I need some help with the code below.
GOAL:
I have a huge ".txt" file and need a script to search a string given as input by the user.
In this specific code I'm trying to speed things up by creating multiple threads (8, to be exact) and assigning slices of the ".txt" file for each thread, so each thread will search for the results in only a portion of the file.
Since I need the return value for the search function I created and that is passed to the threads, I used the "ThreadWithReturnValue" Class shown in this question here:
How to get the return value from a thread in python?
PROBLEM:
The problem is that I only get the result if it located in the FIRST thread. The results from the other threads are always empty lists.
If I create loggings inside the search function, the loggings appears in the terminal for the first thread only, while the loggings for the other threads do not show up. This makes me believe that the other threads are not even running, even though the thread list shows up the correct number of created threads.
HELP: I'm new to Python and have very little experience with threads. I'm very probably missing some basic thing here. If you can help me I would be immensely grateful.
NOTE: I would like to make the current code work; I know there are other options to accomplish the same task, but I would like to make this one work in the first place.
#! python3
#Search string in huge ".txt" file
import pprint
from threading import Thread
#Search function that takes arguments for searched value, file object, start line and end line of search
def searchFile(value, fileObj, linestart, lineend):
results = []
textToSearch = fileObj.readlines()[linestart:lineend]
for line in textToSearch:
if value in line:
results.append(line[0:-1])
return results
#Thread class that actually return the function's return value
class ThreadWithReturnValue(Thread):
def __init__(self, group=None, target=None, name=None,
args=(), kwargs={}, Verbose=None):
Thread.__init__(self, group, target, name, args, kwargs)
self._return = None
def run(self):
print(type(self._target))
if self._target is not None:
self._return = self._target(*self._args,
**self._kwargs)
def join(self, *args):
Thread.join(self, *args)
return self._return
#File variables
fileToSearch = 'somefile.txt'
totalLines = 1000000 #Let's suppose the file has 1 million lines
sizeThread = totalLines // 8 #Each thread will cover 1/8 of the file's lines
#Main program loop
while True:
#Ask user for input
userinput = input('Enter here your input: /n')
#Open file
SearchFileObj = open(fileToSearch)
#Create empty search results list
searchResults = []
#Create empty Threads list
searchThreads = []
#Create 8 Threads
for i in range(0, totalLines, sizeThread):
ThreadlLineStart = i
ThreadlLineEnd = i + sizeThread
#Create Thread Objects
threadObj = ThreadWithReturnValue(target=searchFile, args=[userinput, SearchFileObj, ThreadlLineStart, ThreadlLineEnd])
searchThreads.append(threadObj)
threadObj.start()
#Wait Threads to finish
for thread in searchThreads:
threadResult = thread.join()
#Verify thread result and append result (if any) to list
if threadResult == []:
continue
else:
searchResults.append(threadResult)
#Close file
fileToSearch.close()
#Give output to user
if searchResults == []:
print('No results found.')
else:
print('The results are:')
pprint.pprint(searchResults)
break
I'm creating a program that uses the Twisted module and callbacks.
However, I keep having problems because the asynchronous part goes wrecked.
I have learned (also from previous questions..) that the callbacks will be executed at a certain point, but this is unpredictable.
However, I have a certain program that goes like
j = calc(a)
i = calc2(b)
f = calc3(c)
if s:
combine(i, j, f)
Now the boolean s is set by a callback done by calc3. Obviously, this leads to an undefined error because the callback is not executed before the s is needed.
However, I'm unsure how you SHOULD do if statements with asynchronous programming using Twisted. I've been trying many different things, but can't find anything that works.
Is there some way to use conditionals that require callback values?
Also, I'm using VIFF for secure computations (which uses Twisted): VIFF
Maybe what you're looking for is twisted.internet.defer.gatherResults:
d = gatherResults([calc(a), calc2(b), calc3(c)])
def calculated((j, i, f)):
if s:
return combine(i, j, f)
d.addCallback(calculated)
However, this still has the problem that s is undefined. I can't quite tell how you expect s to be defined. If it is a local variable in calc3, then you need to return it so the caller can use it.
