Interprocess communication in Python
Question:
What is a good way to communicate between two separate Python runtimes? Thing’s I’ve tried:
- reading/writing on named pipes e.g.
os.mkfifo (feels hacky)
dbus services (worked on desktop, but too heavyweight for headless)- sockets (seems too low-level; surely there’s a higher level module to use?)
My basic requirement is to be able to run python listen.py like a daemon, able to receive messages from python client.py. The client should just send a message to the existing process and terminate, with return code 0 for success and nonzero for failure (i.e. a two-way communication will be required.)
Answers:
I would use sockets; local communication was strongly optimized, so you shouldn’t have performance problems and it gives you the ability to distribute your application to different physical nodes if the needs should arise.
With regard to the “low-level” approach, you’re right. But you can always use an higher-level wrapper depending on your needs. XMLRPC could be a good candidate, but it is maybe overkill for the task you’re trying to perform.
Twisted offers some good protocol simple implementations, such as LineReceiver (for simple line based messages) or the more elegant AMP (which was, by the way, standardized and implemented in different languages).
I would use sockets, but use Twisted to give you some abstraction, and to make things easy. Their Simple Echo Client / Server example is a good place to start.
You would just have to combine the files and instantiate and run either the client or server depending on the passed argument(s).
Nah, zeromq is the way to go. Delicious, isn’t it?
import argparse
import zmq
parser = argparse.ArgumentParser(description='zeromq server/client')
parser.add_argument('--bar')
args = parser.parse_args()
if args.bar:
# client
context = zmq.Context()
socket = context.socket(zmq.REQ)
socket.connect('tcp://127.0.0.1:5555')
socket.send(args.bar)
msg = socket.recv()
print msg
else:
# server
context = zmq.Context()
socket = context.socket(zmq.REP)
socket.bind('tcp://127.0.0.1:5555')
while True:
msg = socket.recv()
if msg == 'zeromq':
socket.send('ah ha!')
else:
socket.send('...nah')
The multiprocessing library provides listeners and clients that wrap sockets and allow you to pass arbitrary python objects.
Your server could listen to receive python objects:
from multiprocessing.connection import Listener
address = ('localhost', 6000) # family is deduced to be 'AF_INET'
listener = Listener(address, authkey=b'secret password')
conn = listener.accept()
print 'connection accepted from', listener.last_accepted
while True:
msg = conn.recv()
# do something with msg
if msg == 'close':
conn.close()
break
listener.close()
Your client could send commands as objects:
from multiprocessing.connection import Client
address = ('localhost', 6000)
conn = Client(address, authkey=b'secret password')
conn.send('close')
# can also send arbitrary objects:
# conn.send(['a', 2.5, None, int, sum])
conn.close()
From my experience, rpyc is by far the simplest and most elegant way to go about it.
Check out a cross-platform library/server called RabbitMQ. Might be too heavy for two-process communication, but if you need multi-process or multi-codebase communication (with various different means, e.g. one-to-many, queues, etc), it is a good option.
Requirements:
$ pip install pika
$ pip install bson # for sending binary content
$ sudo apt-get rabbitmq-server # ubuntu, see rabbitmq installation instructions for other platforms
Publisher (sends data):
import pika, time, bson, os
connection = pika.BlockingConnection(pika.ConnectionParameters('localhost'))
channel = connection.channel()
channel.exchange_declare(exchange='logs', type='fanout')
i = 0
while True:
data = {'msg': 'Hello %s' % i, b'data': os.urandom(2), 'some': bytes(bytearray(b'x00x0Fx98x24'))}
channel.basic_publish(exchange='logs', routing_key='', body=bson.dumps(data))
print("Sent", data)
i = i + 1
time.sleep(1)
connection.close()
Subscriber (receives data, can be multiple):
import pika, bson
connection = pika.BlockingConnection(pika.ConnectionParameters(host='localhost'))
channel = connection.channel()
channel.exchange_declare(exchange='logs', type='fanout')
result = channel.queue_declare(exclusive=True)
queue_name = result.method.queue
channel.queue_bind(exchange='logs', queue=queue_name)
def callback(ch, method, properties, body):
data = bson.loads(body)
print("Received", data)
channel.basic_consume(callback, queue=queue_name, no_ack=True)
channel.start_consuming()
Examples based on https://www.rabbitmq.com/tutorials/tutorial-two-python.html
Based on @vsekhar’s answer, here is a Python 3 version with more details and multiple connections:
Server
from multiprocessing.connection import Listener
listener = Listener(('localhost', 6000), authkey=b'secret password')
running = True
while running:
conn = listener.accept()
print('connection accepted from', listener.last_accepted)
while True:
msg = conn.recv()
print(msg)
if msg == 'close connection':
conn.close()
break
if msg == 'close server':
conn.close()
running = False
break
listener.close()
Client
from multiprocessing.connection import Client
import time
# Client 1
conn = Client(('localhost', 6000), authkey=b'secret password')
conn.send('foo')
time.sleep(1)
conn.send('close connection')
conn.close()
time.sleep(1)
# Client 2
conn = Client(('localhost', 6000), authkey=b'secret password')
conn.send('bar')
conn.send('close server')
conn.close()
I found this thread as one of the first results on Python IPC, but I was looking for something that could run with AsyncIO. I eventually found IPyC that provided nice async capabilities, so I came back here to share that gem. IPyC also supports synchronous implementations.
