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Is this multi-threaded function asynchronous

I'm afraid I'm still a bit confused (despite checking other threads) whether:
all asynchronous code is multi-threaded
all multi-threaded functions are asynchronous
My initial guess is no to both and that proper asynchronous code should be able to run in one thread - however it can be improved by adding threads for example like so:
So I constructed this toy example:
from threading import *
from queue import Queue
import time
def do_something_with_io_lag(in_work):
out = in_work
# Imagine we do some work that involves sending
# something over the internet and processing the output
# once it arrives
time.sleep(0.5) # simulate IO lag
print("Hello, bee number: ",
str(current_thread().name).replace("Thread-",""))
class WorkerBee(Thread):
def __init__(self, q):
Thread.__init__(self)
self.q = q
def run(self):
while True:
# Get some work from the queue
work_todo = self.q.get()
# This function will simiulate I/O lag
do_something_with_io_lag(work_todo)
# Remove task from the queue
self.q.task_done()
if __name__ == '__main__':
def time_me(nmbr):
number_of_worker_bees = nmbr
worktodo = ['some input for work'] * 50
# Create a queue
q = Queue()
# Fill with work
[q.put(onework) for onework in worktodo]
# Launch processes
for _ in range(number_of_worker_bees):
t = WorkerBee(q)
t.start()
# Block until queue is empty
q.join()
# Run this code in serial mode (just one worker)
%time time_me(nmbr=1)
# Wall time: 25 s
# Basically 50 requests * 0.5 seconds IO lag
# For me everything gets processed by bee number: 59
# Run this code using multi-tasking (launch 50 workers)
%time time_me(nmbr=50)
# Wall time: 507 ms
# Basically the 0.5 second IO lag + 0.07 seconds it took to launch them
# Now everything gets processed by different bees
Is it asynchronous?
To me this code does not seem asynchronous because it is Figure 3 in my example diagram. The I/O call blocks the thread (although we don't feel it because they are blocked in parallel).
However, if this is the case I am confused why requests-futures is considered asynchronous since it is a wrapper around ThreadPoolExecutor:
with concurrent.futures.ThreadPoolExecutor(max_workers=20) as executor:
future_to_url = {executor.submit(load_url, url, 10): url for url in get_urls()}
for future in concurrent.futures.as_completed(future_to_url):
url = future_to_url[future]
try:
data = future.result()
Can this function on just one thread?
Especially when compared to asyncio, which means it can run single-threaded
There are only two ways to have a program on a single processor do
“more than one thing at a time.” Multi-threaded programming is the
simplest and most popular way to do it, but there is another very
different technique, that lets you have nearly all the advantages of
multi-threading, without actually using multiple threads. It’s really
only practical if your program is largely I/O bound. If your program
is processor bound, then pre-emptive scheduled threads are probably
what you really need. Network servers are rarely processor bound,
however.