Perhaps calc3 looks something like this:
def calc3(argument):
s = bool(argument % 2)
return argument + 1
So, instead, consider making it look like this:
Calc3Result = namedtuple("Calc3Result", "condition value")
def calc3(argument):
s = bool(argument % 2)
return Calc3Result(s, argument + 1)
Now you can rewrite the calling code so it actually works:
It's sort of unclear what you're asking here. It sounds like you know what callbacks are, but if so then you should be able to arrive at this answer yourself:
d = gatherResults([calc(a), calc2(b), calc3(c)])
def calculated((j, i, calc3result)):
if calc3result.condition:
return combine(i, j, calc3result.value)
d.addCallback(calculated)
Or, based on your comment below, maybe calc3 looks more like this (this is the last guess I'm going to make, if it's wrong and you'd like more input, then please actually share the definition of calc3):
def _calc3Result(result, argument):
if result == "250":
# SMTP Success response, yay
return Calc3Result(True, argument)
# Anything else is bad
return Calc3Result(False, argument)
def calc3(argument):
d = emailObserver("The argument was %s" % (argument,))
d.addCallback(_calc3Result)
return d
Fortunately, this definition of calc3 will work just fine with the gatherResults / calculated code block immediately above.
You have to put if in the callback. You may use Deferred to structure your callback.
As stated in previous answer - the preocessing logic should be handled in callback chain, below is simple code demonstration how this could work. C{DelayedTask} is a dummy implementation of a task which happens in the future and fires supplied deferred.
So we first construct a special object - C{ConditionalTask} which takes care of storring the multiple results and servicing callbacks.
calc1, calc2 and calc3 returns the deferreds, which have their callbacks pointed to C{ConditionalTask}.x_callback.
Every C{ConditionalTask}.x_callback does a call to C{ConditionalTask}.process which checks if all of the results have been registered and fires on a full set.
Additionally - C{ConditionalTask}.c_callback sets a flag of wheather or not the data should be processed at all.
from twisted.internet import reactor, defer
class DelayedTask(object):
"""
Delayed async task dummy implementation
"""
def __init__(self,delay,deferred,retVal):
self.deferred = deferred
self.retVal = retVal
reactor.callLater(delay, self.on_completed)
def on_completed(self):
self.deferred.callback(self.retVal)
class ConditionalTask(object):
def __init__(self):
self.resultA=None
self.resultB=None
self.resultC=None
self.should_process=False
def a_callback(self,result):
self.resultA = result
self.process()
def b_callback(self,result):
self.resultB=result
self.process()
def c_callback(self,result):
self.resultC=result
"""
Here is an abstraction for your "s" boolean flag, obviously the logic
normally would go further than just setting the flag, you could
inspect the result variable and do other strange stuff
"""
self.should_process = True
self.process()
def process(self):
if None not in (self.resultA,self.resultB,self.resultC):
if self.should_process:
print 'We will now call the processor function and stop reactor'
reactor.stop()
def calc(a):
deferred = defer.Deferred()
DelayedTask(5,deferred,a)
return deferred
def calc2(a):
deferred = defer.Deferred()
DelayedTask(5,deferred,a*2)
return deferred
def calc3(a):
deferred = defer.Deferred()
DelayedTask(5,deferred,a*3)
return deferred
def main():
conditional_task = ConditionalTask()
dFA = calc(1)
dFB = calc2(2)
dFC = calc3(3)
dFA.addCallback(conditional_task.a_callback)
dFB.addCallback(conditional_task.b_callback)
dFC.addCallback(conditional_task.c_callback)
reactor.run()
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Are there any exemplary examples of the GoF Observer implemented in Python? I have a bit code which currently has bits of debugging code laced through the key class (currently generating messages to stderr if a magic env is set). Additionally, the class has an interface for incrementally return results as well as storing them (in memory) for post processing. (The class itself is a job manager for concurrently executing commands on remote machines over ssh).
Currently the usage of the class looks something like:
job = SSHJobMan(hostlist, cmd)
job.start()
while not job.done():
for each in job.poll():
incrementally_process(job.results[each])
time.sleep(0.2) # or other more useful work
post_process(job.results)
An alernative usage model is:
job = SSHJobMan(hostlist, cmd)
job.wait() # implicitly performs a start()
process(job.results)
This all works fine for the current utility. However it does lack flexibility. For example I currently support a brief output format or a progress bar as incremental results, I also support
brief, complete and "merged message" outputs for the post_process() function.
