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This chapter is from the book

Preparing to Release

There is a business risk associated with every release of a production system. At best, if there is a serious problem at the point of release, it may delay the introduction of valuable new capabilities. At worst, if there is no sensible back-out plan in place, it may leave the business without mission-critical resources because they had to be decommissioned as part of the release of the new system.

The mitigation of these problems is very simple when we view the release step as a natural outcome of our deployment pipeline. Fundamentally, we want to

  • Have a release plan that is created and maintained by everybody involved in delivering the software, including developers and testers, as well as operations, infrastructure, and support personnel
  • Minimize the effect of people making mistakes by automating as much of the process as possible, starting with the most error-prone stages
  • Rehearse the procedure often in production-like environments, so you can debug the process and the technology supporting it
  • Have the ability to back out a release if things don't go according to plan
  • Have a strategy for migrating configuration and production data as part of the upgrade and rollback processes

Our goal is a completely automated release process. Releasing should be as simple as choosing a version of the application to release and pressing a button. Backing out should be just as simple. There is a great deal more information on these topics in Chapter 10, "Deploying and Releasing Applications."

Automating Deployment and Release

The less control we have over the environment in which our code executes, the more potential there is for unexpected behaviors. Thus, whenever we release a software system, we want to be in control of every single bit that is deployed. There are two factors that may work against this ideal. The first is that for many applications, you simply don't have full control of the operational environment of the software that you create. This is especially true of products and applications that are installed by users, such as games or office applications. This problem is generally mitigated by selecting a representative sample of target environments and running your automated acceptance test suite on each of these sample environments in parallel. You can then mine the data produced to work out which tests fail on which platforms.

The second constraint is that the cost of establishing that degree of control is usually assumed to outweigh the benefits. However, usually the converse is true: Most problems with production environments are caused by insufficient control. As we describe in Chapter 11, production environments should be completely locked down—changes to them should only be made through automated processes. That includes not only deployment of your application, but also changes to their configuration, software stack, network topology, and state. Only in this way is it possible to reliably audit them, diagnose problems, and repair them in a predictable time. As the complexity of the system increases, so does the number of different types of servers, and the higher the level of performance required, the more vital this level of control becomes.

The process for managing your production environment should be used for your other testing environments such as staging, integration, and so forth. In this way you can use your automated change management system to create a perfectly tuned configuration in your manual testing environments. These can be tuned to perfection, perhaps using feedback from capacity testing to evaluate the configuration changes that you make. When you are happy with the result, you can replicate it to every server that needs that configuration, including production, in a predictable, reliable way. All aspects of the environment should be managed in this way, including middleware (databases, web servers, message brokers, and application servers). Each can be tuned and tweaked, with the optimal settings added to your configuration baseline.

The costs of automating the provision and maintenance of environments can be lowered significantly by using automated provisioning and management of environments, good configuration management practices, and (if appropriate) virtualization.

Once the environment's configuration is managed correctly, the application can be deployed. The details of this vary widely depending on the technologies employed in the system, but the steps are always very similar. We exploit this similarity in our approach to the creation of build and deployment scripts, discussed in Chapter 6, "Build and Deployment Scripting," and in the way in which we monitor our process.

With automated deployment and release, the process of delivery becomes democratized. Developers, testers, and operations teams no longer need to rely on ticketing systems and email threads to get builds deployed so they can gather feedback on the production readiness of the system. Testers can decide which version of the system they want in their test environment without needing to be technical experts themselves, nor relying on the availability of such expertise to make the deployment. Since deployment is simple, they can change the build under test more often, perhaps returning to an earlier version of the system to compare its behavior with that of the latest version when they find a particularly interesting bug. Sales people can access the latest version of the application with the killer feature that will swing the deal with a client. There are more subtle changes too. In our experience, people begin to relax a little. They perceive the project as a whole as less risky—mainly because it is less risky.

