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This article covers sandboxing in Bazel and debugging your sandboxing environment.
Sandboxing is a permission restricting strategy that isolates processes from each other or from resources in a system. For Bazel, this means restricting file system access.
Bazel‘s file system sandbox runs processes in a working directory that only contains known inputs, such that compilers and other tools don’t see source files they should not access, unless they know the absolute paths to them.
Sandboxing doesn‘t hide the host environment in any way. Processes can freely access all files on the file system. However, on platforms that support user namespaces, processes can’t modify any files outside their working directory. This ensures that the build graph doesn't have hidden dependencies that could affect the reproducibility of the build.
More specifically, Bazel constructs an execroot/ directory for each action, which acts as the action's work directory at execution time. execroot/ contains all input files to the action and serves as the container for any generated outputs. Bazel then uses an operating-system-provided technique, containers on Linux and sandbox-exec on macOS, to constrain the action within execroot/.
Without action sandboxing, Bazel doesn't know if a tool uses undeclared input files (files that are not explicitly listed in the dependencies of an action). When one of the undeclared input files changes, Bazel still believes that the build is up-to-date and won’t rebuild the action. This can result in an incorrect incremental build.
Incorrect reuse of cache entries creates problems during remote caching. A bad cache entry in a shared cache affects every developer on the project, and wiping the entire remote cache is not a feasible solution.
Sandboxing mimics the behavior of remote execution — if a build works well with sandboxing, it will likely also work with remote execution. By making remote execution upload all necessary files (including local tools), you can significantly reduce maintenance costs for compile clusters compared to having to install the tools on every machine in the cluster every time you want to try out a new compiler or make a change to an existing tool.
You can choose which kind of sandboxing to use, if any, with the strategy flags. Using the sandboxed strategy makes Bazel pick one of the sandbox implementations listed below, preferring an OS-specific sandbox to the less hermetic generic one. Persistent workers run in a generic sandbox if you pass the --worker_sandboxing flag.
The local (a.k.a. standalone) strategy does not do any kind of sandboxing. It simply executes the action's command line with the working directory set to the execroot of your workspace.
processwrapper-sandbox is a sandboxing strategy that does not require any “advanced” features - it should work on any POSIX system out of the box. It builds a sandbox directory consisting of symlinks that point to the original source files, executes the action's command line with the working directory set to this directory instead of the execroot, then moves the known output artifacts out of the sandbox into the execroot and deletes the sandbox. This prevents the action from accidentally using any input files that are not declared and from littering the execroot with unknown output files.
linux-sandbox goes one step further and builds on top of the processwrapper-sandbox. Similar to what Docker does under the hood, it uses Linux Namespaces (User, Mount, PID, Network and IPC namespaces) to isolate the action from the host. That is, it makes the entire filesystem read-only except for the sandbox directory, so the action cannot accidentally modify anything on the host filesystem. This prevents situations like a buggy test accidentally rm -rf'ing your $HOME directory. Optionally, you can also prevent the action from accessing the network. linux-sandbox uses PID namespaces to prevent the action from seeing any other processes and to reliably kill all processes (even daemons spawned by the action) at the end.
darwin-sandbox is similar, but for macOS. It uses Apple's sandbox-exec tool to achieve roughly the same as the Linux sandbox.
Both the linux-sandbox and the darwin-sandbox do not work in a “nested” scenario due to restrictions in the mechanisms provided by the operating systems. Because Docker also uses Linux namespaces for its container magic, you cannot easily run linux-sandbox inside a Docker container, unless you use docker run --privileged. On macOS, you cannot run sandbox-exec inside a process that's already being sandboxed. Thus, in these cases, Bazel automatically falls back to using processwrapper-sandbox.
If you would rather get a build error — such as to not accidentally build with a less strict execution strategy — explicitly modify the list of execution strategies that Bazel tries to use (for example, bazel build --spawn_strategy=worker,linux-sandbox).
Dynamic execution usually requires sandboxing for local execution. To opt out, pass the --experimental_local_lockfree_output flag. Dynamic execution silently sandboxes persistent workers.
Sandboxing incurs extra setup and teardown cost. How big this cost is depends on many factors, including the shape of the build and the performance of the host OS. For Linux, sandboxed builds are rarely more than a few percent slower. Setting --reuse_sandbox_directories can mitigate the setup and teardown cost.
Sandboxing effectively disables any cache the tool may have. You can mitigate this by using persistent workers, at the cost of weaker sandbox guarantees.
Multiplex workers require explicit worker support to be sandboxed. Workers that do not support multiplex sandboxing run as singleplex workers under dynamic execution, which can cost extra memory.
Follow the strategies below to debug issues with sandboxing.
On some platforms, such as Google Kubernetes Engine{: .external} cluster nodes or Debian, user namespaces are deactivated by default due to security concerns. If the /proc/sys/kernel/unprivileged_userns_clone file exists and contains a 0, you can activate user namespaces by running:
sudo sysctl kernel.unprivileged_userns_clone=1
The sandbox may fail to execute rules because of the system setup. If you see a message like namespace-sandbox.c:633: execvp(argv[0], argv): No such file or directory, try to deactivate the sandbox with --strategy=Genrule=local for genrules, and --spawn_strategy=local for other rules.
If your build failed, use --verbose_failures and --sandbox_debug to make Bazel show the exact command it ran when your build failed, including the part that sets up the sandbox.
Example error message:
ERROR: path/to/your/project/BUILD:1:1: compilation of rule
'//path/to/your/project:all' failed:
Sandboxed execution failed, which may be legitimate (such as a compiler error),
or due to missing dependencies. To enter the sandbox environment for easier
debugging, run the following command in parentheses. On command failure, a bash
shell running inside the sandbox will then automatically be spawned
namespace-sandbox failed: error executing command
(cd /some/path && \
exec env - \
LANG=en_US \
PATH=/some/path/bin:/bin:/usr/bin \
PYTHONPATH=/usr/local/some/path \
/some/path/namespace-sandbox @/sandbox/root/path/this-sandbox-name.params --
/some/path/to/your/some-compiler --some-params some-target)
You can now inspect the generated sandbox directory and see which files Bazel created and run the command again to see how it behaves.
Note that Bazel does not delete the sandbox directory when you use --sandbox_debug. Unless you are actively debugging, you should disable --sandbox_debug because it fills up your disk over time.