I want to share a variable between two threads using atomic operations in the interpreter, as described here http://effbot.org/zone/thread-synchronization.htm. A simple assignment (single bytecode operation) of a core data type should be thread safe, beacuse of the GIL in python < 3.2. So far the theory. The follwing code can be run in either master or slave mode (-m or -s). The master mode does constantly send data via UDP. The slave mode does create a thread to read data from a udp port and update a variable on each received packet.
The example code does pass the shared variable as an argument to the thread on creation. I've tried also by using a global variable or passing a thread local store to the thread.
The result is alwas the same. Inside the read_time_master thread the variable gets assigned. But in the main thread, the value of shared variable isn't updated.
#!/usr/bin/env python
import socket
import itertools
import multiprocessing
from optparse import OptionParser
from time import sleep
PORT = 1666
def read_time_master(sock, time_master):
while True:
time_master = float(sock.recvfrom(1024)[0])
def main():
time_master = 0.0
p = OptionParser()
p.add_option('--master', '-m', action='store_true')
p.add_option('--slave', '-s', action='store_true')
options, arguments = p.parse_args()
if options.master or options.slave:
sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM, 0)
if options.master:
sock.connect(('127.0.0.1', PORT))
if options.slave:
sock.bind(('0.0.0.0', PORT))
recv_thread = multiprocessing.Process(target=read_time_master, args=(sock, time_master))
recv_thread.start()
for time in itertools.count():
print time
if options.slave:
print "master: %f" % time_master # -> not updated from other thread
if options.master:
try:
sock.send(str(time))
except socket.error:
pass
sleep(1)
if options.master or options.slave:
sock.close()
if __name__ == '__main__':
main()
You're using multiprocessing, not threading, which isn't helping your situation. If you were using threading.Thread to create the background worker you'd probably be able to get what you needed by simply throwing in a global time_master call within the function that's being controlled by your background operation. Because you're using multiprocessing, not threading, you will likely need to look into the multiprocessing.Queue class for containers that you can use to pass information back and forth between your processes or to synchronize them. You can also create variables that are shared between the processes as well (all of this is covered in the multiprocessing documentation / examples at the Python Homepage
You can use Shared Memory, as explained here http://docs.python.org/2/library/multiprocessing.html#sharing-state-between-processes. Remember to wait for the process to finish before reading from the shared space.
Related
I have two python programs which one of them connects to a bluetooth device(socket package), it receives and saves data from device, and another one read the stored data and draw a real time plot. I should make one application from these two programs.
I tried to mix these two python programs, but since bluetooth should wait to receive data (through a while loop), the other parts of program does not work. I tried to solve this problem using Clock.schedule_interval, but the program will hang after a period of time. So I decided to run these two programs simultaneously. I read, we can run some python programs at a same time using a python script. Is there any trick to join these two programs and build one application?
Any help would be greatly appreciated.
Install threaded:
pip install threaded
Create a new python file:
from threading import Thread
def runFile1(): import file1
def runFile2(): import file2
Thread(target=runFile1).start()
runFile2()
Run the new python file.
It can be done with threading. To do communication between the threaded function and your main function, use objects such as queue.Queue and threading.Event.
the bluetooth functions can be placed into a function that is the target of the thread
import time
from threading import Thread
from queue import Queue
class BlueToothFunctions(Thread):
def __init__(self, my_queue):
super().__init__()
self.my_queue = my_queue
# optional: causes this thread to end immediately if the main program is terminated
self.daemon = True
def run(self) -> None:
while True:
# do all the bluetooth stuff foreverer
g = self.my_queue.get()
if g == (None, None):
break
print(g)
time.sleep(1.0)
print("bluetooth closed")
if __name__ == '__main__':
_queue = Queue() # just one way to communicate to a thread
# pass an object reference so both main and thread have a way to communicate on this common queue
my_bluetooth = BlueToothFunctions(_queue)
my_bluetooth.start() # creates the thread and executes run() method
for i in range(5):
# communicate to the threaded functions
_queue.put(i)
_queue.put((None, None)) # optional, a way to cause the thread to end
my_bluetooth.join(timeout=5.0) # optional, pause here until thread ends
print('program complete')
I'm trying to implement a python script that can send serial data generated by two different threads. Now I was able to set the threads running, I would like to create a shared memory with semaphores between the manager process and the sub threads. The code of the manager is the following
import DetectorService
import HearingService
if __name__=='__main__':
global shm01
t1 = Thread(target = DetectorService.start)
t2 = Thread(target = HearingService.start)
t1.setDaemon(True)
t2.setDaemon(True)
t1.start()
t2.start()
while True:
# Get data from thread01
# Get data from thread02
# Send data via serial port
pass
Then there is a minimal representation of a thread:
import time
def start():
while True:
a = a + 1
time.sleep(0.5)
For instance, my goal is to access the a variable and send the value through the serial port. The serial communication is not a problem, I know how to do it.