An important reason for the reduction in risk is the degree to which the process of release itself is rehearsed, tested, and perfected. Since you use the same process to deploy your system to each of your environments and to release it, the deployment process is tested very frequently—perhaps many times a day. After you have deployed a complex system for the fiftieth or hundredth time without a hitch, you don't think about it as a big event any more. Our goal is to get to that stage as quickly as possible. If we want to be wholly confident in the release process and the technology, we must use it and prove it to be good on a regular basis, just like any other aspect of our system. It should be possible to deploy a single change to production through the deployment pipeline with the minimum possible time and ceremony. The release process should be continuously evaluated and improved, identifying any problems as close to the point at which they were introduced as possible.

Many businesses require the ability to release new versions of their software several times a day. Even product companies often need to make new versions of their software available to users quickly, in case critical defects or security holes are found. The deployment pipeline and the associated practices in this book are what makes it possible to do this safely and reliably. Although many agile development processes thrive on frequent release into production—a process we recommend very strongly when it is applicable—it doesn't always make sense to do so. Sometimes we have to do a lot of work before we are in a position to release a set of features that makes sense to our users as a whole, particularly in the realm of product development. However, even if you don't need to release your software several times a day, the process of implementing a deployment pipeline will still make an enormous positive impact on your organization's ability to deliver software rapidly and reliably.

Backing Out Changes

There are two reasons why release days are traditionally feared. The first one is the fear of introducing a problem because somebody might make a hard-to-detect mistake while going through the manual steps of a software release, or because there is a mistake in the instructions. The second fear is that, should the release fail, either because of a problem in the release process or a defect in the new version of the software, you are committed. In either case, the only hope is that you will be clever enough to solve the problem very quickly.

The first problem we mitigate by essentially rehearsing the release many times a day, proving that our automated deployment system works. The second fear is mitigated by providing a back-out strategy. In the worst case, you can then get back to where you were before you began the release, which allows you to take time to evaluate the problem and find a sensible solution.

In general, the best back-out strategy is to keep the previous version of your application available while the new version is being released—and for some time afterwards. This is the basis for some of the deployment patterns we discuss in Chapter 10, "Deploying and Releasing Applications." In a very simple application, this can be achieved (ignoring data and configuration migrations) by having each release in a directory and using a symlink to point to the current version. Usually, the most complex problem associated with both deploying and rolling back is migrating the production data. This is discussed at length in Chapter 12, "Managing Data."

The next best option is to redeploy the previous good version of your appliation from scratch. To this end, you should have the ability to click a button to release any version of your application that has passed all stages of testing, just as you can with other environments under the control of the deployment pipeline. This idealistic position is fully achievable for some systems, even for systems with significant amounts of data associated with them. However, for some systems, even for individual changes, the cost of providing a full, version-neutral back-out may be excessive in time, if not money. Nevertheless, the ideal is useful, because it sets a target which every project should strive to achieve. Even if it falls somewhat short in some respects, the closer you approach this ideal position the easier your deployment becomes.

On no account should you have a different process for backing out than you do for deploying, or perform incremental deployments or rollbacks. These processes will be rarely tested and therefore unreliable. They will also not start from a known-good baseline, and therefore will be brittle. Always roll back either by keeping an old version of the application deployed or by completely redeploying a previous known-good version.

Building on Success

By the time a release candidate is available for deployment into production, we will know with certainty that the following assertions about it are true:

  • The code can compile.
  • The code does what our developers think it should because it passed its unit tests.
  • The system does what our analysts or users think it should because it passed all of the acceptance tests.
  • Configuration of infrastructure and baseline environments is managed appropriately, because the application has been tested in an analog of production.
  • The code has all of the right components in place because it was deployable.
  • The deployment system works because, at a minimum, it will have been used on this release candidate at least once in a development environment, once in the acceptance test stage, and once in a testing environment before the candidate could have been promoted to this stage.
  • The version control system holds everything we need to deploy, without the need for manual intervention, because we have already deployed the system several times.

This "building upon success" approach, allied with our mantra of failing the process or any part of it as quickly as possible, works at every level.

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