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Python can't start new thread multiprocessing

I'm trying to use a cluster of computers to run millions of small simulations. To do this I tried to set up two "servers" on my main computer, one to add input variables in a queue to the network and one to take care of the result.
This is the code for putting stuff into the simulation variables queue:
"""This script reads start parameters and calls on run_sim to run the
simulations"""
import time
from multiprocessing import Process, freeze_support, Manager, Value, Queue, current_process
from multiprocessing.managers import BaseManager
class QueueManager(BaseManager):
pass
class MultiComputers(Process):
def __init__(self, sim_name, queue):
self.sim_name = sim_name
self.queue = queue
super(MultiComputers, self).__init__()
def get_sim_obj(self, offset, db):
"""returns a list of lists from a database query"""
def handle_queue(self):
self.sim_nr = 0
sims = self.get_sim_obj()
self.total = len(sims)
while len(sims) > 0:
if self.queue.qsize() > 100:
self.queue.put(sims[0])
self.sim_nr += 1
print(self.sim_nr, round(self.sim_nr/self.total * 100, 2), self.queue.qsize())
del sims[0]
def run(self):
self.handle_queue()
if __name__ == '__main__':
freeze_support()
queue = Queue()
w = MultiComputers('seed_1_hundred', queue)
w.start()
QueueManager.register('get_queue', callable=lambda: queue)
m = QueueManager(address=('', 8001), authkey=b'abracadabra')
s = m.get_server()
s.serve_forever()
And then is this queue run to take care of the results of the simulations:
__author__ = 'axa'
from multiprocessing import Process, freeze_support, Queue
from multiprocessing.managers import BaseManager
import time
class QueueManager(BaseManager):
pass
class SaveFromMultiComp(Process):
def __init__(self, sim_name, queue):
self.sim_name = sim_name
self.queue = queue
super(SaveFromMultiComp, self).__init__()
def run(self):
res_got = 0
with open('sim_type1_' + self.sim_name, 'a') as f_1:
with open('sim_type2_' + self.sim_name, 'a') as f_2:
while True:
if self.queue.qsize() > 0:
while self.queue.qsize() > 0:
res = self.queue.get()
res_got += 1
if res[0] == 1:
f_1.write(str(res[1]) + '\n')
elif res[0] == 2:
f_2.write(str(res[1]) + '\n')
print(res_got)
time.sleep(0.5)
if __name__ == '__main__':
queue = Queue()
w = SaveFromMultiComp('seed_1_hundred', queue)
w.start()
m = QueueManager(address=('', 8002), authkey=b'abracadabra')
s = m.get_server()
s.serve_forever()
These scripts works as expected for handling the first ~7-800 simulations, after that I get the following error in the terminal running the receiving result script:
Exception in thread Thread-1:
Traceback (most recent call last):
File "C:\Python35\lib\threading.py", line 914, in _bootstrap_inner
self.run()
File "C:\Python35\lib\threading.py", line 862, in run
self._target(*self._args, **self._kwargs)
File "C:\Python35\lib\multiprocessing\managers.py", line 177, in accepter
t.start()
File "C:\Python35\lib\threading.py", line 844, in start
_start_new_thread(self._bootstrap, ())
RuntimeError: can't start new thread
Can anyone give som insights in where and how the threads are spawned, is a new thread spawned every time I call queue.get() or how does it work?
And I would be very glad if someone knows what I can do to avoid this failure? (i'm running the script with Python3.5-32)

All signs point to your system being out of resources it needs to launch a thread (probably memory, but you could be leaking threads or other resources). You could use OS system monitoring tools (top for Linux, Resource Monitor for windows) to look at the number of threads and memory usage to track this down, but I would recommend you just use an easier, more efficient programming pattern.
While not a perfect comparison, you generally are seeing the C10K problem and it states that blocking threads waiting for results do not scale well and can be prone to leaking errors like this. The solution was to implement Async IO patterns (one blocking thread that launches other workers) and this is pretty straight forward to do in Web Servers.
A framework like pythons aiohttp should be a good fit for what you want. You just need a handler that can get the ID of the remote code and the result. The framework should hopefully take care of the scaling for you.
So in your case you can keep your launching code, but after it starts the process on the remote machine, kill the thread. Have the remote code then send an HTTP message to your server with 1) its ID and 2) its result. Throw in a little extra code to ask it to try again if it does not get a 200 'OK' Status code and you should be in much better shape.