However, I'd like to support multiple results/output streams (progress bar to the terminal, debugging and warnings to a log file, outputs from successful jobs to one file/directory, error messages and other results from non-successful jobs to another, etc).
This sounds like a situation that calls for Observer ... have instances of my class accept registration from other objects and call them back with specific types of events as they occur.
I'm looking at PyPubSub since I saw several references to that in SO related questions. I'm not sure I'm ready to add the external dependency to my utility but I could see value in using their interface as a model for mine if that's going to make it easier for others to use. (The project is intended as both a standalone command line utility and a class for writing other scripts/utilities).
In short I know how to do what I want ... but there are numerous ways to accomplish it. I want suggestions on what's most likely to work for other users of the code in the long run.
The code itself is at: classh.
However it does lack flexibility.
Well... actually, this looks like a good design to me if an asynchronous API is what you want. It usually is. Maybe all you need is to switch from stderr to Python's logging module, which has a sort of publish/subscribe model of its own, what with Logger.addHandler() and so on.
If you do want to support observers, my advice is to keep it simple. You really only need a few lines of code.
class Event(object):
pass
class Observable(object):
def __init__(self):
self.callbacks = []
def subscribe(self, callback):
self.callbacks.append(callback)
def fire(self, **attrs):
e = Event()
e.source = self
for k, v in attrs.items():
setattr(e, k, v)
for fn in self.callbacks:
fn(e)
Your Job class can subclass Observable. When something of interest happens, call self.fire(type="progress", percent=50) or the like.
I think people in the other answers overdo it. You can easily achieve events in Python with less than 15 lines of code.
You simple have two classes: Event and Observer. Any class that wants to listen for an event, needs to inherit Observer and set to listen (observe) for a specific event. When an Event is instantiated and fired, all observers listening to that event will run the specified callback functions.
class Observer():
_observers = []
def __init__(self):
self._observers.append(self)
self._observables = {}
def observe(self, event_name, callback):
self._observables[event_name] = callback
class Event():
def __init__(self, name, data, autofire = True):
self.name = name
self.data = data
if autofire:
self.fire()
def fire(self):
for observer in Observer._observers:
if self.name in observer._observables:
observer._observables[self.name](self.data)
Example:
class Room(Observer):
def __init__(self):
print("Room is ready.")
Observer.__init__(self) # Observer's init needs to be called
def someone_arrived(self, who):
print(who + " has arrived!")
room = Room()
room.observe('someone arrived', room.someone_arrived)
Event('someone arrived', 'Lenard')
Output:
Room is ready.
Lenard has arrived!
A few more approaches...
Example: the logging module
Maybe all you need is to switch from stderr to Python's logging module, which has a powerful publish/subscribe model.
It's easy to get started producing log records.
# producer
import logging
log = logging.getLogger("myjobs") # that's all the setup you need
class MyJob(object):
def run(self):
log.info("starting job")
n = 10
for i in range(n):
log.info("%.1f%% done" % (100.0 * i / n))
log.info("work complete")
On the consumer side there's a bit more work. Unfortunately configuring logger output takes, like, 7 whole lines of code to do. ;)
# consumer
import myjobs, sys, logging
if user_wants_log_output:
ch = logging.StreamHandler(sys.stderr)
ch.setLevel(logging.INFO)
formatter = logging.Formatter(
"%(asctime)s - %(name)s - %(levelname)s - %(message)s")
ch.setFormatter(formatter)
myjobs.log.addHandler(ch)
myjobs.log.setLevel(logging.INFO)
myjobs.MyJob().run()
On the other hand there's an amazing amount of stuff in the logging package. If you ever need to send log data to a rotating set of files, an email address, and the Windows Event Log, you're covered.
Example: simplest possible observer
But you don't need to use any library at all. An extremely simple way to support observers is to call a method that does nothing.
# producer
class MyJob(object):
def on_progress(self, pct):
"""Called when progress is made. pct is the percent complete.
