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This chapter is from the book

Deployment Pipeline Practices

Shortly, we'll go into some more detail on the stages in the deployment pipeline. But before we do so, in order to get the benefits of this approach, there are some practices you should follow.

Only Build Your Binaries Once

For convenience, we will refer to the collections of executable code as binaries, although if you don't need to compile your code these "binaries" may be just collections of source files. Jars, .NET assemblies, and .so files are all examples of binaries.

Many build systems use the source code held in the version control system as the canonical source for many steps. The code will be compiled repeatedly in different contexts: during the commit process, again at acceptance test time, again for capacity testing, and often once for each separate deployment target. Every time you compile the code, you run the risk of introducing some difference. The version of the compiler installed in the later stages may be different from the version that you used for your commit tests. You may pick up a different version of some third-party library that you didn't intend. Even the configuration of the compiler may change the behavior of the application. We have seen bugs from every one of these sources reaching production.

This antipattern violates two important principles. The first is to keep the deployment pipeline efficient, so the team gets feedback as soon as possible. Recompiling violates this principle because it takes time, especially in large systems. The second principle is to always build upon foundations known to be sound. The binaries that get deployed into production should be exactly the same as those that went through the acceptance test process—and indeed in many pipeline implementations, this is checked by storing hashes of the binaries at the time they are created and verifying that the binary is identical at every subsequent stage in the process.

If we re-create binaries, we run the risk that some change will be introduced between the creation of the binaries and their release, such as a change in the toolchain between compilations, and that the binary we release will be different from the one we tested. For auditing purposes, it is essential to ensure that no changes have been introduced, either maliciously or by mistake, between creating the binaries and performing the release. Some organizations insist that compilation and assembly, or packaging in the case of interpreted languages, occurs in a special environment that cannot be accessed by anyone except senior personnel. Once we have created our binaries, we will reuse them without re-creating them at the point of use.

So, you should only build your binaries once, during the commit stage of the build. These binaries should be stored on a filesystem somewhere (not in version control, since they are derivatives of your baseline, not part of its definition) where it is easy to retrieve them for later stages in the pipeline. Most CI servers will handle this for you, and will also perform the crucial task of allowing you to trace back to the version control check-in which was used to create them. It isn't worth spending a lot of time and effort ensuring binaries are backed up—it should be possible to exactly re-create them by running your automated build process from the correct revision in version control.

One important corollary of this principle is that it must be possible to deploy these binaries to every environment. This forces you to separate code, which remains the same between environments, and configuration, which differs between environments. This, in turn, will lead you to managing your configuration correctly, applying a gentle pressure towards better-structured build systems.

This brings us neatly to the next practice.

Deploy the Same Way to Every Environment

It is essential to use the same process to deploy to every environment—whether a developer or analyst's workstation, a testing environment, or production—in order to ensure that the build and deployment process is tested effectively. Developers deploy all the time; testers and analysts, less often; and usually, you will deploy to production fairly infrequently. But this frequency of deployment is the inverse of the risk associated with each environment. The environment you deploy to least frequently (production) is the most important. Only after you have tested the deployment process hundreds of times on many environments can you eliminate the deployment script as a source of error.

Every environment is different in some way. If nothing else, it will have a unique IP address, but often there are other differences: operating system and middleware configuration settings, the location of databases and external services, and other configuration information that needs to be set at deployment time. This does not mean you should use a different deployment script for every environment. Instead, keep the settings that are unique for each environment separate. One way to do this is to use properties files to hold configuration information. You can have a separate properties file for each environment. These files should be checked in to version control, and the correct one selected either by looking at the hostname of the local server, or (in a multimachine environment) through the use of an environment variable supplied to the deployment script. Some other ways to supply deploy-time configuration include keeping it in a directory service (like LDAP or ActiveDirectory) or storing it in a database and accessing it through an application like ESCAPE [apvrEr]. There is more on managing software configuration in the "Managing Software Configuration" section on page 39.

If you work in a company where production environments are managed by a team different from the team responsible for development and testing environments, both teams will need to work together to make sure the automated deployment process works effectively across all environments, including development environments. Using the same script to deploy to production that you use to deploy to development environments is a fantastic way to prevent the "it works on my machine" syndrome [c29ETR]. It also means that when you come to release, you will have tested your deployment process hundreds of times by deploying to all of your other environments. This is one of the best ways we know to mitigate the risk of releasing software.