So my question is, how can I implement the shared memory?
There are various ways to implement this. One simple way to serialize the access to a shared resource (which will make the access safe) is by using a Lock. You can read more here.
As an example, your threads might collect the data that needs to be sent in a shared list, like this:
data_to_send = []
and when you need to read from or write to that list, simply do so in the context of a global Lock object. Like this:
with lock:
data_to_send.append(some_data)
please see the code below :
server.py
import zmq
import time
from multiprocessing import Process
class A:
def __init__(self):
ctx = zmq.Context(1)
sock = zmq.Socket(ctx, zmq.PUB)
sock.bind('ipc://test')
p = Process(target=A.run, args=(sock,))
p.start() # Process calls run, but the client can't receive messages
p.join() #
#A.run(sock) # this one is ok, messages get it to be received
#staticmethod
def run(sock):
while True:
sock.send('demo'.encode('utf-8'))
print('sent')
time.sleep(1)
if __name__ =='__main__':
a = A()
client.py
import zmq
ctx=zmq.Context(1)
sock = zmq.Socket(ctx, zmq.SUB)
sock.connect('ipc://test')
sock.setsockopt_string(zmq.SUBSCRIBE, '')
while True:
print(sock.recv())
In the constructor of server.py, if I call .run()-method directly, the client can receive the message, but when I use the multiprocessing.Process()-method, it fails. Can anyone explain on this and provide some advice?
Q : "Why ZeroMQ fails to communicate when I use multiprocessing.Process to run?"
Well, ZeroMQ does not fail to communicate, the problem is, how Python multiprocessing module "operates".
The module is designed so that some processing may escape from the python central GIL-lock (re-[SERIAL]-iser, that is used as a forever present [CONCURRENT]-situations' principal avoider).
This means that the call to the multiprocessing.Process makes one exact "mirror-copy" of the python interpreter state, "exported" into new O/S-spawned process (details depend on localhost O/S).
Given that, there is zero chance a "mirror"-ed replica could get access to resources already owned by the __main__ - here the .bind()-method already acquired ipc://test address, so "remote"-process will never get "permission" to touch this ZeroMQ AccessPoint, unless the code gets repaired & fully re-factored.
Q : "Can anyone explain on this and provide some advice?"
Sure. The best step to start is to fully understand Pythonic culture of monopolistic GIL-lock re-[SERIAL]-isation, where no two things ever happen in the same time, so even adding more threads does not speed-up the flow of the processing, as it all gets re-aligned by the central "monopolist" The GIL-lock.
Next, understanding a promise of a fully reflected copy of the python interpreter state, while it sounds promising, also has some obvious drawbacks - the new processes, being "mirror"-copies cannot introduce colliding cases on already owned resources. If they try to, a not working as expected cases are the milder of the problems in such principally ill-designed cases.
In your code, the first row in __main__ instantiates a = A(), where A's .__init__ method straight occupies the IPC-resource since .bind('ipc://test'). The later step, p = Process( target = A.run, args = ( sock, ) ) "mirror"-replicates the state of the python interpreter (an as-is copy) and the p.start() cannot but crash into disability to "own" the same resource as the __main__ already owns (yes, the ipc://test for a "mirror"-ed process instructed call to grab the same, non-free resource in .bind('ipc://test') ). This will never fly.
Last but not least, enjoy the Zen-of-Zero, the masterpiece of Martin SUSTRIK for distributed-computing, so well crafted for ultimately scalable, almost zero-latency, very comfortable, widely ported signalling & messaging framework.
Short answer: Start your subprocesses. Create your zmq.Context- and .Socket-instances from within your Producer.run()-classmethod within each subprocess. Use .bind()-method on the side on which your cardinality is 1, and .connect()-method on the side where your cardinality is >1 (in this case, the "server").
My approach would be structured something like...