I think you have to many Threads running for your system. I would first check your system ressources and then rethink my Program.
Try limiting your threads and use as few as possible.

Does python logging support multiprocessing?

I have been told that logging can not be used in Multiprocessing. You have to do the concurrency control in case multiprocessing messes the log.
But I did some test, it seems like there is no problem using logging in multiprocessing
import time
import logging
from multiprocessing import Process, current_process, pool
# setup log
logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)s %(filename)s[line:%(lineno)d] %(levelname)s %(message)s',
datefmt='%a, %d %b %Y %H:%M:%S',
filename='/tmp/test.log',
filemode='w')
def func(the_time, logger):
proc = current_process()
while True:
if time.time() >= the_time:
logger.info('proc name %s id %s' % (proc.name, proc.pid))
return
if __name__ == '__main__':
the_time = time.time() + 5
for x in xrange(1, 10):
proc = Process(target=func, name=x, args=(the_time, logger))
proc.start()
As you can see from the code.
I deliberately let the subprocess write log at the same moment( 5s after start) to increase the chance of conflict. But there are no conflict at all.
So my question is can we use logging in multiprocessing?
Why so many posts say we can not ?

As Matino correctly explained: logging in a multiprocessing setup is not safe, as multiple processes (who do not know anything about the other ones existing) are writing into the same file, potentially intervening with each other.
Now what happens is that every process holds an open file handle and does an "append write" into that file. The question is under what circumstances the append write is "atomic" (that is, cannot be interrupted by e.g. another process writing to the same file and intermingling his output). This problem applies to every programming language, as in the end they'll do a syscall to the kernel. This answer answers under which circumstances a shared log file is ok.
It comes down to checking your pipe buffer size, on linux that is defined in /usr/include/linux/limits.h and is 4096 bytes. For other OSes you find here a good list.
That means: If your log line is less than 4'096 bytes (if on Linux), then the append is safe, if the disk is directly attached (i.e. no network in between). But for more details please check the first link in my answer. To test this you can do logger.info('proc name %s id %s %s' % (proc.name, proc.pid, str(proc.name)*5000)) with different lenghts. With 5000 for instance I got already mixed up log lines in /tmp/test.log.
In this question there are already quite a few solutions to this, so I won't add my own solution here.
Update: Flask and multiprocessing
Web frameworks like flask will be run in multiple workers if hosted by uwsgi or nginx. In that case, multiple processes may write into one log file. Will it have problems?
The error handling in flask is done via stdout/stderr which is then cought by the webserver (uwsgi, nginx, etc.) which needs to take care that logs are written in correct fashion (see e.g. this flask+nginx example), probably also adding process information so you can associate error lines to processes. From flasks doc:
By default as of Flask 0.11, errors are logged to your webserver’s log automatically. Warnings however are not.
So you'd still have this issue of intermingled log files if you use warn and the message exceeds the pipe buffer size.

It is not safe to write to a single file from multiple processes.
According to https://docs.python.org/3/howto/logging-cookbook.html#logging-to-a-single-file-from-multiple-processes
Although logging is thread-safe, and logging to a single file from
multiple threads in a single process is supported, logging to a single
file from multiple processes is not supported, because there is no
standard way to serialize access to a single file across multiple
processes in Python.
One possible solution would be to have each process write to its own file. You can achieve this by writing your own handler that adds process pid to the end of the file:
import logging.handlers
import os
class PIDFileHandler(logging.handlers.WatchedFileHandler):
def __init__(self, filename, mode='a', encoding=None, delay=0):
filename = self._append_pid_to_filename(filename)
super(PIDFileHandler, self).__init__(filename, mode, encoding, delay)
def _append_pid_to_filename(self, filename):
pid = os.getpid()
path, extension = os.path.splitext(filename)
return '{0}-{1}{2}'.format(path, pid, extension)
Then you just need to call addHandler:
logger = logging.getLogger('foo')
fh = PIDFileHandler('bar.log')
logger.addHandler(fh)

