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How to periodically call instance method from a separate process

I'm trying to write a class to help with buffering some data that takes a while to read in, and which needs to be periodically updated. The python version is 3.7.
There are 3 criteria I would like the class to satisfy:
Manual update: An instance of the class should have an 'update' function, which reads in new data.
Automatic update: An instance's update method should be periodically run, so the buffered data never gets too old. As reading takes a while, I'd like to do this without blocking the main process.
Self contained: Users should be able to inherit from the class and overwrite the method for refreshing data, i.e. the automatic updating should work out of the box.
I've tried having instances create their own subprocess for running the updates. This causes problems because simply passing the instance to another process seems to create a copy, so the desired instance is not updated automatically.
Below is an example of the approach I'm trying. Can anyone help getting the automatic update to work?
import multiprocessing as mp
import random
import time
def refresh_helper(buffer, lock):
"""Periodically calls refresh method in a buffer instance."""
while True:
with lock.acquire():
buffer._refresh_data()
time.sleep(10)
class Buffer:
def __init__(self):
# Set up a helper process to periodically update data
self.lock = mp.Lock()
self.proc = mp.Process(target=refresh_helper, args=(self, self.lock), daemon=True)
self.proc.start()
# Do an initial update
self.data = None
self.update()
def _refresh_data(self):
"""Pretends to read in some data. This would take a while for real data"""
numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9]
data = [random.choice(numbers) for _ in range(3)]
self.data = data
def update(self):
with self.lock.acquire():
self._refresh_data()
def get_data(self):
return self.data
#
if __name__ == '__main__':
buffer = Buffer()
data_first = buffer.get_data()
time.sleep(11)
data_second = buffer.get_data() # should be different from first

Here is an approach that makes use a of a multiprocessing queue. It's similar to what you had implemented, but your implementation was trying to assign to self within Buffer._refresh_data in both processes. Because self refers to a different Buffer object in each process, they did not affect each other.
To send data from one process to another you need to use shared memory, pipes, or some other such mechanism. Python's multiprocessing library provides multiprocess.Queue, which simplifies this for us.
To send data from the refresh helper to the main process we need only use queue.put in the helper process, and queue.get in the main process. The data being sent must be serializable using Python's pickle module to be sent between the processes through a multiprocess.Queue.
Using a multiprocess.Queue also saves us from having to use locks ourselves, since the queue handles that internally.
To handle the helper process starting and stopping cleanly for the example, I have added __enter__ and __exit__ methods to make Buffer into a context manager. They can be removed if you would rather manually stop the helper process.
I have also changed your _refresh_data method into _get_new_data, which returns new data half the time, and has no new data to give the other half of the time (i.e. it returns None). This was done to make it more similar to what I imagine a real application for this class would be.
It is important that only static/class methods or external functions are called from the other process, as otherwise they may operate on a self attribute that refers to a completely different instance. The exception is if the attribute is meant to be sent across the process barrier, like with self.queue. That is why the update method can use self.queue to send data to the main process despite self being a different Buffer instance in the other process.
The method get_next_data will return the oldest item found in the queue. If there is nothing in the queue, it will wait until something is added to the queue. You can change this behaviour by giving the call to self.queue.get a timeout (which will cause an exception to be raised if it times out), or using self.queue.get_nowait (which will return None immediately if the queue is empty).
from __future__ import annotations
import multiprocessing as mp
import random
import time
class Buffer:
def __init__(self):
self.queue = mp.Queue()
self.proc = mp.Process(target=self._refresh_helper, args=(self,))
self.update()
def __enter__(self):
self.proc.start()
return self
def __exit__(self, ex_type, ex_val, ex_tb):
self.proc.kill()
self.proc.join()
#staticmethod
def _refresh_helper(buffer: "Buffer", period: float = 1.0) -> None:
"""Periodically calls refresh method in a buffer instance."""
while True:
buffer.update()
time.sleep(period)
#staticmethod
def _get_new_data() -> list[int] | None:
"""Pretends to read in some data. This would take a while for real data"""
if random.randint(0, 1):
return random.choices(range(10), k=3)
return None
def update(self) -> None:
new_data = self._get_new_data()
if new_data is not None:
self.queue.put(new_data)
def get_next_data(self):
return self.queue.get()
if __name__ == '__main__':
with Buffer() as buffer:
for _ in range(5):
print(buffer.get_next_data())
Running this code will, as an example, start the helper process, then print out the first 5 pieces of data it gets from the buffer. The first one will be from the update that is performed when the buffer is initialized. The others will all be provided by the helper process running update.
Let's review your criteria:
Manual update: An instance of the class should have an 'update' function, which reads in new data.
The Buffer.update method can be used for this.
Automatic update: An instance's update method should be periodically run, so the buffered data never gets too old. As reading takes a while, I'd like to do this without blocking the main process.
This is done by a helper process which adds data to a queue for later processing. If you would rather throw away old data, and only process the newest data, then the queue can be swapped out for a multiprocess.Array, or whatever other multiprocessing compatible shared memory wrapper you prefer.
Self contained: Users should be able to inherit from the class and overwrite the method for refreshing data, i.e. the automatic updating should work out of the box.
This works by overwriting the _get_new_data method. So long as it's a static or class method which returns the data, automatic updating should work with it without any changes.