By default this does nothing. The user may override this method
or even just assign to it."""
pass
def run(self):
n = 10
for i in range(n):
self.on_progress(100.0 * i / n)
self.on_progress(100.0)
# consumer
import sys, myjobs
job = myjobs.MyJob()
job.on_progress = lambda pct: sys.stdout.write("%.1f%% done\n" % pct)
job.run()
Sometimes instead of writing a lambda, you can just say job.on_progress = progressBar.update, which is nice.
This is about as simple as it gets. One drawback is that it doesn't naturally support multiple listeners subscribing to the same events.
Example: C#-like events
With a bit of support code, you can get C#-like events in Python. Here's the code:
# glue code
class event(object):
def __init__(self, func):
self.__doc__ = func.__doc__
self._key = ' ' + func.__name__
def __get__(self, obj, cls):
try:
return obj.__dict__[self._key]
except KeyError, exc:
be = obj.__dict__[self._key] = boundevent()
return be
class boundevent(object):
def __init__(self):
self._fns = []
def __iadd__(self, fn):
self._fns.append(fn)
return self
def __isub__(self, fn):
self._fns.remove(fn)
return self
def __call__(self, *args, **kwargs):
for f in self._fns[:]:
f(*args, **kwargs)
The producer declares the event using a decorator:
# producer
class MyJob(object):
#event
def progress(pct):
"""Called when progress is made. pct is the percent complete."""
def run(self):
n = 10
for i in range(n+1):
self.progress(100.0 * i / n)
#consumer
import sys, myjobs
job = myjobs.MyJob()
job.progress += lambda pct: sys.stdout.write("%.1f%% done\n" % pct)
job.run()
This works exactly like the "simple observer" code above, but you can add as many listeners as you like using +=. (Unlike C#, there are no event handler types, you don't have to new EventHandler(foo.bar) when subscribing to an event, and you don't have to check for null before firing the event. Like C#, events do not squelch exceptions.)
How to choose
If logging does everything you need, use that. Otherwise do the simplest thing that works for you. The key thing to note is that you don't need to take on a big external dependency.
How about an implementation where objects aren't kept alive just because they're observing something? Below please find an implementation of the observer pattern with the following features:
Usage is pythonic. To add an observer to a bound method .bar of instance foo, just do foo.bar.addObserver(observer).
Observers are not kept alive by virtue of being observers. In other words, the observer code uses no strong references.
No sub-classing necessary (descriptors ftw).
Can be used with unhashable types.
Can be used as many times you want in a single class.
(bonus) As of today the code exists in a proper downloadable, installable package on github.
Here's the code (the github package or PyPI package have the most up to date implementation):
import weakref
import functools
class ObservableMethod(object):
"""
A proxy for a bound method which can be observed.
I behave like a bound method, but other bound methods can subscribe to be
called whenever I am called.
"""
def __init__(self, obj, func):
self.func = func
functools.update_wrapper(self, func)
self.objectWeakRef = weakref.ref(obj)
self.callbacks = {} #observing object ID -> weak ref, methodNames
def addObserver(self, boundMethod):
"""
Register a bound method to observe this ObservableMethod.
The observing method will be called whenever this ObservableMethod is
called, and with the same arguments and keyword arguments. If a
boundMethod has already been registered to as a callback, trying to add
it again does nothing. In other words, there is no way to sign up an
observer to be called back multiple times.
"""
obj = boundMethod.__self__
ID = id(obj)
if ID in self.callbacks:
s = self.callbacks[ID][1]
else:
wr = weakref.ref(obj, Cleanup(ID, self.callbacks))
s = set()
self.callbacks[ID] = (wr, s)
s.add(boundMethod.__name__)
def discardObserver(self, boundMethod):
"""
Un-register a bound method.
"""
obj = boundMethod.__self__
if id(obj) in self.callbacks:
self.callbacks[id(obj)][1].discard(boundMethod.__name__)
def __call__(self, *arg, **kw):
"""
Invoke the method which I proxy, and all of it's callbacks.
The callbacks are called with the same *args and **kw as the main
method.