# server.py :
import zmq
from multiprocessing import Process
class Producer (Process):
def init(self):
...
def run(self):
ctx = zmq.Context(1)
sock = zmq.Socket(ctx, zmq.PUB)
# Multiple producers, so connect instead of bind (consumer must bind)
sock.connect('ipc://test')
while True:
...
if __name__ == "__main__":
producer = Producer()
p = Process(target=producer.run)
p.start()
p.join()
# client.py :
import zmq
ctx = zmq.Context(1)
sock = zmq.Socket(ctx, zmq.SUB)
# Capture from multiple producers, so bind (producers must connect)
sock.bind('ipc://test')
sock.setsockopt_string(zmq.SUBSCRIBE, '')
while True:
print(sock.recv())
I have experienced a very weird issue that I just can't explain when dealing with printing to a file from multiple processes (started with the subprocess module). The behavior I am seeing is that some of my output is slightly truncated and some of it is just completely missing. I am using a slightly modified version of Alex Martelli's solution for thread safe printing found here How do I get a thread safe print in Python 2.6?. The main difference is in the write method. To guarantee that output is not interleaved between the multiple processes writing to the same file I buffer the output and only write when I see a newline.
import sys
import threading
tls = threading.local()
class ThreadSafeFile(object):
"""
#author: Alex Martelli
#see: https://stackoverflow.com/questions/3029816/how-do-i-get-a-thread-safe-print-in-python-2-6
#summary: Allows for safe printing of output of multi-threaded programs to stdout.
"""
def __init__(self, f):
self.f = f
self.lock = threading.RLock()
self.nesting = 0
self.dataBuffer = ""
def _getlock(self):
self.lock.acquire()
self.nesting += 1
def _droplock(self):
nesting = self.nesting
self.nesting = 0
for i in range(nesting):
self.lock.release()
def __getattr__(self, name):
if name == 'softspace':
return tls.softspace
else:
raise AttributeError(name)
def __setattr__(self, name, value):
if name == 'softspace':
tls.softspace = value
else:
return object.__setattr__(self, name, value)
def write(self, data):
self._getlock()
self.dataBuffer += data
if data == '\n':
self.f.write(self.dataBuffer)
self.f.flush()
self.dataBuffer = ""
self._droplock()
def flush(self):
self.f.flush()
It should also be noted that to get this to behave abnormally it is going to require either a lot of time or a machine with multiple processors or cores. I ran the offending program in my test suite ~7000 times on a single processor machine before it reported a failure. This program that I've created to demonstrate the issue I've been experiencing in my test suite also seems to work on a single processor machine, but when you execute it on a multicore or multiprocessor machine it will certainly fail.
The following program shows the issue and it is somewhat more involved than I wanted it to be, but I wanted to preserve enough of the behavior of my programs as possible.
The code for process 1 main.py
import subprocess, sys, socket, time, random
from threadSafeFile import ThreadSafeFile
sys.stdout = ThreadSafeFile(sys.__stdout__)
usage = "python main.py nprocs niters"
workerFilename = "/path/to/worker.py"
def startMaster(n, iters):
host = socket.gethostname()
for i in xrange(n):
#set up ~synchronization between master and worker
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
sock.bind((host,0))
sock.listen(1)
socketPort = sock.getsockname()[1]
cmd = 'ssh %s python %s %s %d %d %d' % \
(host, workerFilename, host, socketPort, i, iters)
proc = subprocess.Popen(cmd.split(), shell=False, stdout=None, stderr=None)
conn, addr = sock.accept()
#wait for worker process to start
conn.recv(1024)
for j in xrange(iters):
#do very bursty i/o
for k in xrange(iters):
print "master: %d iter: %d message: %d" % (n,i, j)
#sleep for some amount of time between .02s and .5s
time.sleep(1 * (random.randint(1,50) / float(100)))
#wait for worker to finish
conn.recv(1024)
sock.close()
proc.kill()
def main(nprocs, niters):
startMaster(nprocs, niters)
if __name__ == "__main__":
if len(sys.argv) != 3:
print usage
sys.exit(1)
nprocs = int(sys.argv[1])
niters = int(sys.argv[2])
main(nprocs, niters)
code for process 2 worker.py
import sys, socket,time, random, time
from threadSafeFile import ThreadSafeFile
usage = "python host port id iters"
sys.stdout = ThreadSafeFile(sys.__stdout__)
def main(host, port, n, iters):
#tell master to start
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
sock.connect((host, port))
sock.send("begin")
for i in xrange(iters):
#do bursty i/o
for j in xrange(iters):
print "worker: %d iter: %d message: %d" % (n,i, j)
#sleep for some amount of time between .02s and .5s
time.sleep(1 * (random.randint(1,50) / float(100)))
#tell master we are done
sock.send("done")
sock.close()
if __name__ == "__main__":
if len(sys.argv) != 5:
print usage
sys.exit(1)
host = sys.argv[1]
port = int(sys.argv[2])
n = int(sys.argv[3])
iters = int(sys.argv[4])
main(host,port,n,iters)
When testing I ran main.py as follows:
python main.py 1 75 > main.out
The resulting file should be of length 75*75*2 = 11250 lines of the format:
(master|worker): %d iter: %d message: %d
Most of the time it is short 20-30 lines, but I have seen on occasion the program having the appropriate number of lines. After further investigation of the rare successes some of the lines are being truncated with something like:
ter: %d message: %d
Another interesting aspect to this is that when starting the ssh process using multiprocessing instead of subprocess this program behaves as intended. Some may just say why bother using subprocess when multiprocessing works fine. Unfortunately, it is the academic in me that really wants to know why this is behaving abnormally. Any thoughts and/or insights would be very appreciated. Thanks.
