tree: e6493ae23b5a77cf9c1ae6c93b931c6acc32b9dd [path history] [tgz]
  1. test/
  2. BUILD
  3. client.py
  4. prime.proto
  5. README.md
  6. server.py
examples/python/multiprocessing/README.md

Multiprocessing with gRPC Python

Multiprocessing allows application developers to sidestep the Python global interpreter lock and achieve true parallelism 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, any threads in a critical section may leave the state of the gRPC library invalid in the child process. See this excellent research paper for a thorough discussion of the topic.

Calling fork without exec in your 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 socket option.

_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.

Running the Example

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

Alternatively, generate code using the following and then run the client and server directly:

cd examples/python/multiprocessing
python -m grpc_tools.protoc -I . prime.proto  --python_out=. --grpc_python_out=.