"""
result = self.func(self.objectWeakRef(), *arg, **kw)
for ID in self.callbacks:
wr, methodNames = self.callbacks[ID]
obj = wr()
for methodName in methodNames:
getattr(obj, methodName)(*arg, **kw)
return result
#property
def __self__(self):
"""
Get a strong reference to the object owning this ObservableMethod
This is needed so that ObservableMethod instances can observe other
ObservableMethod instances.
"""
return self.objectWeakRef()
class ObservableMethodDescriptor(object):
def __init__(self, func):
"""
To each instance of the class using this descriptor, I associate an
ObservableMethod.
"""
self.instances = {} # Instance id -> (weak ref, Observablemethod)
self._func = func
def __get__(self, inst, cls):
if inst is None:
return self
ID = id(inst)
if ID in self.instances:
wr, om = self.instances[ID]
if not wr():
msg = "Object id %d should have been cleaned up"%(ID,)
raise RuntimeError(msg)
else:
wr = weakref.ref(inst, Cleanup(ID, self.instances))
om = ObservableMethod(inst, self._func)
self.instances[ID] = (wr, om)
return om
def __set__(self, inst, val):
raise RuntimeError("Assigning to ObservableMethod not supported")
def event(func):
return ObservableMethodDescriptor(func)
class Cleanup(object):
"""
I manage remove elements from a dict whenever I'm called.
Use me as a weakref.ref callback to remove an object's id from a dict
when that object is garbage collected.
"""
def __init__(self, key, d):
self.key = key
self.d = d
def __call__(self, wr):
del self.d[self.key]
To use this we just decorate methods we want to make observable with #event. Here's an example
class Foo(object):
def __init__(self, name):
self.name = name
#event
def bar(self):
print("%s called bar"%(self.name,))
def baz(self):
print("%s called baz"%(self.name,))
a = Foo('a')
b = Foo('b')
a.bar.addObserver(b.bar)
a.bar()
From wikipedia:
from collections import defaultdict
class Observable (defaultdict):
def __init__ (self):
defaultdict.__init__(self, object)
def emit (self, *args):
'''Pass parameters to all observers and update states.'''
for subscriber in self:
response = subscriber(*args)
self[subscriber] = response
def subscribe (self, subscriber):
'''Add a new subscriber to self.'''
self[subscriber]
def stat (self):
'''Return a tuple containing the state of each observer.'''
return tuple(self.values())
The Observable is used like this.
myObservable = Observable ()
# subscribe some inlined functions.
# myObservable[lambda x, y: x * y] would also work here.
myObservable.subscribe(lambda x, y: x * y)
myObservable.subscribe(lambda x, y: float(x) / y)
myObservable.subscribe(lambda x, y: x + y)
myObservable.subscribe(lambda x, y: x - y)
# emit parameters to each observer
myObservable.emit(6, 2)
# get updated values
myObservable.stat() # returns: (8, 3.0, 4, 12)
Based on Jason's answer, I implemented the C#-like events example as a fully-fledged python module including documentation and tests. I love fancy pythonic stuff :)
So, if you want some ready-to-use solution, you can just use the code on github.
Example: twisted log observers
To register an observer yourCallable() (a callable that accepts a dictionary) to receive all log events (in addition to any other observers):
twisted.python.log.addObserver(yourCallable)
Example: complete producer/consumer example
From Twisted-Python mailing list:
#!/usr/bin/env python
"""Serve as a sample implementation of a twisted producer/consumer
system, with a simple TCP server which asks the user how many random
integers they want, and it sends the result set back to the user, one
result per line."""
import random
from zope.interface import implements
from twisted.internet import interfaces, reactor
from twisted.internet.protocol import Factory
from twisted.protocols.basic import LineReceiver
class Producer:
"""Send back the requested number of random integers to the client."""
implements(interfaces.IPushProducer)
def __init__(self, proto, cnt):
self._proto = proto
self._goal = cnt
self._produced = 0
self._paused = False
def pauseProducing(self):
"""When we've produced data too fast, pauseProducing() will be
called (reentrantly from within resumeProducing's transport.write
method, most likely), so set a flag that causes production to pause
temporarily."""