All processes exist in different areas of memory from one another, each of which is meant to be fully separate from all others. As you pointed out, the additional process creates a copy of the instance on which it operates, meaning the updated version exists in a separate memory space from the instance you're running get_data() on. Because of this there is no easy way to perform this operation on this specific instance from a different process.
Given that you want the updating of the data to not block the checking of the data, you may not use threading, as only 1 thread may operate at a time in any given process. Instead, you need to use an object which exists in a memory space shared between all processes. To do this, you can use a multiprocessing.Value object or a multiprocessing.Array, both of which store ctypes objects. Both of these objects existed in 3.7 (appropriate documentation attached.)
If this approach does not work, consider examining these similar threads:
Sharing a complex object between processes?
multiprocessing: sharing a large read-only object between processes?
Good luck with your project!

How to have dedicated variable for multiprocessing worker, which keeps its value between calls?

I have the following code:
pool = Pool(cpu_count())
pool.imap(process_item, items, chunksize=100)
In the process_item() function I am using structures which are resource demanding to create, but it would be reusable. (but not concurrently shareable) Currently within each call of process_item() it creates the resource in a local variable repeatedly. It would be great performance benefit to create once (for each worker) then reuse
Question
How to have delegated cpu_count() instances for those resource, and how to implement the process_item() function to access the appropriate delegated instance belonging that particular worker?

If you cannot use anything outside the standard library, I would suggest using using an initializer when creating the pool:
from multiprocessing import Pool, Manager, Process
import os
import random
class A:
def __init__(self):
self.var = random.randint(0, 1000)
def get(self):
print(self.var, os.getpid())
def worker(some_arg):
global expensive_var
expensive_var.get()
def initializer(*args):
global expensive_var
expensive_var = A()
if __name__ == "__main__":
pool = Pool(8, initializer=initializer, initargs=())
for result in pool.imap(worker, range(100)):
continue
Create your local variables inside the initializer, and make them global. Then you can use them inside the function you are passing to the pool. This works because the initializer is executed in when each process of the pool starts. So making them global would make it a global variable in the scope of the child process only, allowing access to it during execution of the function you passed to the pool.
There was a stackoverflow answer that explained all this better, but I can't seem to find it for now. But this is basically the gist of it.

Python class instance starts method in new thread

I spent the last hour(s???) looking/googling for a way to have a class start one of its methods in a new thread as soon as it is instanciated.
I could run something like this:
x = myClass()
def updater():
while True:
x.update()
sleep(0.01)
update_thread = Thread(target=updater)
update_thread.daemon = True
update_thread.start()
A more elegant way would be to have the class doing it in init when it is instanciated.
Imagine having 10 instances of that class...
Until now I couldn't find a (working) solution for this problem...
The actual class is a timer and the method is an update method that updates all the counter's variables. As this class also has to run functions at a given time it is important that the time updates won't be blocked by the main thread.
Any help is much appreciated. Thx in advance...