***edit
Ben I understand that threadSafeFile uses different locks per process, but I need it in my larger project for 2 reasons.
1) Each process may have multiple threads that will be writing to stdout even though this example does not. So I need to guarantee both safety at the thread level and at the process level.
2) If I don't make sure that when stdout gets flushed that there is a '\n' at the end of the buffer then there is going to be some potential execution trace where process 1 writes its buffer to a file without a trailing '\n' and then process 2 comes in and writes its buffer. Now we have lines interleaving and that's not what I want.
I also understand that this mechanism makes things a bit restrictive for what can be printed. Right now, in my stage of development of this project, restrictiveness is ok. When I can guarantee correctness I can start to relax the restrictions.
Your comment about locking inside of the conditional check if data == '\n' is incorrect. If the lock goes inside the conditional check then threadSafeFile is no longer thread safe in the general case. If any thread can add to the data buffer then there will be a race condition at dataBuffer += data as this is not an atomic operation. Perhaps your comment is simply related to this example in which we only have 1 thread per process, but if that's the case then we don't even need a lock at all.
In regards to OS level locks, my understanding was that multiple programs were able to safely write to the same file on a unix platform iff the number of bytes being written was smaller than the size of the internal buffer. Shouldn't the OS take care of all of the necessary locking for me in this case?
In each process you create a ThreadSafeFile for sys.stdout, each of which has a lock, but they're different locks; there's nothing connecting the locks used in all the different processes. So you're getting the same effect as if you used no locks at all; no process is ever going to be blocked by a lock held in another process, since they all have their own.
The only reason this works when run on a single processor machine is the buffering you do to queue up writes until a newline is encountered. This means that each line of output is written all in one go. On a uniprocessor, it's not unlikely that the OS will decide to switch processes in the middle of a bunch of successive calls to write, which would trash your data. But if the output is all written in chunks of a single line and you don't care about the order in which lines end up in the file, then it's very very unlikely for a context switch to happen in the middle of an operation you care about. Not theoretically impossible though, so I wouldn't call this code correct even for a uniprocessor.
ThreadSafeFile is very specifically only thread safe. It relies on the fact that the program only has a single ThreadSafeFile object for each file it's writing to. So any writes to that file are going to be going through that single object, synchronizing upon the lock.
When you have multiple processes, you don't have the shared global memory that threads in a single process do. So each process necessarily has its own separate ThreadSafeFile(sys.stdout) object. This is exactly the same mistake as if you had used threads and spawned N threads, each of which created its own ThreadSafeFile(sys.stdout).
I have no idea how this works when you use multiprocessing, because you haven't posted the code you used to do that. But my understanding is that this would still fail, for all the same reasons, if you used multiprocessing in such a way that each process created its own fresh ThreadSafeFile. Maybe you're not doing that in the version that uses multiprocessing?
What you need to do is arrange for the synchronization object (the lock) to be connected somehow. The multiprocessing module can do this for you. Note in the example here how the lock is created once and then passed in to each new process as it is created. (This still results in 10 different lock objects in 10 different processes of course, but what Python must be doing behind the scenes is creating an OS-level lock and then making each of the copied Python-level lock objects refer to the single OS-level lock).
If you want to do this with subprocessing, where you're just starting totally independent worker commands from separate scripts, then you'll need some way to get them all talking to a single OS-level lock. I don't know of anything in the standard library that helps you do that. I would just use multiprocessing.