Use a queue for correct handling of concurrency simultaneously recovering from errors by feeding everything to the parent process via a pipe.
from logging.handlers import RotatingFileHandler
import multiprocessing, threading, logging, sys, traceback
class MultiProcessingLog(logging.Handler):
def __init__(self, name, mode, maxsize, rotate):
logging.Handler.__init__(self)
self._handler = RotatingFileHandler(name, mode, maxsize, rotate)
self.queue = multiprocessing.Queue(-1)
t = threading.Thread(target=self.receive)
t.daemon = True
t.start()
def setFormatter(self, fmt):
logging.Handler.setFormatter(self, fmt)
self._handler.setFormatter(fmt)
def receive(self):
while True:
try:
record = self.queue.get()
self._handler.emit(record)
except (KeyboardInterrupt, SystemExit):
raise
except EOFError:
break
except:
traceback.print_exc(file=sys.stderr)
def send(self, s):
self.queue.put_nowait(s)
def _format_record(self, record):
# ensure that exc_info and args
# have been stringified. Removes any chance of
# unpickleable things inside and possibly reduces
# message size sent over the pipe
if record.args:
record.msg = record.msg % record.args
record.args = None
if record.exc_info:
dummy = self.format(record)
record.exc_info = None
return record
def emit(self, record):
try:
s = self._format_record(record)
self.send(s)
except (KeyboardInterrupt, SystemExit):
raise
except:
self.handleError(record)
def close(self):
self._handler.close()
logging.Handler.close(self)
The handler does all the file writing from the parent process and uses just one thread to receive messages passed from child processes

QueueHandler is native in Python 3.2+, and safely handles multiprocessing logging.
Python docs have two complete examples: Logging to a single file from multiple processes
For those using Python < 3.2, just copy QueueHandler into your own code from: https://gist.github.com/vsajip/591589 or alternatively import logutils.
Each process (including the parent process) puts its logging on the Queue, and then a listener thread or process (one example is provided for each) picks those up and writes them all to a file - no risk of corruption or garbling.
Note: this question is basically a duplicate of How should I log while using multiprocessing in Python? so I've copied my answer from that question as I'm pretty sure it's currently the best solution.