You can subclass directly from Thread in this specific case
from threading import Thread
class MyClass(Thread):
def __init__(self, other, arguments, here):
super(MyClass, self).__init__()
self.daemon = True
self.cancelled = False
# do other initialization here
def run(self):
"""Overloaded Thread.run, runs the update
method once per every 10 milliseconds."""
while not self.cancelled:
self.update()
sleep(0.01)
def cancel(self):
"""End this timer thread"""
self.cancelled = True
def update(self):
"""Update the counters"""
pass
my_class_instance = MyClass()
# explicit start is better than implicit start in constructor
my_class_instance.start()
# you can kill the thread with
my_class_instance.cancel()

In order to run a function (or memberfunction) in a thread, use this:
th = Thread(target=some_func)
th.daemon = True
th.start()
Comparing this with deriving from Thread, it has the advantage that you don't export all of Thread's public functions as own public functions. Actually, you don't even need to write a class to use this code, self.function or global_function are both equally usable as target here.
I'd also consider using a context manager to start/stop the thread, otherwise the thread might stay alive longer than necessary, leading to resource leaks and errors on shutdown. Since you're putting this into a class, start the thread in __enter__ and join with it in __exit__.

Python threading.Thread, scopes and garbage collection

Say I derive from threading.Thread:
from threading import Thread
class Worker(Thread):
def start(self):
self.running = True
Thread.start(self)
def terminate(self):
self.running = False
self.join()
def run(self):
import time
while self.running:
print "running"
time.sleep(1)
Any instance of this class with the thread being started must have it's thread actively terminated before it can get garbage collected (the thread holds a reference itself). So this is a problem, because it completely defies the purpose of garbage collection. In that case having some object encapsulating a thread, and with the last instance of the object going out of scope the destructor gets called for thread termination and cleanup. Thuss a destructor
def __del__(self):
self.terminate()
will not do the trick.
The only way I see to nicely encapsulate threads is by using low level thread builtin module and weakref weak references. Or I may be missing something fundamental. So is there a nicer way than tangling things up in weakref spaghetti code?

How about using a wrapper class (which has-a Thread rather than is-a Thread)?
eg:
class WorkerWrapper:
__init__(self):
self.worker = Worker()
__del__(self):
self.worker.terminate()
And then use these wrapper classes in client code, rather than threads directly.
Or perhaps I miss something (:

To add an answer inspired by #datenwolf's comment, here is another way to do it that deals with the object being deleted or the parent thread ending:
import threading
import time
import weakref
class Foo(object):
def __init__(self):
self.main_thread = threading.current_thread()
self.initialised = threading.Event()
self.t = threading.Thread(target=Foo.threaded_func,
args=(weakref.proxy(self), ))
self.t.start()
while not self.initialised.is_set():
# This loop is necessary to stop the main threading doing anything
# until the exception handler in threaded_func can deal with the
# object being deleted.
pass
def __del__(self):
print 'self:', self, self.main_thread.is_alive()
self.t.join()
def threaded_func(self):
self.initialised.set()
try:
while True:
print time.time()
if not self.main_thread.is_alive():
print('Main thread ended')
break
time.sleep(1)
except ReferenceError:
print('Foo object deleted')
foo = Foo()
del foo
foo = Foo()

I guess you are a convert from C++ where a lot of meaning can be attached to scopes of variables, equalling lifetimes of variables. This is not the case for Python, and garbage collected languages in general.
Scope != Lifetime simply because garbage collection occurs whenever the interpreter gets around to it, not on scope boundaries. Especially as you are trying to do asynchronuous stuff with it, the raised hairs on your neck should vibrate to the clamour of all the warning bells in your head!
You can do stuff with the lifetime of objects, using 'del'.
(In fact, if you read the sources to the cpython garbage collector module, the obvious (and somewhat funny) disdain for objects with finalizers (del methods) expressed there, should tell everybody to use even the lifetime of an object only if necessary).
You could use sys.getrefcount(self) to find out when to leave the loop in your thread. But I can hardly recommend that (just try out what numbers it returns. You won't be happy. To see who holds what just check gc.get_referrers(self)).
The reference count may/will depend on garbage collection as well.
Besides, tying the runtime of a thread of execution to scopes/lifetimes of objects is an error 99% of the time. Not even Boost does it. It goes out of its RAII way to define something called a 'detached' thread.
http://www.boost.org/doc/libs/1_55_0/doc/html/thread/thread_management.html

How to create a synchronized object with Python multiprocessing?