This principle is really another application of the rule that you should separate what changes from what doesn't. If your deployment script is different for different environments, you have no way of knowing that what you're testing will actually work when you go live. Instead, if you use the same process to deploy everywhere, when a deployment doesn't work to a particular environment you can narrow it down to one of three causes:

  • A setting in your application's environment-specific configuration file
  • A problem with your infrastructure or one of the services on which your application depends
  • The configuration of your environment

Establishing which of these is the underlying cause is the subject of the next two practices.

Smoke-Test Your Deployments

When you deploy your application, you should have an automated script that does a smoke test to make sure that it is up and running. This could be as simple as launching the application and checking to make sure that the main screen comes up with the expected content. Your smoke test should also check that any services your application depends on are up and running—such as a database, messaging bus, or external service.

The smoke test, or deployment test, is probably the most important test to write once you have a unit test suite up and running—indeed, it's arguably even more important. It gives you the confidence that your application actually runs. If it doesn't run, your smoke test should be able to give you some basic diagnostics as to whether your application is down because something it depends on is not working.

Deploy into a Copy of Production

The other main problem many teams experience going live is that their production environment is significantly different from their testing and development environments. To get a good level of confidence that going live will actually work, you need to do your testing and continuous integration on environments that are as similar as possible to your production environment.

Ideally, if your production environment is simple or you have a sufficiently large budget, you can have exact copies of production to run your manual and automated tests on. Making sure that your environments are the same requires a certain amount of discipline to apply good configuration management practices. You need to ensure that:

  • Your infrastructure, such as network topology and firewall configuration, is the same.
  • Your operating system configuration, including patches, is the same.
  • Your application stack is the same.
  • Your application's data is in a known, valid state. Migrating data when performing upgrades can be a major source of pain in deployments. We deal more with this topic in Chapter 12, "Managing Data."

You can use such practices as disk imaging and virtualization, and tools like Puppet and InstallShield along with a version control repository, to manage your environments' configuration. We discuss this in detail in Chapter 11, "Managing Infrastructure and Environments."

Each Change Should Propagate through the Pipeline Instantly

Before continuous integration was introduced, many projects ran various parts of their process off a schedule—for example, builds might run hourly, acceptance tests nightly, and capacity tests over the weekend. The deployment pipeline takes a different approach: The first stage should be triggered upon every check-in, and each stage should trigger the next one immediately upon successful completion. Of course this is not always possible when developers (especially on large teams) are checking in very frequently, given that the stages in your process can take a not insignificant amount of time. The problem is shown in Figure 5.6.

Figure 5.6

Figure 5.6 Scheduling stages in a pipeline

In this example, somebody checks a change into version control, creating version 1. This, in turn, triggers the first stage in the pipeline (build and unit tests). This passes, and triggers the second stage: the automated acceptance tests. Somebody then checks in another change, creating version 2. This triggers the build and unit tests again. However, even though these have passed, they cannot trigger a new instance of the automated acceptance tests, since they are already running. In the meantime, two more check-ins have occurred in quick succession. However, the CI system should not attempt to build both of them—if it followed that rule, and developers continued to check in at the same rate, the builds would get further and further behind what the developers are currently doing.

Instead, once an instance of the build and unit tests has finished, the CI system checks to see if new changes are available, and if so, builds off the most recent set available—in this case, version 4. Suppose this breaks the build and unit tests stage. The build system doesn't know which commit, 3 or 4, caused the stage to break, but it is usually simple for the developers to work this out for themselves. Some CI systems will let you run specified versions out of order, in which case the developers could trigger the first stage off revision 3 to see if it passes or fails, and thus whether it was commit 3 or 4 that broke the build. Either way, the development team checks in version 5, which fixes the problem.

When the acceptance tests finally finish, the CI system's scheduler notices that new changes are available, and triggers a new run of the acceptance tests against version 5.

This intelligent scheduling is crucial to implementing a deployment pipeline. Make sure your CI server supports this kind of scheduling workflow—many do—and ensure that changes propagate immediately so that you don't have to run stages off a fixed schedule.

This only applies to stages that are fully automated, such as those containing automated tests. The later stages in the pipeline that perform deployments to manual testing environments need to be activated on demand, which we describe in a later section in this chapter.

If Any Part of the Pipeline Fails, Stop the Line

As we said in the "Implementing Continuous Integration" section on page 56, the most important step in achieving the goals of this book—rapid, repeatable, reliable releases—is for your team to accept that every time they check code into version control, it will successfully build and pass every test. This applies to the entire deployment pipeline. If a deployment to an environment fails, the whole team owns that failure. They should stop and fix it before doing anything else.

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