Simple Python RPC system (ZeroMQ + Threading/Greenlets/IOLoop)
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NetCall -- a simple Python RPC system

This is a simple Python RPC system using ZeroMQ as a transport and supporting various concurrency techniques: Python Threading, Tornado/IOLoop, Gevent, Eventlet, Greenhouse,

Initially the code was forked from ZPyRPC in Feb 2014. The fork has added support for Python Threading and various Greenlet environments, refactored code, made incompatible API changes, added new features and examples.

Feature Overview

  • Reasonably fast
  • Simple hackable code
  • Really easy API
  • Auto load balancing of multiple services (thanks to ZeroMQ)
  • Full ZeroMQ routing as a bonus
  • Asynchronous servers (Threading, Tornado/IOLoop, Gevent, Eventlet, Greenhouse)
  • Both synchronous and asynchronous clients (Threading, Tornado/IOLoop, Gevent, Eventlet, Greenhouse)
  • Ability to set a timeout on RPC calls
  • Ability to run multiple services in a single process
  • Pluggable serialization (Pickle [default], JSON, MessagePack)
  • Support generators (functions yielding) over RPC, including bi-directional capabilities (next(), send(), throw() and close()).


To create a service:

from netcall.tornado import TornadoRPCService

echo = TornadoRPCService()

def echo(self, s):
    return s

echo.register(lambda n: "Hello %s" % n, name='hello')    

echo.bind('ipc:///tmp/echo.service')  # multiple endpoints

To talk to this service:

from netcall.threading import ThreadingRPCClient

p = ThreadingRPCClient()
p.connect('ipc:///tmp/echo.service')  # auto load balancing
p.echo('Hi there')
'Hi there'
'Hello World'

See other examples.


Using generators over RPC will allow you to:

  • Push data to a client whenever your service is ready to produce them.
  • Control the flow of data whenever the client is ready to receive them.
  • Have the client send data (and throw exceptions!) to the service during the communication process.
  • Have the client cancel the transmission in the middle.

Generators being part of the Python language itself (yield expression), you can express all of these usages naturally in the code.

Generators are semicoroutines. They might be powerful, they also have some limitations. The communication flow is fully synchronous. For instance, your service cannot prepare its next reply until the client called next() on it. You can see the yield expression and the next(), send(), throw() and close() calls as blocking. However, you can use threads or coroutines on either sides to allow asynchronous processes.

Example of a service yielding:

from import GeventRPCService

echo_service = GeventRPCService()

def echo(value=None):
    print "Execution starts when 'next()' is called for the first time."
        while True:
                value = (yield value)
            except Exception, e:
                value = e
        print "Don't forget to clean up when 'close()' is called."


Example of a client consuming a generator (S> are print-out from the service):

from import GeventRPCClient

client = GeventRPCClient()

generator = client.echo(1)
S> Execution starts when 'next()' is called for the first time.
> 1
> None
print generator.send(2)
> 2
generator.throw(TypeError, "spam")
> TypeError('spam',)
generator = None # implicitly call generator.close()
S> Don't forget to clean up when 'close()' is called.