Python - Non-empty shared list on separate thread appears empty

I've two classes - MessageProducer and MessageConsumer.
MessageConsumer does the following:
receives messages and puts them in its message list "_unprocessed_msgs"
on a separate worker thread, moves the messages to internal list "_in_process_msgs"
on the worker thread, processes messages from "_in_process_msgs"
On my development environment, I'm facing issue with #2 above - after adding a message by performing step#1, when worker thread checks length of "_unprocessed_msgs", it gets it as zero.
When step #1 is repeated, the list properly shows 2 items on the thread on which the item was added. But in step #2, on worker thread, again the len(_unprocessed_msgs) returns zero.
Not sure why this is happening. Would really appreciate help any help on this.
I'm using Ubuntu 16.04 having Python 2.7.12.
Below is the sample source code. Please let me know if more information is required.
import threading
import time
class MessageConsumerThread(threading.Thread):
def __init__(self):
super(MessageConsumerThread, self).__init__()
self._unprocessed_msg_q = []
self._in_process_msg_q = []
self._lock = threading.Lock()
self._stop_processing = False
def start_msg_processing_thread(self):
self._stop_processing = False
self.start()
def stop_msg_processing_thread(self):
self._stop_processing = True
def receive_msg(self, msg):
with self._lock:
LOG.info("Before: MessageConsumerThread::receive_msg: "
"len(self._unprocessed_msg_q)=%s" %
len(self._unprocessed_msg_q))
self._unprocessed_msg_q.append(msg)
LOG.info("After: MessageConsumerThread::receive_msg: "
"len(self._unprocessed_msg_q)=%s" %
len(self._unprocessed_msg_q))
def _queue_unprocessed_msgs(self):
with self._lock:
LOG.info("MessageConsumerThread::_queue_unprocessed_msgs: "
"len(self._unprocessed_msg_q)=%s" %
len(self._unprocessed_msg_q))
if self._unprocessed_msg_q:
LOG.info("Moving messages from unprocessed to in_process queue")
self._in_process_msg_q += self._unprocessed_msg_q
self._unprocessed_msg_q = []
LOG.info("Moved messages from unprocessed to in_process queue")
def run(self):
while not self._stop_processing:
# Allow other threads to add messages to message queue
time.sleep(1)
# Move unprocessed listeners to in-process listener queue
self._queue_unprocessed_msgs()
# If nothing to process continue the loop
if not self._in_process_msg_q:
continue
for msg in self._in_process_msg_q:
self.consume_message(msg)
# Clean up processed messages
del self._in_process_msg_q[:]
def consume_message(self, msg):
print(msg)
class MessageProducerThread(threading.Thread):
def __init__(self, producer_id, msg_receiver):
super(MessageProducerThread, self).__init__()
self._producer_id = producer_id
self._msg_receiver = msg_receiver
def start_producing_msgs(self):
self.start()
def run(self):
for i in range(1,10):
msg = "From: %s; Message:%s" %(self._producer_id, i)
self._msg_receiver.receive_msg(msg)
def main():
msg_receiver_thread = MessageConsumerThread()
msg_receiver_thread.start_msg_processing_thread()
msg_producer_thread = MessageProducerThread(producer_id='Producer-01',
msg_receiver=msg_receiver_thread)
msg_producer_thread.start_producing_msgs()
msg_producer_thread.join()
msg_receiver_thread.stop_msg_processing_thread()
msg_receiver_thread.join()
if __name__ == '__main__':
main()
Following is the log the I get:
INFO: MessageConsumerThread::_queue_unprocessed_msgs: len(self._unprocessed_msg_q)=0
INFO: Before: MessageConsumerThread::receive_msg: len(self._unprocessed_msg_q)=0
INFO: After: MessageConsumerThread::receive_msg: **len(self._unprocessed_msg_q)=1**
INFO: MessageConsumerThread::_queue_unprocessed_msgs: **len(self._unprocessed_msg_q)=0**
INFO: MessageConsumerThread::_queue_unprocessed_msgs: len(self._unprocessed_msg_q)=0
INFO: Before: MessageConsumerThread::receive_msg: len(self._unprocessed_msg_q)=1
