## Multiprocessing with gRPC Python

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`](http://man7.org/linux/man-pages/man2/fork.2.html), 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.

## Running the Example

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`](https://lwn.net/Articles/542629/) socket option.

To run the server,
[ensure `bazel` is installed](https://docs.bazel.build/versions/master/install.html)
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.
...
```

Now, start the client by running

```
bazel run //examples/python/multiprocessing:client -- [SERVER_ADDRESS]
```

For example,

```
bazel run //examples/python/multiprocessing:client -- [::]:33915
```