I am trouble figuring out how to make a synchronized Python object. I have a class called Observation and a class called Variable that basically looks like (code is simplified to show the essence):
class Observation:
def __init__(self, date, time_unit, id, meta):
self.date = date
self.time_unit = time_unit
self.id = id
self.count = 0
self.data = 0
def add(self, value):
if isinstance(value, list):
if self.count == 0:
self.data = []
self.data.append(value)
else:
self.data += value
self.count += 1
class Variable:
def __init__(self, name, time_unit, lock):
self.name = name
self.lock = lock
self.obs = {}
self.time_unit = time_unit
def get_observation(self, id, date, meta):
self.lock.acquire()
try:
obs = self.obs.get(id, Observation(date, self.time_unit, id, meta))
self.obs[id] = obs
finally:
self.lock.release()
return obs
def add(self, date, value, meta={}):
self.lock.acquire()
try:
obs = self.get_observation(id, date, meta)
obs.add(value)
self.obs[id] = obs
finally:
self.lock.release()
This is how I setup the multiprocessing part:
plugin = function defined somewhere else
tasks = JoinableQueue()
result = JoinableQueue()
mgr = Manager()
lock = mgr.RLock()
var = Variable('foobar', 'year', lock)
for person in persons:
tasks.put(Task(plugin, var, person))
Example of how the code is supposed to work:
I have an instance of Variable called var and I want to add an observation to var:
today = datetime.datetime.today()
var.add(today, 1)
So, the add function of Variable looks whether there already exists an observation for that date, if it does then it returns that observation else it creates a new instance of Observation. Having found an observation than the actual value is added by the call obs.add(value). My main concern is that I want to make sure that different processes are not creating multiple instances of Observation for the same date, that's why I lock it.
One instance of Variable is created and is shared between different processes using the multiprocessing library and is the container for numerous instances of Observation. The above code does not work, I get the error:
RuntimeError: Lock objects should only
be shared between processes through
inheritance
However, if I instantiate a Lock object before launching the different processes and supply it to the constructor of Variable then it seems that I get a race condition as all processes seem to be waiting for each other.
The ultimate goal is that different processes can update the obs variable in the object Variable. I need this to be threadsafe because I am not just modifying the dictionary in place but adding new elements and incrementing existing variables. the obs variable is a dictionary that contains a bunch of instances of Observation.
How can I make this synchronized where I share one single instance of Variable between numerous multiprocessing processes? Thanks so much for your cognitive surplus!
UPDATE 1:
* I am using multiprocessing Locks and I have changed the source code to show this.
* I have changed the title to more accurately capture the problem
* I have replaced theadsafe with synchronization where I was confusing the two terms.
Thanks to Dmitry Dvoinikov for pointing me out!
One question that I am still not sure about is where do I instantiate Lock? Should this happen inside the class or before initializing the multiprocesses and give it as an argument? ANSWER: Should happen outside the class.
UPDATE 2:
* I fixed the 'Lock objects should only be shared between processes through inheritance' error by moving the initialization of the Lock outside the class definition and using a manager.
* Final question, now everything works except that it seems that when I put my Variable instance in the queue then it does not get updated, and everytime I get it from the queue it does not contain the observation I added in the previous iteration. This is the only thing that is confusing me :(
UPDATE 3:
The final solution was to set the var.obs dictionary to an instance of mgr.dict() and then to have a custom serializer. Happy tho share the code with somebody who is struggling with this as well.

You are talking not about thread safety but about synchronization between separate processes and that's entirely different thing. Anyway, to start
different processes can update the obs variable in the object Variable.
implies that Variable is in shared memory, and you have to explicitly store objects there, by no magic a local instance becomes visible to separate process. Here:
Data can be stored in a shared memory map using Value or Array
Then, your code snippet is missing crucial import section. No way to tell whether you instantiate the right multiprocessing.Lock, not multithreading.Lock. Your code doesn't show the way you create processes and pass data around.
Therefore, I'd suggest that you realize the difference between threads and processes, whether you truly need a shared memory model for an application which contains multiple processes and examine the spec.