INFO: After: MessageConsumerThread::receive_msg: **len(self._unprocessed_msg_q)=2**
INFO: MessageConsumerThread::_queue_unprocessed_msgs: **len(self._unprocessed_msg_q)=0**

This is not a good desing for you application.
I spent some time trying to debug this - but threading code is naturally complicated, so we should try to descomplicate it, instead of getting it even more confure.
When I see threading code in Python, I usually see it written a in a procedural form: a normal function that is passed to threading.Thread as the target argument that drives each thread. That way, you don't need to write code for a new class that will have a single instance.
Another thing is that, although Python's global interpreter lock itself guarantees lists won't get corrupted if modified in two separate threads, lists are not a recomended "thread data passing" data structure. You probably should look at threading.Queue to do that
The thing is wrong in this code at first sight is probably not the cause of your problem due to your use of locks, but it might be. Instead of
self._unprocessed_msg_q = []
which will create a new list object, the other thread have momentarily no reference too (so it might write data to the old list), you should do:
self._unprocessed_msg_q[:] = []
Or just the del slice thing you do on the other method.
But to be on the safer side, and having mode maintanable and less surprising code, you really should change to a procedural approach there, assuming Python threading. Assume "Thread" is the "final" object that can do its thing, and then use Queues around:
# coding: utf-8
from __future__ import print_function
from __future__ import unicode_literals
from threading import Thread
try:
from queue import Queue, Empty
except ImportError:
from Queue import Queue, Empty
import time
import random
TERMINATE_SENTINEL = object()
NO_DATA_SENTINEL = object()
class Receiver(object):
def __init__(self, queue):
self.queue = queue
self.in_process = []
def receive_data(self, data):
self.in_process.append(data)
def consume_data(self):
print("received data:", self.in_process)
del self.in_process[:]
def receiver_loop(self):
queue = self.queue
while True:
try:
data = queue.get(block=False)
except Empty:
print("got no data from queue")
data = NO_DATA_SENTINEL
if data is TERMINATE_SENTINEL:
print("Got sentinel: exiting receiver loop")
break
self.receive_data(data)
time.sleep(random.uniform(0, 0.3))
if queue.empty():
# Only process data if we have nothing to receive right now:
self.consume_data()
print("sleeping receiver")
time.sleep(1)
if self.in_process:
self.consume_data()
def producer_loop(queue):
for i in range(10):
time.sleep(random.uniform(0.05, 0.4))
print("putting {0} in queue".format(i))
queue.put(i)
def main():
msg_queue = Queue()
msg_receiver_thread = Thread(target=Receiver(msg_queue).receiver_loop)
time.sleep(0.1)
msg_producer_thread = Thread(target=producer_loop, args=(msg_queue,))
msg_receiver_thread.start()
msg_producer_thread.start()
msg_producer_thread.join()
msg_queue.put(TERMINATE_SENTINEL)
msg_receiver_thread.join()
if __name__ == '__main__':
main()
note that since you want multiple methods in the recever thread to do things with data, I used a class - but it does not inherit from Thread, and does not have to worry about its workings. All its methods are called within the same thread: no need of locks, no worries about race conditions within the receiver class itself. For communicating outside the class, the Queue class is structured to handle any race conditions for us.
The producer loop, as it is just a dummy producer, has no need at all to be written in class form. But it would look just the same, if it had more methods.
(The random sleeps help visualize what would happen in "real world" message receiving)
Also, you might want to take a look at something like:
https://www.thoughtworks.com/insights/blog/composition-vs-inheritance-how-choose

