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Bazel is a tool that automates software builds and tests. Supported build tasks include running compilers and linkers to produce executable programs and libraries, and assembling deployable packages for Android, iOS and other target environments. Bazel is similar to other tools like Make, Ant, Gradle, Buck, Pants and Maven.
Bazel was designed to fit the way software is developed at Google. It has the following features:
Multi-language support: Bazel supports Java, Objective-C and C++ out of the box, and can be extended to support arbitrary programming languages.
High-level build language: Projects are described in the BUILD language, a concise text format that describes a project as sets of small interconnected libraries, binaries and tests. In contrast, with tools like Make, you have to describe individual files and compiler invocations.
Multi-platform support: The same tool and the same BUILD files can be used to build software for different architectures, and even different platforms. At Google, we use Bazel to build everything from server applications running on systems in our data centers to client apps running on mobile phones.
Reproducibility: In BUILD files, each library, test and binary must specify its direct dependencies completely. Bazel uses this dependency information to know what must be rebuilt when you make changes to a source file, and which tasks can run in parallel. This means that all builds are incremental and will always produce the same result.
Scalable: Bazel can handle large builds; at Google, it is common for a server binary to have 100k source files, and builds where no files were changed take about ~200ms.
Make, Ninja: These tools give very exact control over what commands get invoked to build files, but it’s up to the user to write rules that are correct.
Users interact with Bazel on a higher level. For example, Bazel has built-in rules for “Java test”, “C++ binary”, and notions such as “target platform” and “host platform”. These rules have been battle tested to be foolproof.
Ant and Maven: Ant and Maven are primarily geared toward Java, while Bazel handles multiple languages. Bazel encourages subdividing codebases in smaller reusable units, and can rebuild only ones that need rebuilding. This speeds up development when working with larger codebases.
Gradle: Bazel configuration files are much more structured than Gradle’s, letting Bazel understand exactly what each action does. This allows for more parallelism and better reproducibility.
Pants, Buck: Both tools were created and developed by ex-Googlers at Twitter and Foursquare, and Facebook respectively. They have been modeled after Bazel, but their feature sets are different, so they aren’t viable alternatives for us.
Bazel is a flavor of the tool that Google uses to build its server software internally. It has expanded to build other software as well, like mobile apps (iOS, Android) that connect to our servers.
Bazel shares most of its code with the internal tool and its rules are used for millions of builds every day.
A long time ago, Google built its software using large, generated Makefiles. These led to slow and unreliable builds, which began to interfere with our developers’ productivity and the company’s agility. Bazel was a way to solve these problems.
Google’s in-house flavor of Bazel does use build clusters, so Bazel does have hooks in the code base to plug in a remote build cache or a remote execution system.
The open source Bazel code runs build operations locally. We believe that this is fast enough for most of our users, but work is underway to provide distributed caching.
For our server code base, we use the following development workflow:
All our server code is in a single, gigantic version control system.
Everybody builds their software with Bazel.
Different teams own different parts of the source tree, and make their components available as BUILD targets.
Branching is primarily used for managing releases, so everybody develops their software at the head revision.
Bazel is a cornerstone of this philosophy: since Bazel requires all dependencies to be fully specified, we can predict which programs and tests are affected by a change, and vet them before submission.
More background on the development process at Google can be found on the eng tools blog.
Building software should be fun and easy. Slow and unpredictable builds take the fun out of programming.
Bazel may give you faster build times because it can recompile only the files that need to be recompiled. Similarly, it can skip re-running tests that it knows haven’t changed.
Bazel produces deterministic results. This eliminates skew between incremental and clean builds, laptop and CI system, etc.
Bazel can build different client and server apps with the same tool from the same workspace. For example, you can change a client/server protocol in a single commit, and test that the updated mobile app works with the updated server, building both with the same tool, reaping all the aforementioned benefits of Bazel.