It is possible to use the IPyC library from two different processes, but here is a small example with two asyncio tasks in the same file. It is using TCP:9999 as default port.
NOTE: This example crashes with unexpected keyword argument 'loop' on Python >=3.10; that is due to an interface change in asyncio. I have tested with v3.9.
import asyncio
import ipyc
import json
import logging
logging.basicConfig(level=logging.INFO) # Set to DEBUG to see inner workings
# IPyC has support for custom (de)serialization; using json as example here
ipyc.IPyCSerialization.add_custom_serialization(list, json.dumps)
ipyc.IPyCSerialization.add_custom_deserialization(list, json.loads)
## Host stuff
host = ipyc.AsyncIPyCHost()
@host.on_connect
async def on_client_connect(connection: ipyc.AsyncIPyCLink):
logging.info("Got a connection")
while connection.is_active():
message = await connection.receive()
if message:
logging.info(f"Received: {message}")
logging.info("Connection closed")
## Client stuff
async def client_task():
client = ipyc.AsyncIPyCClient()
link = await client.connect()
for i in range(3):
await link.send(["Hello World!", i, 3.14])
await asyncio.sleep(1)
await client.close() # Close the connection
await asyncio.sleep(1)
## AsyncIO stuff
loop = asyncio.get_event_loop()
loop.create_task(host.start())
loop.run_until_complete(client_task())
What is a good way to communicate between two separate Python runtimes? Thing’s I’ve tried:
- reading/writing on named pipes e.g.
os.mkfifo(feels hacky) dbusservices (worked on desktop, but too heavyweight for headless)- sockets (seems too low-level; surely there’s a higher level module to use?)
My basic requirement is to be able to run python listen.py like a daemon, able to receive messages from python client.py. The client should just send a message to the existing process and terminate, with return code 0 for success and nonzero for failure (i.e. a two-way communication will be required.)
I would use sockets; local communication was strongly optimized, so you shouldn’t have performance problems and it gives you the ability to distribute your application to different physical nodes if the needs should arise.
With regard to the “low-level” approach, you’re right. But you can always use an higher-level wrapper depending on your needs. XMLRPC could be a good candidate, but it is maybe overkill for the task you’re trying to perform.
Twisted offers some good protocol simple implementations, such as LineReceiver (for simple line based messages) or the more elegant AMP (which was, by the way, standardized and implemented in different languages).
I would use sockets, but use Twisted to give you some abstraction, and to make things easy. Their Simple Echo Client / Server example is a good place to start.
You would just have to combine the files and instantiate and run either the client or server depending on the passed argument(s).
Nah, zeromq is the way to go. Delicious, isn’t it?
import argparse
import zmq
parser = argparse.ArgumentParser(description='zeromq server/client')
parser.add_argument('--bar')
args = parser.parse_args()
if args.bar:
# client
context = zmq.Context()
socket = context.socket(zmq.REQ)
socket.connect('tcp://127.0.0.1:5555')
socket.send(args.bar)
msg = socket.recv()
print msg
else:
# server
context = zmq.Context()
socket = context.socket(zmq.REP)
socket.bind('tcp://127.0.0.1:5555')
while True:
msg = socket.recv()
if msg == 'zeromq':
socket.send('ah ha!')
else:
socket.send('...nah')
The multiprocessing library provides listeners and clients that wrap sockets and allow you to pass arbitrary python objects.
