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# Remote worker
This program implements a remote execution worker that uses gRPC to accept work
requests. It can work as a remote execution worker, a cache worker, or both.
The simplest setup is as follows:
- First build remote_worker and run it.
bazel build src/tools/remote_worker:all
bazel-bin/src/tools/remote_worker/remote_worker \
--work_path=/tmp/test \
--listen_port=8080
- Then you run Bazel pointing to the remote_worker instance.
bazel --host_jvm_args=-Dbazel.DigestFunction=SHA1 build \
--spawn_strategy=remote --remote_cache=localhost:8080 \
--remote_executor=localhost:8080 src/tools/generate_workspace:all
The above command will build generate_workspace with remote spawn strategy that
uses the local worker as the distributed caching and execution backend.
## Sandboxing
If you run the remote_worker on Linux, you can also enable sandboxing for increased hermeticity:
bazel-bin/src/tools/remote_worker/remote_worker \
--work_path=/tmp/test \
--listen_port=8080 \
--sandboxing \
--sandboxing_writable_path=/run/shm \
--sandboxing_tmpfs_dir=/tmp \
--sandboxing_block_network
As you can see, the specific behavior of the sandbox can be tuned via the flags
- --sandboxing_writable_path=<path> makes a path writable for running actions.
- --sandboxing_tmpfs_dir=<path> will mount a fresh, empty tmpfs for each running action on a path.
- --sandboxing_block_network will put each running action into its own network namespace that has
no network connectivity except for its own "localhost". Note that due to a Linux kernel issue this
might result in a loss of performance if you run many actions in parallel. For long running tests
it probably won't matter much, though.
## Hazelcast caching
You can also use a Hazelcast server for the distributed cache as follows:
Suppose your Hazelcast server is listening on address:port. Then, run the
remote worker with --hazelcast_node=address:port.