Yes. For a simple example, see:
The Bazel source code itself provides a more complex example:
Bazel shines at building and testing projects with the following properties:
Bazel runs on Linux, macOS (OS X), and Windows.
Porting to other UNIX platforms should be relatively easy, as long as a JDK is available for the platform.
Bazel tries to be smart about caching. This means that it is not good for running build operations whose outputs should not be cached. For example, the following steps should not be run from Bazel:
If your build consists of a few long, sequential steps, Bazel may not be able to help much. You’ll get more speed by breaking long steps into smaller, discrete targets that Bazel can run in parallel.
The core features (C++, Java, and shell rules) have extensive use inside Google, so they are thoroughly tested and have very little churn. Similarly, we test new versions of Bazel across hundreds of thousands of targets every day to find regressions, and we release new versions multiple times every month.
In short, except for features marked as experimental, Bazel should Just Work. Changes to non-experimental rules will be backward compatible. A more detailed list of feature support statuses can be found in our support document.
Inside Google, we make sure that Bazel crashes are very rare. This should also hold for our open source codebase.
See our getting started document.
With Docker you can easily create sandboxes with fixed OS releases, for example, Ubuntu 12.04, Fedora 21. This solves the problem of reproducibility for the system environment – that is, “which version of /usr/bin/c++ do I need?”
Docker does not address reproducibility with regard to changes in the source code. Running Make with an imperfectly written Makefile inside a Docker container can still yield unpredictable results.
Inside Google, we check tools into source control for reproducibility. In this way, we can vet changes to tools (“upgrade GCC to 4.6.1”) with the same mechanism as changes to base libraries (“fix bounds check in OpenSSL”).
With Bazel, you can build standalone, statically linked binaries in C/C++, and self-contained jar files for Java. These run with few dependencies on normal UNIX systems, and as such should be simple to install inside a Docker container.
Bazel has conventions for structuring more complex programs, for example, a Java program that consumes a set of data files, or runs another program as subprocess. It is possible to package up such environments as standalone archives, so they can be deployed on different systems, including Docker images.
Yes, you can use our Docker rules to build reproducible Docker images.
For Java and C++ binaries, yes, assuming you do not change the toolchain. If you have build steps that involve custom recipes (for example, executing binaries through a shell script inside a rule), you will need to take some extra care:
Do not use dependencies that were not declared. Sandboxed execution (–spawn_strategy=sandboxed, only on Linux) can help find undeclared dependencies.
Avoid storing timestamps and user-IDs in generated files. ZIP files and other archives are especially prone to this.
Avoid connecting to the network. Sandboxed execution can help here too.
Avoid processes that use random numbers, in particular, dictionary traversal is randomized in many programming languages.
For IntelliJ, check out the IntelliJ with Bazel plugin.
For XCode, check out Tulsi.
For Eclipse, check out E4B plugin.
For other IDEs, check out the blog post on how these plugins work.
Bazel returns a non-zero exit code if the build or test invocation fails, and this should be enough for basic CI integration. Since Bazel does not need clean builds for correctness, the CI system should not be configured to clean before starting a build/test run.
Further details on exit codes are in the User Manual.
See our roadmap.
We have an extension mechanism called Skylark that allows you to add new rules without recompiling Bazel.
If our extension mechanism is insufficient for your use case, email the mailing list for advice: firstname.lastname@example.org.
See our contribution guidelines.
We still have to refactor the interfaces between the public code in Bazel and our internal extensions frequently. This makes it hard to do much development in the open. See our governance plan for more details.
We are reachable at email@example.com.
Open on issue on GitHub.
This is an internal name for the tool. Please refer to Bazel as Bazel.
Until the first (Alpha) release, Bazel was not available externally, so open source projects such as Chromium, Android, etc. could not use it. In addition, the original lack of Windows support was a problem for building Windows applications, such as Chrome.
The same way as “basil” (the herb) in US English: “BAY-zel”. It rhymes with “hazel”. IPA: /ˈbeɪzˌəl/