self._paused = True
print('pausing connection from %s' % (self._proto.transport.getPeer()))
def resumeProducing(self):
self._paused = False
while not self._paused and self._produced < self._goal:
next_int = random.randint(0, 10000)
self._proto.transport.write('%d\r\n' % (next_int))
self._produced += 1
if self._produced == self._goal:
self._proto.transport.unregisterProducer()
self._proto.transport.loseConnection()
def stopProducing(self):
pass
class ServeRandom(LineReceiver):
"""Serve up random data."""
def connectionMade(self):
print('connection made from %s' % (self.transport.getPeer()))
self.transport.write('how many random integers do you want?\r\n')
def lineReceived(self, line):
cnt = int(line.strip())
producer = Producer(self, cnt)
self.transport.registerProducer(producer, True)
producer.resumeProducing()
def connectionLost(self, reason):
print('connection lost from %s' % (self.transport.getPeer()))
factory = Factory()
factory.protocol = ServeRandom
reactor.listenTCP(1234, factory)
print('listening on 1234...')
reactor.run()
OP asks "Are there any exemplary examples of the GoF Observer implemented in Python?"
This is an example in Python 3.7. This Observable class meets the requirement of creating a relationship between one observable and many observers while remaining independent of their structure.
from functools import partial
from dataclasses import dataclass, field
import sys
from typing import List, Callable
#dataclass
class Observable:
observers: List[Callable] = field(default_factory=list)
def register(self, observer: Callable):
self.observers.append(observer)
def deregister(self, observer: Callable):
self.observers.remove(observer)
def notify(self, *args, **kwargs):
for observer in self.observers:
observer(*args, **kwargs)
def usage_demo():
observable = Observable()
# Register two anonymous observers using lambda.
observable.register(
lambda *args, **kwargs: print(f'Observer 1 called with args={args}, kwargs={kwargs}'))
observable.register(
lambda *args, **kwargs: print(f'Observer 2 called with args={args}, kwargs={kwargs}'))
# Create an observer function, register it, then deregister it.
def callable_3():
print('Observer 3 NOT called.')
observable.register(callable_3)
observable.deregister(callable_3)
# Create a general purpose observer function and register four observers.
def callable_x(*args, **kwargs):
print(f'{args[0]} observer called with args={args}, kwargs={kwargs}')
for gui_field in ['Form field 4', 'Form field 5', 'Form field 6', 'Form field 7']:
observable.register(partial(callable_x, gui_field))
observable.notify('test')
if __name__ == '__main__':
sys.exit(usage_demo())
A functional approach to observer design:
def add_listener(obj, method_name, listener):
# Get any existing listeners
listener_attr = method_name + '_listeners'
listeners = getattr(obj, listener_attr, None)
# If this is the first listener, then set up the method wrapper
if not listeners:
listeners = [listener]
setattr(obj, listener_attr, listeners)
# Get the object's method
method = getattr(obj, method_name)
#wraps(method)
def method_wrapper(*args, **kwags):
method(*args, **kwags)
for l in listeners:
l(obj, *args, **kwags) # Listener also has object argument
# Replace the original method with the wrapper
setattr(obj, method_name, method_wrapper)
else:
# Event is already set up, so just add another listener
listeners.append(listener)
def remove_listener(obj, method_name, listener):
# Get any existing listeners
listener_attr = method_name + '_listeners'
listeners = getattr(obj, listener_attr, None)
if listeners:
# Remove the listener
next((listeners.pop(i)
for i, l in enumerate(listeners)
if l == listener),
None)
# If this was the last listener, then remove the method wrapper
if not listeners:
method = getattr(obj, method_name)
delattr(obj, listener_attr)
setattr(obj, method_name, method.__wrapped__)
These methods can then be used to add a listener to any class method. For example:
class MyClass(object):
def __init__(self, prop):
self.prop = prop
def some_method(self, num, string):
print('method:', num, string)
def listener_method(obj, num, string):
print('listener:', num, string, obj.prop)
my = MyClass('my_prop')
add_listener(my, 'some_method', listener_method)
my.some_method(42, 'with listener')
remove_listener(my, 'some_method', listener_method)
my.some_method(42, 'without listener')
And the output is:
method: 42 with listener
listener: 42 with listener my_prop
method: 42 without listener