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This page covers artifact-based build systems and the philosophy behind their creation. Bazel is an artifact-based build system. While task-based build systems are good step above build scripts, they give too much power to individual engineers by letting them define their own tasks.
Artifact-based build systems have a small number of tasks defined by the system that engineers can configure in a limited way. Engineers still tell the system what to build, but the build system determines how to build it. As with task-based build systems, artifact-based build systems, such as Bazel, still have buildfiles, but the contents of those buildfiles are very different. Rather than being an imperative set of commands in a Turing-complete scripting language describing how to produce an output, buildfiles in Bazel are a declarative manifest describing a set of artifacts to build, their dependencies, and a limited set of options that affect how they’re built. When engineers run bazel on the command line, they specify a set of targets to build (the what), and Bazel is responsible for configuring, running, and scheduling the compilation steps (the how). Because the build system now has full control over what tools to run when, it can make much stronger guarantees that allow it to be far more efficient while still guaranteeing correctness.
It’s easy to make an analogy between artifact-based build systems and functional programming. Traditional imperative programming languages (such as, Java, C, and Python) specify lists of statements to be executed one after another, in the same way that task-based build systems let programmers define a series of steps to execute. Functional programming languages (such as, Haskell and ML), in contrast, are structured more like a series of mathematical equations. In functional languages, the programmer describes a computation to perform, but leaves the details of when and exactly how that computation is executed to the compiler.
This maps to the idea of declaring a manifest in an artifact-based build system and letting the system figure out how to execute the build. Many problems can't be easily expressed using functional programming, but the ones that do benefit greatly from it: the language is often able to trivially parallelize such programs and make strong guarantees about their correctness that would be impossible in an imperative language. The easiest problems to express using functional programming are the ones that simply involve transforming one piece of data into another using a series of rules or functions. And that’s exactly what a build system is: the whole system is effectively a mathematical function that takes source files (and tools like the compiler) as inputs and produces binaries as outputs. So, it’s not surprising that it works well to base a build system around the tenets of functional programming.
Google's build system, Blaze, was the first artifact-based build system. Bazel is the open-sourced version of Blaze.
Here’s what a buildfile (normally named BUILD) looks like in Bazel:
java_binary( name = "MyBinary", srcs = ["MyBinary.java"], deps = [ ":mylib", ], ) java_library( name = "mylib", srcs = ["MyLibrary.java", "MyHelper.java"], visibility = ["//java/com/example/myproduct:__subpackages__"], deps = [ "//java/com/example/common", "//java/com/example/myproduct/otherlib", ], )
In Bazel, BUILD files define targets—the two types of targets here are java_binary and java_library. Every target corresponds to an artifact that can be created by the system: binary targets produce binaries that can be executed directly, and library targets produce libraries that can be used by binaries or other libraries. Every target has:
name: how the target is referenced on the command line and by other targetssrcs: the source files to compiled to create the artifact for the targetdeps: other targets that must be built before this target and linked into itDependencies can either be within the same package (such as MyBinary’s dependency on :mylib) or on a different package in the same source hierarchy (such as mylib’s dependency on //java/com/example/common).
As with task-based build systems, you perform builds using Bazel’s command-line tool. To build the MyBinary target, you run bazel build :MyBinary. After entering that command for the first time in a clean repository, Bazel:
BUILD file in the workspace to create a graph of dependencies among artifacts.MyBinary; that is, every target that MyBinary depends on and every target that those targets depend on, recursively.MyBinary’s transitive dependencies have been built.MyBinary to produce a final executable binary that links in all of the dependencies that were built in step 3.Fundamentally, it might not seem like what’s happening here is that much different than what happened when using a task-based build system. Indeed, the end result is the same binary, and the process for producing it involved analyzing a bunch of steps to find dependencies among them, and then running those steps in order. But there are critical differences. The first one appears in step 3: because Bazel knows that each target only produces a Java library, it knows that all it has to do is run the Java compiler rather than an arbitrary user-defined script, so it knows that it’s safe to run these steps in parallel. This can produce an order of magnitude performance improvement over building targets one at a time on a multicore machine, and is only possible because the artifact-based approach leaves the build system in charge of its own execution strategy so that it can make stronger guarantees about parallelism.
The benefits extend beyond parallelism, though. The next thing that this approach gives us becomes apparent when the developer types bazel build :MyBinary a second time without making any changes: Bazel exits in less than a second with a message saying that the target is up to date. This is possible due to the functional programming paradigm we talked about earlier—Bazel knows that each target is the result only of running a Java compiler, and it knows that the output from the Java compiler depends only on its inputs, so as long as the inputs haven’t changed, the output can be reused. And this analysis works at every level; if MyBinary.java changes, Bazel knows to rebuild MyBinary but reuse mylib. If a source file for //java/com/example/common changes, Bazel knows to rebuild that library, mylib, and MyBinary, but reuse //java/com/example/myproduct/otherlib. Because Bazel knows about the properties of the tools it runs at every step, it’s able to rebuild only the minimum set of artifacts each time while guaranteeing that it won’t produce stale builds.
Reframing the build process in terms of artifacts rather than tasks is subtle but powerful. By reducing the flexibility exposed to the programmer, the build system can know more about what is being done at every step of the build. It can use this knowledge to make the build far more efficient by parallelizing build processes and reusing their outputs. But this is really just the first step, and these building blocks of parallelism and reuse form the basis for a distributed and highly scalable build system.