First of all, one note: concurrent.futures.Future is not the same as asyncio.Future. Basically it's just an abstraction - an object, that allows you to refer to job result (or exception, which is also a result) in your program after you assigned a job, but before it is completed. It's similar to assigning common function's result to some variable.
Multithreading: Regarding your example, when using multiple threads you can say that your code is "asynchronous" as several operations are performed in different threads at the same time without waiting for each other to complete, and you can see it in the timing results. And you're right, your function due to sleep is blocking, it blocks the worker thread for the specified amount of time, but when you use several threads those threads are blocked in parallel. So if you would have one job with sleep and the other one without and run multiple threads, the one without sleep would perform calculations while the other would sleep. When you use single thread, the jobs are performed in in a serial manner one after the other, so when one job sleeps the other jobs wait for it, actually they just don't exist until it's their turn. All this is pretty much proven by your time tests. The thing happened with print has to do with "thread safety", i.e. print uses standard output, which is a single shared resource. So when your multiple threads tried to print at the same time the switching happened inside and you got your strange output. (This also show "asynchronicity" of your multithreaded example.) To prevent such errors there are locking mechanisms, e.g. locks, semaphores, etc.
Asyncio: To better understand the purpose note the "IO" part, it's not 'async computation', but 'async input/output'. When talking about asyncio you usually don't think about threads at first. Asyncio is about event loop and generators (coroutines). The event loop is the arbiter, that governs the execution of coroutines (and their callbacks), that were registered to the loop. Coroutines are implemented as generators, i.e. functions that allow to perform some actions iteratively, saving state at each iteration and 'returning', and on the next call continuing with the saved state. So basically the event loop is while True: loop, that calls all coroutines/generators, assigned to it, one after another, and they provide result or no-result on each such call - this provides possibility for "asynchronicity". (A simplification, as there's scheduling mechanisms, that optimize this behavior.) The event loop in this situation can run in single thread and if coroutines are non-blocking it will give you true "asynchronicity", but if they are blocking then it's basically a linear execution.
You can achieve the same thing with explicit multithreading, but threads are costly - they require memory to be assigned, switching them takes time, etc. On the other hand asyncio API allows you to abstract from actual implementation and just consider your jobs to be performed asynchronously. It's implementation may be different, it includes calling the OS API and the OS decides what to do, e.g. DMA, additional threads, some specific microcontroller use, etc. The thing is it works well for IO due to lower level mechanisms, hardware stuff. On the other hand, performing computation will require explicit breaking of computation algorithm into pieces to use as asyncio coroutine, so a separate thread might be a better decision, as you can launch the whole computation as one there. (I'm not talking about algorithms that are special to parallel computing). But asyncio event loop might be explicitly set to use separate threads for coroutines, so this will be asyncio with multithreading.
Regarding your example, if you'll implement your function with sleep as asyncio coroutine, shedule and run 50 of them single threaded, you'll get time similar to the first time test, i.e. around 25s, as it is blocking. If you will change it to something like yield from [asyncio.sleep][3](0.5) (which is a coroutine itself), shedule and run 50 of them single threaded, it will be called asynchronously. So while one coroutine will sleep the other will be started, and so on. The jobs will complete in time similar to your second multithreaded test, i.e. close to 0.5s. If you will add print here you'll get good output as it will be used by single thread in serial manner, but the output might be in different order then the order of coroutine assignment to the loop, as coroutines could be run in different order. If you will use multiple threads, then the result will obviously be close to the last one anyway.
Simplification: The difference in multythreading and asyncio is in blocking/non-blocking, so basicly blocking multithreading will somewhat come close to non-blocking asyncio, but there're a lot of differences.
Multithreading for computations (i.e. CPU bound code)
Asyncio for input/output (i.e. I/O bound code)
Regarding your original statement:
all asynchronous code is multi-threaded
all multi-threaded functions are asynchronous
I hope that I was able to show, that:
asynchronous code might be both single threaded and multi-threaded
all multi-threaded functions could be called "asynchronous"

I think the main confusion comes from the meaning of asynchronous. From the Free Online Dictionary of Computing, "A process [...] whose execution can proceed independently" is asynchronous. Now, apply that to what your bees do:
Retrieve an item from the queue. Only one at a time can do that, while the order in which they get an item is undefined. I wouldn't call that asynchronous.
Sleep. Each bee does so independently of all others, i.e. the sleep duration runs on all, otherwise the time wouldn't go down with multiple bees. I'd call that asynchronous.
Call print(). While the calls are independent, at some point the data is funneled into the same output target, and at that point a sequence is enforced. I wouldn't call that asynchronous. Note however that the two arguments to print() and also the trailing newline are handled independently, which is why they can be interleaved.
Lastly, the call to q.join(). Here of course the calling thread is blocked until the queue is empty, so some kind of synchronization is enforced and wanted. I don't see why this "seems to break" for you.

using multiple threads in Python

I'm trying to solve a problem, where I have many (on the order of ten thousand) URLs, and need to download the content from all of them. I've been doing this in a "for link in links:" loop up till now, but the amount of time it's taking is now too long. I think it's time to implement a multithreaded or multiprocessing approach. My question is, what is the best approach to take?
I know about the Global Interpreter Lock, but since my problem is network-bound, not CPU-bound, I don't think that will be an issue. I need to pass data back from each thread/process to the main thread/process. I don't need help implementing whatever approach (Terminate multiple threads when any thread completes a task covers that), I need advice on which approach to take. My current approach:
data_list = get_data(...)
output = []
for datum in data:
output.append(get_URL_data(datum))
return output
There's no other shared state.
I think the best approach would be to have a queue with all the data in it, and have several worker threads pop from the input queue, get the URL data, then push onto an output queue.
Am I right? Is there anything I'm missing? This is my first time implementing multithreaded code in any language, and I know it's generally a Hard Problem.