Finally I was able to solve the issue. In the actual code, I've a Manager class that is responsible for instantiating MessageConsumerThread as its last thing in the initializer:
class Manager(object):
def __init__(self):
...
...
self._consumer = MessageConsumerThread(self)
self._consumer.start_msg_processing_thread()
The problem seems to be with passing 'self' in MessageConsumerThread initializer when Manager is still executing its initializer (eventhough those are last two steps). The moment I moved the creation of consumer out of initializer, consumer thread was able to see the elements in "_unprocessed_msg_q".
Please note that the issue is still not reproducible with the above sample code. It is manifesting itself in the production environment only. Without the above fix, I tried queue and dictionary as well but observed the same issue. After the fix, tried with queue and list and was able to successfully execute the code.
I really appreciate and thank #jsbueno and #ivan_pozdeev for their time and help! Community #stackoverflow is very helpful!

Run a script with web server and maintain data to be used later

Note: I want to implement this without using any framework.
I have to create an web application using python. The application should maintain a running average of the CPU usage for each process over the past 60 seconds. It should should act as a web server and when it gets a request, it should return the current average for each process. Following are the scripts I've written. record_usage.py is a script which I want to run as soon as the server.py is run. So that it runs and maintain the cpu usage data, which I intend to read whenever I get an XHR request and send it back to the client.
So, my problem is how do I invoke this requirement? I tried running record_usage.py using subprocess.POPEN after starting the server. record_usage.py starts running in background as well. But when I try accessing the data created by it, the class object I create is not the one it uses but a new one. How to complete this link?
Kindly ask things that I could not make clear.
Latest changes in server.py
if __name__ == '__main__':
RU_OBJ = RU(settings.SAMPLING_FREQ, settings.AVG_INTERVAL)
RU_LOCK = RLock()
# Record CPU usage in a thread.
ru_thread = Thread(target=RU_OBJ.record, args=(RU_LOCK,))
ru_thread.daemon = True
ru_thread.start()
# Run server.
run()
Latest change in record_usage.py
def record(self, lock):
while True:
with lock:
self.add_processes()
time.sleep(self.sampling_freq)
Is this a proper way of applying locks? A similar lock is being applied when am reading the processes information. Would it work?
Added the functions:
def add_processes(self,):
for _process in psutil.process_iter():
try:
new_proc = _process.as_dict(attrs=['cpu_times', 'name', 'pid',
'status'])
except psutil.NoSuchProcess:
continue
pid, (user, _sys) = new_proc['pid'], new_proc.pop('cpu_times')
# Get or create details object for the process.
existing = self.processes.setdefault(pid, new_proc)
# Get or create queue object for the CPU times of the process.
queue_dict = self.process_queue.setdefault(pid, dict())
# User CPU time.
user_q = queue_dict.setdefault('user_q', PekableQueue(self.avg_interval))
user_q.enqueue(user)
user_avg = get_avg(user_q)
# System CPU time.
sys_q = queue_dict.setdefault('sys_q', PekableQueue(self.avg_interval))
sys_q.enqueue(_sys)
sys_avg = get_avg(sys_q)
# Update the details object for the process.
existing.update(user_avg=user_avg, sys_avg=sys_avg, **new_proc)
def get_curr_processes(self):
return [self.processes[pid] for pid in psutil.get_pid_list()
if pid in self.processes]

