|
|
|
|
## 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.
|
|
|
|
|
|
|
|
|
|
## Calculating Prime Numbers with Multiple Processes
|
|
|
|
|
|
|
|
|
|
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. 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`.
|
|
|
|
|
|
|
|
|
|
```python
|
|
|
|
|
_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. If we spin up more, then the
|
|
|
|
|
kernel will have to multiplex the processes on the available CPUs.
|
|
|
|
|
|
|
|
|
|
## Running the Example
|
|
|
|
|
|
|
|
|
|
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.
|
|
|
|
|
...
|
|
|
|
|
```
|
|
|
|
|
|
|
|
|
|
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
|
|
|
|
|
```
|