For your specific task I would recommend a multiprocessing worker pool. You simply define a pool and tell it how many processes you want to use (one per processor core by default) as well as a function you want to run on each unit of work. Then you ready every unit of work (in your case this would be a list of URLs) in a list and give it to the worker pool.
Your output will be a list of the return values of your worker function for every item of work in your original array. All the cool multi-processing goodness will happen in the background. There is of course other ways of working with the worker pool as well, but this is my favourite one.
Happy multi-processing!

The best approach I can think of in your use case will be to use a thread pool and maintain a work queue. The threads in the thread pool get work from the work queue, do the work and then go get some more work. This way you can finely control the number of threads working on your URLs.
So, create a WorkQueue, which in your case is basically a list containing the URLs that need to be downloaded.
Create a thread pool, which create the number of threads you specify, fetches work from the WorkQueue and assigns it to a thread. Each time a thread finishes and returns you check if the work queues has more work and accordingly assign work to that thread again. You may also want to put a hook so that every time work is added to the work queue, your threads assigns it to a free thread if available.

The fastest and most efficient method of doing IO bound tasks like this is an asynchronous event loop. The libcurl can do this, and there is a Python wrapper for that called pycurl. Using it's "multi" interface you can do high-performance client activities. I have done over 1000 simultaneous fetchs as fast as one.
However, the API is quite low-level and difficult to use. There is a simplifying wrapper here, which you can use as an example.

Pattern for a background Twisted server that fills an incoming message queue and empties an outgoing message queue?

I'd like to do something like this:
twistedServer.start() # This would be a nonblocking call
while True:
while twistedServer.haveMessage():
message = twistedServer.getMessage()
response = handleMessage(message)
twistedServer.sendResponse(response)
doSomeOtherLogic()
The key thing I want to do is run the server in a background thread. I'm hoping to do this with a thread instead of through multiprocessing/queue because I already have one layer of messaging for my app and I'd like to avoid two. I'm bringing this up because I can already see how to do this in a separate process, but what I'd like to know is how to do it in a thread, or if I can. Or if perhaps there is some other pattern I can use that accomplishes this same thing, like perhaps writing my own reactor.run method. Thanks for any help.
:)

The key thing I want to do is run the server in a background thread.
You don't explain why this is key, though. Generally, things like "use threads" are implementation details. Perhaps threads are appropriate, perhaps not, but the actual goal is agnostic on the point. What is your goal? To handle multiple clients concurrently? To handle messages of this sort simultaneously with events from another source (for example, a web server)? Without knowing the ultimate goal, there's no way to know if an implementation strategy I suggest will work or not.
With that in mind, here are two possibilities.
First, you could forget about threads. This would entail defining your event handling logic above as only the event handling parts. The part that tries to get an event would be delegated to another part of the application, probably something ultimately based on one of the reactor APIs (for example, you might set up a TCP server which accepts messages and turns them into the events you're processing, in which case you would start off with a call to reactor.listenTCP of some sort).
So your example might turn into something like this (with some added specificity to try to increase the instructive value):
from twisted.internet import reactor
class MessageReverser(object):
"""
Accept messages, reverse them, and send them onwards.
"""
def __init__(self, server):
self.server = server
def messageReceived(self, message):
"""
Callback invoked whenever a message is received. This implementation
will reverse and re-send the message.
"""
self.server.sendMessage(message[::-1])
doSomeOtherLogic()
def main():
twistedServer = ...
twistedServer.start(MessageReverser(twistedServer))
reactor.run()
main()
Several points to note about this example:
I'm not sure how your twistedServer is defined. I'm imagining that it interfaces with the network in some way. Your version of the code would have had it receiving messages and buffering them until they were removed from the buffer by your loop for processing. This version would probably have no buffer, but instead just call the messageReceived method of the object passed to start as soon as a message arrives. You could still add buffering of some sort if you want, by putting it into the messageReceived method.
There is now a call to reactor.run which will block. You might instead write this code as a twistd plugin or a .tac file, in which case you wouldn't be directly responsible for starting the reactor. However, someone must start the reactor, or most APIs from Twisted won't do anything. reactor.run blocks, of course, until someone calls reactor.stop.
There are no threads used by this approach. Twisted's cooperative multitasking approach to concurrency means you can still do multiple things at once, as long as you're mindful to cooperate (which usually means returning to the reactor once in a while).
The exact times the doSomeOtherLogic function is called is changed slightly, because there's no notion of "the buffer is empty for now" separate from "I just handled a message". You could change this so that the function is installed called once a second, or after every N messages, or whatever is appropriate.
The second possibility would be to really use threads. This might look very similar to the previous example, but you would call reactor.run in another thread, rather than the main thread. For example,
from Queue import Queue
from threading import Thread
class MessageQueuer(object):
def __init__(self, queue):
self.queue = queue
def messageReceived(self, message):
self.queue.put(message)
def main():
queue = Queue()
twistedServer = ...
twistedServer.start(MessageQueuer(queue))
Thread(target=reactor.run, args=(False,)).start()
while True:
message = queue.get()
response = handleMessage(message)
reactor.callFromThread(twistedServer.sendResponse, response)
main()
This version assumes a twistedServer which works similarly, but uses a thread to let you have the while True: loop. Note:
You must invoke reactor.run(False) if you use a thread, to prevent Twisted from trying to install any signal handlers, which Python only allows to be installed in the main thread. This means the Ctrl-C handling will be disabled and reactor.spawnProcess won't work reliably.
MessageQueuer has the same interface as MessageReverser, only its implementation of messageReceived is different. It uses the threadsafe Queue object to communicate between the reactor thread (in which it will be called) and your main thread where the while True: loop is running.
You must use reactor.callFromThread to send the message back to the reactor thread (assuming twistedServer.sendResponse is actually based on Twisted APIs). Twisted APIs are typically not threadsafe and must be called in the reactor thread. This is what reactor.callFromThread does for you.
You'll want to implement some way to stop the loop and the reactor, one supposes. The python process won't exit cleanly until after you call reactor.stop.
Note that while the threaded version gives you the familiar, desired while True loop, it doesn't actually do anything much better than the non-threaded version. It's just more complicated. So, consider whether you actually need threads, or if they're merely an implementation technique that can be exchanged for something else.