Your server could listen to receive python objects:
from multiprocessing.connection import Listener
address = ('localhost', 6000) # family is deduced to be 'AF_INET'
listener = Listener(address, authkey=b'secret password')
conn = listener.accept()
print 'connection accepted from', listener.last_accepted
while True:
msg = conn.recv()
# do something with msg
if msg == 'close':
conn.close()
break
listener.close()
Your client could send commands as objects:
from multiprocessing.connection import Client
address = ('localhost', 6000)
conn = Client(address, authkey=b'secret password')
conn.send('close')
# can also send arbitrary objects:
# conn.send(['a', 2.5, None, int, sum])
conn.close()
From my experience, rpyc is by far the simplest and most elegant way to go about it.
Check out a cross-platform library/server called RabbitMQ. Might be too heavy for two-process communication, but if you need multi-process or multi-codebase communication (with various different means, e.g. one-to-many, queues, etc), it is a good option.
Requirements:
$ pip install pika
$ pip install bson # for sending binary content
$ sudo apt-get rabbitmq-server # ubuntu, see rabbitmq installation instructions for other platforms
Publisher (sends data):
import pika, time, bson, os
connection = pika.BlockingConnection(pika.ConnectionParameters('localhost'))
channel = connection.channel()
channel.exchange_declare(exchange='logs', type='fanout')
i = 0
while True:
data = {'msg': 'Hello %s' % i, b'data': os.urandom(2), 'some': bytes(bytearray(b'x00x0Fx98x24'))}
channel.basic_publish(exchange='logs', routing_key='', body=bson.dumps(data))
print("Sent", data)
i = i + 1
time.sleep(1)
connection.close()
Subscriber (receives data, can be multiple):
import pika, bson
connection = pika.BlockingConnection(pika.ConnectionParameters(host='localhost'))
channel = connection.channel()
channel.exchange_declare(exchange='logs', type='fanout')
result = channel.queue_declare(exclusive=True)
queue_name = result.method.queue
channel.queue_bind(exchange='logs', queue=queue_name)
def callback(ch, method, properties, body):
data = bson.loads(body)
print("Received", data)
channel.basic_consume(callback, queue=queue_name, no_ack=True)
channel.start_consuming()
Examples based on https://www.rabbitmq.com/tutorials/tutorial-two-python.html
Based on @vsekhar’s answer, here is a Python 3 version with more details and multiple connections:
Server
from multiprocessing.connection import Listener
listener = Listener(('localhost', 6000), authkey=b'secret password')
running = True
while running:
conn = listener.accept()
print('connection accepted from', listener.last_accepted)
while True:
msg = conn.recv()
print(msg)
if msg == 'close connection':
conn.close()
break
if msg == 'close server':
conn.close()
running = False
break
listener.close()
Client
from multiprocessing.connection import Client
import time
# Client 1
conn = Client(('localhost', 6000), authkey=b'secret password')
conn.send('foo')
time.sleep(1)
conn.send('close connection')
conn.close()
time.sleep(1)
# Client 2
conn = Client(('localhost', 6000), authkey=b'secret password')
conn.send('bar')
conn.send('close server')
conn.close()
I found this thread as one of the first results on Python IPC, but I was looking for something that could run with AsyncIO. I eventually found IPyC that provided nice async capabilities, so I came back here to share that gem. IPyC also supports synchronous implementations.
It is possible to use the IPyC library from two different processes, but here is a small example with two asyncio tasks in the same file. It is using TCP:9999 as default port.
NOTE: This example crashes with unexpected keyword argument 'loop' on Python >=3.10; that is due to an interface change in asyncio. I have tested with v3.9.
import asyncio
import ipyc
import json
import logging
logging.basicConfig(level=logging.INFO) # Set to DEBUG to see inner workings
# IPyC has support for custom (de)serialization; using json as example here
ipyc.IPyCSerialization.add_custom_serialization(list, json.dumps)
ipyc.IPyCSerialization.add_custom_deserialization(list, json.loads)
## Host stuff
host = ipyc.AsyncIPyCHost()
@host.on_connect
async def on_client_connect(connection: ipyc.AsyncIPyCLink):
logging.info("Got a connection")
while connection.is_active():
message = await connection.receive()
if message:
logging.info(f"Received: {message}")
logging.info("Connection closed")
## Client stuff
async def client_task():
client = ipyc.AsyncIPyCClient()
link = await client.connect()
for i in range(3):
await link.send(["Hello World!", i, 3.14])
await asyncio.sleep(1)
await client.close() # Close the connection
await asyncio.sleep(1)
## AsyncIO stuff
loop = asyncio.get_event_loop()
loop.create_task(host.start())
loop.run_until_complete(client_task())