the acceptance test framework needs to be started up, and the reports it generates should be collected at the end of the test run for analysis. It also makes sense to store the logs created by your application. If your application has a GUI, you can also use a tool like Vnc2swf to create a screen recording as the acceptance tests are running to help you debug problems.
Acceptance tests fall into two types: functional and nonfunctional. It is essential to start testing nonfunctional parameters such as capacity and scaling characteristics from early on in any project, so that you have some idea of whether your application will meet its nonfunctional requirements. In terms of setup and deployment, this stage can work exactly the same way as the functional acceptance testing stage. However, the tests of course will differ (see Chapter 9, "Testing Nonfunctional Requirements," for more on creating such tests). When you start off, it is perfectly possible to run acceptance tests and performance tests back-to-back as part of a single stage. You can then separate them in order to be able to distinguish easily which set of tests failed. A good set of automated acceptance tests will help you track down intermittent and hard-to-reproduce problems such as race conditions, deadlocks, and resource contention that will be a good deal harder to discover and debug once your application is released.
The varieties of tests you create as part of the acceptance test and commit test stages of your pipeline will of course be determined by your testing strategy (see Chapter 4, "Implementing a Testing Strategy"). However, you should try and get at least one or two of each type of test you need to run automated early on in your project's life, and incorporate them into your deployment pipeline. Thus you will have a framework that makes it easy to add tests as your project grows.
Evolving Your Pipeline
The steps we describe above are found in pretty much every value stream, and hence pipeline, that we have seen. They are usually the first targets for automation. As your project gets more complex, your value stream will evolve. There are two other common potential extensions to the pipeline: components and branches. Large applications are best built as a set of components which are assembled together. In such projects, it may make sense to have a minipipeline for each component, and then a pipeline that assembles all the components and puts the entire application through acceptance tests, nonfunctional tests, and then deployment to testing, staging, and production environments. This topic is dealt with at length in Chapter 13, "Managing Components and Dependencies." Managing branches is discussed in Chapter 14, "Advanced Version Control."
The implementation of the pipeline will vary enormously between projects, but the tasks themselves are consistent for most projects. Using them as a pattern can speed up the creation of the build and deployment process for any project. However, ultimately, the point of the pipeline is to model your process for building, deploying, testing, and releasing your application. The pipeline then ensures that each change can pass through this process independently in as automated a fashion as possible.
As you implement the pipeline, you will find that the conversations you have with the people involved and the gains in efficiency you realize will, in turn, have an effect on your process. Thus it is important to remember three things.
First of all, the whole pipeline does not need to be implemented at once. It should be implemented incrementally. If there is a part of your process that is currently manual, create a placeholder for it in your workflow. Ensure your implementation records when this manual process is started and when it completes. This allows you to see how much time is spent on each manual process, and thus estimate to what extent it is a bottleneck.
Second, your pipeline is a rich source of data on the efficiency of your process for building, deploying, testing, and releasing applications. The deployment pipeline implementation you create should record every time a process starts and finishes, and what the exact changes were that went through each stage of your process. This data, in turn, allows you to measure the cycle time from committing a change to having it deployed into production, and the time spent on each stage in the process (some of the commercial tools on the market will do this for you). Thus it becomes possible to see exactly what your process' bottlenecks are and attack them in order of priority.
Finally, your deployment pipeline is a living system. As you work continuously to improve your delivery process, you should continue to take care of your deployment pipeline, working to improve and refactor it the same way you work on the applications you are using it to deliver.