Communicating end of Queue

I'm learning to use the Queue module, and am a bit confused about how a queue consumer thread can be made to know that the queue is complete. Ideally I'd like to use get() from within the consumer thread and have it throw an exception if the queue has been marked "done". Is there a better way to communicate this than by appending a sentinel value to mark the last item in the queue?

original (most of this has changed; see updates below)
Based on some of the suggestions (thanks!) of Glenn Maynard and others, I decided to roll up a descendant of Queue.Queue that implements a close method. It's available in the form of a primitive (unpackaged) module. I'll clean this up a bit and package it properly when I have a bit more time. For now the module only contains the CloseableQueue class and the Closed exception class. I'm planning to expand it to also include subclasses of Queue.LifoQueue and Queue.PriorityQueue.
It's in a pretty preliminary state currently, which is to say that although it passes its test suite, I haven't actually used it for anything yet. Your mileage may vary. I'll keep this answer updated with exciting news.
The CloseableQueue class differs a bit from Glenn's suggestion in that closing the queue will prevent future puts, but not prevent future gets until the queue is emptied. This made the most sense to me; it seemed like functionality to clear the queue could be added as a separate mixin* that would be orthogonal to the closeability functionality. So basically with CloseableQueue, by closing the queue you indicate that the last element has been put. There's also an option to do this atomically by passing last=True to the final put call. Subsequent calls to put, and subsequent calls to get once the queue is emptied, as well as outstanding blocked calls matching those descriptions, will raise the Closed exception.
This is mostly useful for situations where a single producer is generating data for one or more consumers, but it could also be useful for a multi-multi arrangement where consumers are waiting for a particular item or set of items. In particular it doesn't provide a way to determine that all of a number of producers have finished production. Getting that working would entail the provision of some way to register producers (.open()?), as well as a way to indicate that producer registration is itself closed.
Suggestions and/or code reviews are quite welcome. I haven't written a whole lot of concurrency code, but hopefully the test suite is thorough enough that the fact that the code passes it is an indication of the code's quality, rather than the suite's lack thereof. I was able to reuse a bunch of the code from the Queue module's test suite: the file itself is included in this module and used as a basis for various subclasses and routines, including regression testing. This probably (hopefully) helped to avoid complete ineptitude in the testing department. The code itself just overrides Queue.get and Queue.put with fairly minimal changes, and adds the close and closed methods.
I've sort of intentionally avoided using any new-fangled fanciness like context managers in both the code itself and in the test suite in an effort to keep the code as backwards-compatible as is the Queue module itself, which is considerably backwards indeed. I'll probably add __enter__ and __exit__ methods at some point; otherwise, the contextlib's closing function should be applicable to a CloseableQueue instance.
*: Here I use the term "mixin" loosely. As the Queue module's classes are old-style, mixins would need to be mixed using class factory functions; some restrictions apply; offer void where prohibited by Guido.
update
The CloseableQueue module now provides CloseableLifoQueue and CloseablePriorityQueue classes. I've also added some convenience functions to support iteration. Still need to rework it as a proper package. There's a class factory function to allow for convenient subclassing of other Queue.Queue-derived classes.
update 2
CloseableQueue is now available via PyPI, e.g. with
$ easy_install CloseableQueue
Comments and criticism are welcome, especially from this answer's anonymous downvoter.