To collect statistics in another thread:
if __name__ == '__main__':
from threading import Thread, Lock
import record_usage
lock = Lock()
t = Thread(target=record_usage.record, args=[lock])
t.daemon = True
t.start()
run(lock)
If you change some shared data in one thread and read it in another then you could protect the places where you access/change the value with a lock:
#...
with self.lock:
existing = self.processes.setdefault(pid, new_proc)
#...
with self.lock:
existing.update(user_avg=user_avg, sys_avg=sys_avg, **new_proc)
#...
def get_curr_processes(self):
with self.lock:
return [self.processes[pid] for pid in psutil.get_pid_list()
if pid in self.processes]
It is essential that self.lock is the same object in all threads. If self.processes is a dict then you don't need to use a lock in CPython. The methods are implemented in C and the interpreter doesn't release GIL (global lock) while calling them i.e., only one thread at a time accesses the dict.

Errno 9 using the multiprocessing module with Tornado in Python

For operations in my Tornado server that are expected to block (and can't be easily modified to use things like Tornado's asynchronous HTTP request client), I have been offloading the work to separate worker processes using the multiprocessing module. Specifically, I was using a multiprocessing Pool because it offers a method called apply_async, which works very well with Tornado since it takes a callback as one of its arguments.
I recently realized that a pool preallocates the number of processes, so if they all become blocking, operations that require a new process will have to wait. I do realize that the server can still take connections since apply_async works by adding things to a task queue, and is rather immediately finished, itself, but I'm looking to spawn n processes for n amount of blocking tasks I need to perform.
I figured that I could use the add_handler method for my Tornado server's IOLoop to add a handler for each new PID that I create to that IOLoop. I've done something similar before, but it was using popen and an arbitrary command. An example of such use of this method is here. I wanted to pass arguments into an arbitrary target Python function within my scope, though, so I wanted to stick with multiprocessing.
However, it seems that something doesn't like the PIDs that my multiprocessing.Process objects have. I get IOError: [Errno 9] Bad file descriptor. Are these processes restricted somehow? I know that the PID isn't available until I actually start the process, but I do start the process. Here's the source code of an example I've made that demonstrates this issue:
#!/usr/bin/env python
"""Creates a small Tornado program to demonstrate asynchronous programming.
Specifically, this demonstrates using the multiprocessing module."""
import tornado.httpserver
import tornado.ioloop
import tornado.web
import multiprocessing as mp
import random
import time
__author__ = 'Brian McFadden'
__email__ = 'brimcfadden#gmail.com'
def sleepy(queue):
"""Pushes a string to the queue after sleeping for 5 seconds.
This sleeping can be thought of as a blocking operation."""
time.sleep(5)
queue.put("Now I'm awake.")
return
def random_num():
"""Returns a string containing a random number.
This function can be used by handlers to receive text for writing which
facilitates noticing change on the webpage when it is refreshed."""
n = random.random()
return "<br />Here is a random number to show change: {0}".format(n)
class SyncHandler(tornado.web.RequestHandler):
"""Demonstrates handing a request synchronously.
It executes sleepy() before writing some more text and a random number to
the webpage. While the process is sleeping, the Tornado server cannot
handle any requests at all."""
def get(self):
q = mp.Queue()
sleepy(q)
val = q.get()
self.write(val)
self.write('<br />Brought to you by SyncHandler.')
self.write('<br />Try refreshing me and then the main page.')
self.write(random_num())
class AsyncHandler(tornado.web.RequestHandler):
"""Demonstrates handing a request asynchronously.
It executes sleepy() before writing some more text and a random number to
the webpage. It passes the sleeping function off to another process using
the multiprocessing module in order to handle more requests concurrently to
the sleeping, which is like a blocking operation."""
#tornado.web.asynchronous
def get(self):
"""Handles the original GET request (normal function delegation).
Instead of directly invoking sleepy(), it passes a reference to the
function to the multiprocessing pool."""
# Create an interprocess data structure, a queue.
q = mp.Queue()
# Create a process for the sleepy function. Provide the queue.
p = mp.Process(target=sleepy, args=(q,))
# Start it, but don't use p.join(); that would block us.
p.start()
# Add our callback function to the IOLoop. The async_callback wrapper
# makes sure that Tornado sends an HTTP 500 error to the client if an
# uncaught exception occurs in the callback.
iol = tornado.ioloop.IOLoop.instance()
print "p.pid:", p.pid
iol.add_handler(p.pid, self.async_callback(self._finish, q), iol.READ)
def _finish(self, q):
"""This is the callback for post-sleepy() request handling.
Operation of this function occurs in the original process."""
val = q.get()
self.write(val)
self.write('<br />Brought to you by AsyncHandler.')
self.write('<br />Try refreshing me and then the main page.')
self.write(random_num())
# Asynchronous handling must be manually finished.
self.finish()
class MainHandler(tornado.web.RequestHandler):
"""Returns a string and a random number.
Try to access this page in one window immediately after (<5 seconds of)
accessing /async or /sync in another window to see the difference between
them. Asynchronously performing the sleepy() function won't make the client
wait for data from this handler, but synchronously doing so will!"""
def get(self):
self.write('This is just responding to a simple request.')
self.write('<br />Try refreshing me after one of the other pages.')
self.write(random_num())
if __name__ == '__main__':
# Create an application using the above handlers.
application = tornado.web.Application([
(r"/", MainHandler),
(r"/sync", SyncHandler),
(r"/async", AsyncHandler),
])
# Create a single-process Tornado server from the application.
http_server = tornado.httpserver.HTTPServer(application)
http_server.listen(8888)
print 'The HTTP server is listening on port 8888.'
tornado.ioloop.IOLoop.instance().start()
Here is the traceback:
Traceback (most recent call last):
File "/usr/local/lib/python2.6/dist-packages/tornado/web.py", line 810, in _stack_context
yield
File "/usr/local/lib/python2.6/dist-packages/tornado/stack_context.py", line 77, in StackContext
yield
File "/usr/local/lib/python2.6/dist-packages/tornado/web.py", line 827, in _execute
getattr(self, self.request.method.lower())(*args, **kwargs)
File "/usr/local/lib/python2.6/dist-packages/tornado/web.py", line 909, in wrapper
return method(self, *args, **kwargs)
File "./process_async.py", line 73, in get
iol.add_handler(p.pid, self.async_callback(self._finish, q), iol.READ)
File "/usr/local/lib/python2.6/dist-packages/tornado/ioloop.py", line 151, in add_handler
self._impl.register(fd, events | self.ERROR)
IOError: [Errno 9] Bad file descriptor
The above code is actually modified from an older example that used process pools. I've had it saved for reference for my coworkers and myself (hence the heavy amount of comments) for quite a while. I constructed it in such a way so that I could open two small browser windows side-by-side to demonstrate to my boss that the /sync URI blocks connections while /async allows more connections. For the purposes of this question, all you need to do to reproduce it is try to access the /async handler. It errors immediately.
What should I do about this? How can the PID be "bad"? If you run the program, you can see it be printed to stdout.
For the record, I'm using Python 2.6.5 on Ubuntu 10.04. Tornado is 1.1.

add_handler takes a valid file descriptor, not a PID. As an example of what's expected, tornado itself uses add_handler normally by passing in a socket object's fileno(), which returns the object's file descriptor. PID is irrelevant in this case.

Check out this project:
https://github.com/vukasin/tornado-subprocess
it allows you to start arbitrary processes from tornado and get a callback when they finish (with access to their status, stdout and stderr).