Artifact-based build systems fundamentally solve the problems with parallelism and reuse that are inherent in task-based build systems. But there are still a few problems that came up earlier that we haven’t addressed. Bazel has clever ways of solving each of these, and we should discuss them before moving on.
One problem we ran into earlier was that builds depended on the tools installed on our machine, and reproducing builds across systems could be difficult due to different tool versions or locations. The problem becomes even more difficult when your project uses languages that require different tools based on which platform they’re being built on or compiled for (such as, Windows versus Linux), and each of those platforms requires a slightly different set of tools to do the same job.
Bazel solves the first part of this problem by treating tools as dependencies to each target. Every java_library in the workspace implicitly depends on a Java compiler, which defaults to a well-known compiler. Whenever Bazel builds a java_library, it checks to make sure that the specified compiler is available at a known location. Just like any other dependency, if the Java compiler changes, every artifact that depends on it is rebuilt.
Bazel solves the second part of the problem, platform independence, by setting build configurations. Rather than targets depending directly on their tools, they depend on types of configurations:
Bazel comes with targets for several popular programming languages out of the box, but engineers will always want to do more—part of the benefit of task-based systems is their flexibility in supporting any kind of build process, and it would be better not to give that up in an artifact-based build system. Fortunately, Bazel allows its supported target types to be extended by adding custom rules.
To define a rule in Bazel, the rule author declares the inputs that the rule requires (in the form of attributes passed in the BUILD file) and the fixed set of outputs that the rule produces. The author also defines the actions that will be generated by that rule. Each action declares its inputs and outputs, runs a particular executable or writes a particular string to a file, and can be connected to other actions via its inputs and outputs. This means that actions are the lowest-level composable unit in the build system—an action can do whatever it wants so long as it uses only its declared inputs and outputs, and Bazel takes care of scheduling actions and caching their results as appropriate.
The system isn’t foolproof given that there’s no way to stop an action developer from doing something like introducing a nondeterministic process as part of their action. But this doesn’t happen very often in practice, and pushing the possibilities for abuse all the way down to the action level greatly decreases opportunities for errors. Rules supporting many common languages and tools are widely available online, and most projects will never need to define their own rules. Even for those that do, rule definitions only need to be defined in one central place in the repository, meaning most engineers will be able to use those rules without ever having to worry about their implementation.
Actions sound like they might run into the same problems as tasks in other systems—isn’t it still possible to write actions that both write to the same file and end up conflicting with one another? Actually, Bazel makes these conflicts impossible by using sandboxing. On supported systems, every action is isolated from every other action via a filesystem sandbox. Effectively, each action can see only a restricted view of the filesystem that includes the inputs it has declared and any outputs it has produced. This is enforced by systems such as LXC on Linux, the same technology behind Docker. This means that it’s impossible for actions to conflict with one another because they are unable to read any files they don’t declare, and any files that they write but don’t declare will be thrown away when the action finishes. Bazel also uses sandboxes to restrict actions from communicating via the network.
There’s still one problem remaining: build systems often need to download dependencies (whether tools or libraries) from external sources rather than directly building them. This can be seen in the example via the @com_google_common_guava_guava//jar dependency, which downloads a JAR file from Maven.
Depending on files outside of the current workspace is risky. Those files could change at any time, potentially requiring the build system to constantly check whether they’re fresh. If a remote file changes without a corresponding change in the workspace source code, it can also lead to unreproducible builds—a build might work one day and fail the next for no obvious reason due to an unnoticed dependency change. Finally, an external dependency can introduce a huge security risk when it is owned by a third party: if an attacker is able to infiltrate that third-party server, they can replace the dependency file with something of their own design, potentially giving them full control over your build environment and its output.
The fundamental problem is that we want the build system to be aware of these files without having to check them into source control. Updating a dependency should be a conscious choice, but that choice should be made once in a central place rather than managed by individual engineers or automatically by the system. This is because even with a “Live at Head” model, we still want builds to be deterministic, which implies that if you check out a commit from last week, you should see your dependencies as they were then rather than as they are now.
Bazel and some other build systems address this problem by requiring a workspacewide manifest file that lists a cryptographic hash for every external dependency in the workspace. The hash is a concise way to uniquely represent the file without checking the entire file into source control. Whenever a new external dependency is referenced from a workspace, that dependency’s hash is added to the manifest, either manually or automatically. When Bazel runs a build, it checks the actual hash of its cached dependency against the expected hash defined in the manifest and redownloads the file only if the hash differs.
If the artifact we download has a different hash than the one declared in the manifest, the build will fail unless the hash in the manifest is updated. This can be done automatically, but that change must be approved and checked into source control before the build will accept the new dependency. This means that there’s always a record of when a dependency was updated, and an external dependency can’t change without a corresponding change in the workspace source. It also means that, when checking out an older version of the source code, the build is guaranteed to use the same dependencies that it was using at the point when that version was checked in (or else it will fail if those dependencies are no longer available).
Of course, it can still be a problem if a remote server becomes unavailable or starts serving corrupt data—this can cause all of your builds to begin failing if you don’t have another copy of that dependency available. To avoid this problem, we recommend that, for any nontrivial project, you mirror all of its dependencies onto servers or services that you trust and control. Otherwise you will always be at the mercy of a third party for your build system’s availability, even if the checked-in hashes guarantee its security.