As another thought, your buffering and locking code looks a little suspicious too. What happens if something calls sys.stdout.write("foo\n")? I'm not certain, but at a guess this is only working because the implementation of print happens to call sys.stdout.write on whatever you're printing, then call it again with a single newline. There is absolutely no reason it has to do this! It could just as easily assemble a single string of output in memory and then only call sys.stdout.write once. Plus, what happens if you need to print a block of multiple lines that need to go together in the output?
Another problem is that you acquire the lock the first time a process writes to the buffer, continue to hold it as the buffer is filled, then write the line, and finally release the lock. If your lock actually worked and a process took a long time between starting a line and finishing it it would block all other processes from even buffering up their writes! Maybe that's sort of what you want, if the intention that when a process starts writing something it gets a guarantee that its output will hit the file next. But in that case, you don't even need the buffering at all. I think you should be acquiring the lock just after if data == '\n':, and then you wouldn't need all that code tracking the nesting level either.
This is my 'game server'. It's nothing serious, I thought this was a nice way of learning a few things about python and sockets.
First the server class initialized the server.
Then, when someone connects, we create a client thread. In this thread we continually listen on our socket.
Once a certain command comes in (I12345001001, for example) it spawns another thread.
The purpose of this last thread is to send updates to the client.
But even though I see the server is performing this code, the data isn't actually being sent.
Could anyone tell where it's going wrong?
It's like I have to receive something before I'm able to send. So I guess somewhere I'm missing something
#!/usr/bin/env python
"""
An echo server that uses threads to handle multiple clients at a time.
Entering any line of input at the terminal will exit the server.
"""
import select
import socket
import sys
import threading
import time
import Queue
globuser = {}
queue = Queue.Queue()
class Server:
def __init__(self):
self.host = ''
self.port = 2000
self.backlog = 5
self.size = 1024
self.server = None
self.threads = []
def open_socket(self):
try:
self.server = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
self.server.bind((self.host,self.port))
self.server.listen(5)
except socket.error, (value,message):
if self.server:
self.server.close()
print "Could not open socket: " + message
sys.exit(1)
def run(self):
self.open_socket()
input = [self.server,sys.stdin]
running = 1
while running:
inputready,outputready,exceptready = select.select(input,[],[])
for s in inputready:
if s == self.server:
# handle the server socket
c = Client(self.server.accept(), queue)
c.start()
self.threads.append(c)
elif s == sys.stdin:
# handle standard input
junk = sys.stdin.readline()
running = 0
# close all threads
self.server.close()
for c in self.threads:
c.join()
class Client(threading.Thread):
initialized=0
def __init__(self,(client,address), queue):
threading.Thread.__init__(self)
self.client = client
self.address = address
self.size = 1024
self.queue = queue
print 'Client thread created!'
def run(self):
running = 10
isdata2=0
receivedonce=0
while running > 0:
if receivedonce == 0:
print 'Wait for initialisation message'
data = self.client.recv(self.size)
receivedonce = 1
if self.queue.empty():
print 'Queue is empty'
else:
print 'Queue has information'
data2 = self.queue.get(1, 1)
isdata2 = 1
if data2 == 'Exit':
running = 0
print 'Client is being closed'
self.client.close()
if data:
print 'Data received through socket! First char: "' + data[0] + '"'
if data[0] == 'I':
print 'Initializing user'
user = {'uid': data[1:6] ,'x': data[6:9], 'y': data[9:12]}
globuser[user['uid']] = user
print globuser
initialized=1
self.client.send('Beginning - Initialized'+';')
m=updateClient(user['uid'], queue)
m.start()
else:
print 'Reset receivedonce'
receivedonce = 0
print 'Sending client data'
self.client.send('Feedback: ' +data+';')
print 'Client Data sent: ' + data
data=None
if isdata2 == 1:
print 'Data2 received: ' + data2
self.client.sendall(data2)
self.queue.task_done()
isdata2 = 0
time.sleep(1)
running = running - 1
print 'Client has stopped'
class updateClient(threading.Thread):
def __init__(self,uid, queue):
threading.Thread.__init__(self)
self.uid = uid
self.queue = queue
global globuser
print 'updateClient thread started!'
def run(self):
running = 20
test=0
while running > 0:
test = test + 1
self.queue.put('Test Queue Data #' + str(test))
running = running - 1
time.sleep(1)
print 'Updateclient has stopped'
if __name__ == "__main__":
s = Server()
s.run()
I don't understand your logic -- in particular, why you deliberately set up two threads writing at the same time on the same socket (which they both call self.client), without any synchronization or coordination, an arrangement that seems guaranteed to cause problems.
