Multiprocessing allows application developers to sidestep the Python global interpreter lock and achieve true concurrency on multicore systems. Unfortunately, using multiprocessing and gRPC Python is not yet as simple as instantiating your server with a futures.ProcessPoolExecutor.
The library is implemented as a C extension, maintaining much of the state that drives the system in native code. As such, upon calling fork, much of the state copied into the child process is invalid, leading to hangs and crashes.
However, calling fork without exec in your python process is supported before any gRPC servers have been instantiated. Application developers can take advantage of this to parallelize their CPU-intensive operations.
This example calculates the first 10,000 prime numbers as an RPC. We instantiate one server per subprocess, balancing requests between the servers using the SO_REUSEPORT socket option. Note that this option is not available in manylinux1 distributions, which are, as of the time of writing, the only gRPC Python wheels available on PyPI. To take advantage of this feature, you'll need to build from source, either using bazel (as we do for these examples) or via pip, using pip install grpcio --no-binary grpcio.
_PROCESS_COUNT = multiprocessing.cpu_count()
On the server side, we detect the number of CPUs available on the system and spawn exactly that many child processes. If we spin up fewer, we won't be taking full advantage of the hardware resources available.
To run the server, ensure bazel is installed and run:
bazel run //examples/python/multiprocessing:server &
Note the address at which the server is running. For example,
... [PID 107153] Binding to '[::]:33915' [PID 107507] Starting new server. [PID 107508] Starting new server. ...
Note that several servers have been started, each with its own PID.
Now, start the client by running
bazel run //examples/python/multiprocessing:client -- [SERVER_ADDRESS]
For example,
bazel run //examples/python/multiprocessing:client -- [::]:33915