Simple wrapper for python multiprocessing and threading.
Simple wrappers for python multiprocessing and threading.
python -m pip install ezq
Example: Quick Start
If you just want to apply a function to some inputs, you can use
ezq.map() to run it on all available CPUs and get the results back.
import ezq print(list(ezq.map(lambda x: x * 2, range(6)))) # => [0, 2, 4, 6, 8, 10]
Example: Sum Messages
Here's a simple example of a worker that reads from an input queue, sums up the messages, and puts the result on an output queue.
import ezq def worker(q, out): """Add up all the messages.""" total = 0 for msg in q: # read a message from the queue total += msg.data # after reading all the messages, write the total out.put(total) def main(): """Run several workers.""" # Step 1: Creates the queues and start the workers. q, out = ezq.Q(), ezq.Q() # input & output queues workers = [ezq.run(worker, q, out) for _ in range(ezq.NUM_CPUS)] # workers are all running # Step 2: Send work to the workers. for i in range(1000): q.put(i) # send work # Step 3: Tell the workers to finish. q.stop(workers) # workers are all stopped # Step 4: Process the results. want = sum(range(1000)) have = sum(msg.data for msg in out.items()) assert have == want print(have) if __name__ == "__main__": main()
Typical worker lifecycle
The main process creates queues with
The main process creates workers with
The main process sends data using
The worker iterates over the queue.
The main process ends the queue with
The worker returns when it reaches the end of the queue.
(Optional) The main process processes the results.
ezq supports two kinds of workers:
Thread. There is a lot of existing discussion about when to use which approach, but a general rule of thumb is:
Processis for parallelism so you can use multiple CPUs at once. Ideal for CPU-bound tasks like doing lots of mathematical calculations.
Threadis for concurrency so you can use a single CPU to do multiple things. Ideal for I/O-bound tasks like waiting for a disk, database, or network.
Some more differences:
Shared memory: Each
Processworker has data sent to it via
picklereplacement) and it doesn't share data with other workers. By contrast, each
Threadworker shares its memory with all other workers on the same CPU, so it can accidentally change global state.
ezq.Qhas more overhead for
Threadworkers can create additional
Threadworkers, but they cannot create additional
In the main process, create the queues you'll need. Here are my common situations:
0 queues: I'm using a simple function and can ask
ezq.mapto make the queues for me.
1 queue: the worker reads from an input queue and persists the result somewhere else (e.g., writing to disk, making a network call, running some other program).
2 queues (most common): the worker reads from an input queue and write the results to an output queue.
3 queues: multiple stages of work are happening where workers are reading from one queue and writing to another queue for another worker to process.
NOTE: If you're using
Thread workers, you can save some overhead by passing
Q("thread"). This lightweight queue also doesn't use
pickle, so you can use it to pass hard-to-pickle things (e.g., database connection).
q, out = ezq.Q(), ezq.Q() # most common q2 = ez.Q("thread") # only ok for Thread workers
A worker task is just a function
In general, there's nothing special about a worker function, but note:
If you're using
Processworkers, all arguments are passed through
We don't currently do anything with the return value of this function (unless you use
ezq.map()). You'll need an output queue to return data back to the main process/thread.
In the main process, create workers using
ezq.run_thread which take a function and any additional parameters. Typically, you'll pass the queues you created to the workers at this point.
Thread workers can create additional
Thread workers, but they cannot create additional
Once you've created the workers, you send them data with
Q.put which creates
ezq.Msg objects and puts them in the queue. Each message has three attributes (all optional):
data: Any- This is the data you want the worker to work on.
kind: str- You can use this to send multiple kinds of work to the same worker. Note that the special
ENDkind is used to indicate the end of a queue.
order: int- This is the message order which can help you reorder results or ensure that messages from a queue are read in a particular order (that's what
If you are using
Process workers, everything passed to the worker (arguments, messages) is first passed to
dill). Anything that cannot be pickled with dill (e.g., database connections), cannot be passed to
Process workers. Note that
dill can serialize many more types than
Beware shared state
If you are using
Thread workers, workers can share certain variables, so you need to be careful of how variables are access to avoid accidentally corrupting data.
Iterate over messages
Inside the worker, iterate over the queue to read each message until the queue ends (see below). If the messages need to be processed in order, use
for msg in q: # read each message until the queue ends ... for msg in q.sorted(): # read each message in order ...
End the queue
After the main process has sent all the data to the workers, it needs to indicate
that there's no additional work to be done. This is done by calling
Q.stop() using the input queue that the workers are reading from and passing the list of workers to wait for.
In some rare situations, you can use
Q.end() to explicitly end the queue.
If you have an output queue, you may want to to process the results. You can use
Q.items() to end the queue and read the current messages.
import ezq out = ezq.Q() ... result = [msg.data for msg in out.items()] # OR result = [msg.data for msg in out.items(sort=True)] # sorted by Msg.order # OR result = [msg.data for msg in out.items(cache=True)] # cache the messages
Example: Read and Write Queues
In this example, several workers read from a queue, process data, and then write to a different queue that a single worker uses to print to the screen sorting the results as it goes along.
Note that we use a single
writer to avoid clashes or overwriting.
import ezq def printer(out: ezq.Q) -> None: """Print results in increasing order.""" for msg in out.sorted(): print(msg.data) def collatz(q: ezq.Q, out: ezq.Q) -> None: """Read numbers and compute values.""" for msg in q: num = float(msg.data) if msg.kind == "EVEN": out.put((num, num / 2), order=msg.order) elif msg.kind == "ODD": out.put((num, 3 * num + 1), order=msg.order) def main() -> None: """Run several threads with a subprocess for printing.""" q, out = ezq.Q("thread"), ezq.Q() readers = [ezq.run_thread(collatz, q, out) for _ in range(ezq.NUM_THREADS)] writer = ezq.run(printer, out) for num in range(40): kind = "EVEN" if num % 2 == 0 else "ODD" q.put(num, kind=kind, order=num) q.stop(readers) out.stop(writer) if __name__ == "__main__": main()
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