Queue's don't inherently have the idea of being complete or done. They can be used indefinitely. To close it up when you are done, you will indeed need to put None or some other magic value at the end and write the logic to check for it, as you described. The ideal way would probably be subclassing the Queue object.
See http://en.wikipedia.org/wiki/Queue_(data_structure) to learn more about queue in general.

A sentinel is a natural way to shut down a queue, but there are a couple things to watch out for.
First, remember that you may have more than one consumer, so you need to send a sentinel once for each running consumer, and guarantee that each consumer will only consume one sentinel, to ensure that each consumer receives its shutdown sentinel.
Second, remember that Queue defines an interface, and that when possible, code should behave regardless of the underlying Queue. You might have a PriorityQueue, or you might have some other class that exposes the same interface and returns values in some other order.
Unfortunately, it's hard to deal with both of these. To deal with the general case of different queues, a consumer that's shutting down must continue to consume values after receiving its shutdown sentinel until the queue is empty. That means that it may consume another thread's sentinel. This is a weakness of the Queue interface: it should have a Queue.shutdown call to cause an exception to be thrown by all consumers, but that's missing.
So, in practice:
if you're sure you're only ever using a regular Queue, simply send one sentinel per thread.
if you may be using a PriorityQueue, ensure that the sentinel has the lowest priority.

Queue is a FIFO (first in first out) register so remember that the consumer can be faster than producer. When consumers thread detect that the queue is empty normally realise one of following actions:
Send to API: switch to next thread.
Send to API: sleep some ms and than check again the queue.
Send to API: wait on event (like new message in queue).
If you wont that consumers thread terminate after job is complete than put in queue a sentinel value to terminate task.

The best practice way of doing this would be to have the queue itself notify a client that it has reached the 'done' state. The client can then take any action that is appropriate.
What you have suggested; checking the queue to see if it is done periodically, would be highly undesirable. Polling is an antipattern in multithreaded programming, you should always be using notifications.
EDIT:
So your saying that the queue itself knows that it's 'done' based on some criteria and needs to notify the clients of that fact. I think you are correct and the best way to do this is by throwing when a client calls get() and the queue is in the done state. If your throwing this would negate the need for a sentinel value on the client side. Internally the queue can detect that it is 'done' in any way it pleases e.g. queue is empty, it's state was set to done etc I don't see any need for a sentinel value.

A multi-part/threaded downloader via python?

I've seen a few threaded downloaders online, and even a few multi-part downloaders (HTTP).
I haven't seen them together as a class/function.
If any of you have a class/function lying around, that I can just drop into any of my applications where I need to grab multiple files, I'd be much obliged.
If there is there a library/framework (or a program's back-end) that does this, please direct me towards it?

Threadpool by Christopher Arndt may be what you're looking for. I've used this "easy to use object-oriented thread pool framework" for the exact purpose you describe and it works great. See the usage examples at the bottom on the linked page. And it really is easy to use: just define three functions (one of which is an optional exception handler in place of the default handler) and you are on your way.
from http://www.chrisarndt.de/projects/threadpool/:
Object-oriented, reusable design
Provides callback mechanism to process results as they are returned from the worker threads.
WorkRequest objects wrap the tasks assigned to the worker threads and allow for easy passing of arbitrary data to the callbacks.
The use of the Queue class solves most locking issues.
All worker threads are daemonic, so they exit when the main program exits, no need for joining.
Threads start running as soon as you create them. No need to start or stop them. You can increase or decrease the pool size at any time, superfluous threads will just exit when they finish their current task.
You don't need to keep a reference to a thread after you have assigned the last task to it. You just tell it: "don't come back looking for work, when you're done!"
Threads don't eat up cycles while waiting to be assigned a task, they just block when the task queue is empty (though they wake up every few seconds to check whether they are dismissed).
Also available at http://pypi.python.org/pypi/threadpool, easy_install, or as a subversion checkout (see project homepage).