This is an entry in my loose series of posts about build systems.

I’ve been thinking recently about why I’ve come to think that build systems are so important, and this post is mostly just me thinking aloud about this issue and related questions.

Making Builds Efficient

Writing a build systems for a project is often relatively trivial, once you capture the process, and figure out the base dependencies, you can write scripts and make files to automate this process. The problem is that the most rudimentary build systems aren’t terribly efficient, for two main reasons:

1. It’s difficult to stumble into a build process that is easy to parallelize, so these rudimentary solutions often depend on a series of step happening in a specific order.

2. It’s easier to write a build system that rebuilds too much rather than too little for subsequent builds. From the perspective of build tool designers, this is the correct behavior; but it means that it takes more work to ensure that you only rebuild what you need to.

As a corollary, you need to test build systems and approaches with significantly large systems, where “rebuilding too much,” can be detectable.

Making a build system efficient isn’t too hard, but it does require some amount of testing and experimentation, and often it centers on having explicit dependencies, so that the build tool (i.e. Make, SCons, Ninja, etc.) can build output files in the correct order and only build when a dependency changes.¹

The Benefits of a Fast Build

1. Fast builds increase overall personal productivity.

   You don’t have to wait for a build to complete, and you’re not tempted to context switch during the build, so you stay focused on your work.

2. Fast builds increase quality.

   If your build system (and to a similar extent, your test system,) run efficiently, it’s possible to detect errors earlier in the development process, which will prevent errors and defects. A tighter feedback loop on the code you write is helpful.

3. Fast builds democratize the development process.

   If builds are easy to run, and require minimal cajoling and intervention, it becomes much more likely that many people

   This is obviously most prevalent in open source communities and projects, this is probably true of all development teams.

4. Fast builds promote freshness.

   If the build process is frustrating, then anyone who might run the build will avoid it and run the build less frequently, and on the whole the development effort looses important feedback and data.

   Continuous integration systems help with this, but they require significant resources, are clumsy solutions, and above all, CI attempts to solve a slightly different problem.

Optimizing Builds

Steps you can take to optimizing builds:

(Note: I’m by no means an expert in this, so feel free to add or edit these suggestions.)

• A large number of smaller jobs that can complete independently of other tools, are easy to run in parallel. If the jobs that create a product take longer and are more difficult to split into components, then the build will be slower, particularly on more powerful hardware.
• Incremental builds are a huge win, particularly for larger processes. Most of the reasons why you want “fast builds,” only require fast rebuilds and partial builds, not necessarily the full “clean builds.” While fast initial builds are not unimportant, they account for a small percentage of use.
• Manage complexity.

There are a lot of things you can do to make builds smarter, which should theoretically make builds faster.

Examples of this kind of complexity include storing dependency information in a database, or using hashing rather than “mtime” to detect staleness, or integrating the build automation with other parts of the development tool chain, or using a more limited method to specify build processes.

The problem, or the potential problem is that you lose simplicity, and it’s possible that something in this “smarter and more complex” system can break or slow down under certain pressures, or can have enough overhead to render them unproductive optimizations.