Anyway, a definite bug in your code is you use of the send method -- you appear to believe that it guarantees to send all of its argument string, but that's very definitely not the case, see the docs:
Returns the number of bytes sent.
Applications are responsible for
checking that all data has been sent;
if only some of the data was
transmitted, the application needs to
attempt delivery of the remaining
data.
sendall is the method that you probably want:
Unlike send(), this method continues
to send data from string until either
all data has been sent or an error
occurs.
Other problems include the fact that updateClient is apparently designed to never terminate (differently from the other two thread classes -- when those terminate, updateClient instances won't, and they'll just keep running and keep the process alive), redundant global statements (innocuous, just confusing), some threads trying to read a dict (via the iteritems method) while other threads are changing it, again without any locking or coordination, etc, etc -- I'm sure there may be even more bugs or problems, but, after spotting several, one's eyes tend to start to glaze over;-).
You have three major problems. The first problem is likely the answer to your question.
Blocking (Question Problem)
The socket.recv is blocking. This means that execution is halted and the thread goes to sleep until it can read data from the socket. So your third update thread just fills the queue up but it only gets emptied when you get a message. The queue is also emptied by one message at a time.
This is likely why it will not send data unless you send it data.
Message Protocol On Stream Protocol
You are trying to use the socket stream like a message stream. What I mean is you have:
self.server = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
The SOCK_STREAM part says it is a stream not a message such as SOCK_DGRAM. However, TCP does not support message. So what you have to do is build messages such as:
data =struct.pack('I', len(msg)) + msg
socket.sendall(data)
Then the receiving end will looking for the length field and read the data into a buffer. Once enough data is in the buffer it can grab out the entire message.
Your current setup is working because your messages are small enough to all be placed into the same packet and also placed into the socket buffer together. However, once you start sending large data over multiple calls with socket.send or socket.sendall you are going to start having multiple messages and partial messages being read unless you implement a message protocol on top of the socket byte stream.
Threads
Even though threads can be easier to use when starting out they come with a lot of problems and can degrade performance if used incorrectly especially in Python. I love threads so do not get me wrong. Python also has a problem with the GIL (global interpreter lock) so you get bad performance when using threads that are CPU bound. Your code is mostly I/O bound at the moment, but in the future it may become CPU bound. Also you have to worry about locking with threading. A thread can be a quick fix but may not be the best fix. There are circumstances where threading is quite simply the easiest way to break some time consuming process. So do not discard threads as evil or terrible. In Python they are considered bad mainly because of the GIL, and in other languages (including Python) because of concurrency issues so most people recommend you to use multiple processes with Python or use asynchronous code. The subject of to use a thread or not is very complex as it depends on the language (way your code is run), the system (single or multiple processors), and contention (trying to share a resource with locking), and other factors, but generally asynchronous code is faster because it utilizes more CPU with less overhead especially if you are not CPU bound.
The solution is the usage of the select module in Python, or something similar. It will tell you when a socket has data to be read, and you can set a timeout parameter.
You can gain more performance by doing asynchronous work (asynchronous sockets). To turn a socket into asynchronous mode you simply call socket.settimeout(0) which will make it not block. However, you will constantly eat CPU spinning waiting for data. The select module and friends will prevent you from spinning.
Generally for performance you want to do as much asynchronous (same thread) as possible, and then expand with more threads that are also doing as much asynchronously as possible. However as previously noted Python is an exception to this rule because of the GIL (global interpreter lock) which can actually degrade performance from what I have read. If you are interesting you should try writing a test case and find out!
You should also check out the thread locking primitives in the threading module. They are Lock, RLock, and Condition. They can help multiple threads share data with out problems.
lock = threading.Lock()
def myfunction(arg):
with lock:
arg.do_something()
Some Python objects are thread safe and others are not.
Sending Updates Asynchronously (Improvement)
Instead of using a third thread only to send updates you could instead use the client thread to send updates by checking the current time with the last time an update was sent. This would remove the usage of a Queue and a Thread. Also to do this you must convert your client code into asynchronous code and have a timeout on your select so that you can at interval check the current time to see if an update is needed.
Summary
I would recommend you rewrite your code using asynchronous socket code. I would also recommend that you use a single thread for all clients and the server. This will improve performance and decrease latency. It would make debugging easier because you would have no possible concurrency issues like you have with threads. Also, fix your message protocol before it fails.