# SuperFastPython.com # example of logging from multiple workers in the multiprocessing pool from random import random from time import sleep from multiprocessing import current_process from multiprocessing import Pool from multiprocessing import Queue from multiprocessing import Manager from logging.handlers import QueueHandler import logging # executed in a process that performs logging def logger_process(queue): # create a logger logger = logging.getLogger('app') # configure a stream handler logger.addHandler(logging.StreamHandler()) # log all messages, debug and up logger.setLevel(logging.DEBUG) # report that the logger process is running logger.info(f'Logger process running.') # run forever while True: # consume a log message, block until one arrives message = queue.get() # check for shutdown if message is None: logger.info(f'Logger process shutting down.') break # log the message logger.handle(message) # task to be executed in child processes def task(queue): # create a logger logger = logging.getLogger('app') # add a handler that uses the shared queue logger.addHandler(QueueHandler(queue)) # log all messages, debug and up logger.setLevel(logging.DEBUG) # get the current process process = current_process() # report initial message logger.info(f'Child {process.name} starting.') # simulate doing work for i in range(5): # report a message logger.debug(f'Child {process.name} step {i}.') # block sleep(random()) # report final message logger.info(f'Child {process.name} done.') # protect the entry point if __name__ == '__main__': # create the manager with Manager() as manager: # create the shared queue and get the proxy object queue = manager.Queue() # create a logger logger = logging.getLogger('app') # add a handler that uses the shared queue logger.addHandler(QueueHandler(queue)) # log all messages, debug and up logger.setLevel(logging.DEBUG) # create the process pool with default configuration with Pool() as pool: # issue a long running task to receive logging messages _ = pool.apply_async(logger_process, args=(queue,)) # report initial message logger.info('Main process started.') # issue task to the process pool results = [pool.apply_async(task, args=(queue,)) for i in range(5)] # wait for all issued tasks to complete logger.info('Main process waiting...') for result in results: result.wait() # report final message logger.info('Main process done.') # shutdown the long running logger task queue.put(None) # close the process pool pool.close() # wait for all tasks to complete (e.g. the logger to close) pool.join()
Write, Run & Share Python code online using OneCompiler's Python online compiler for free. It's one of the robust, feature-rich online compilers for python language, supporting both the versions which are Python 3 and Python 2.7. Getting started with the OneCompiler's Python editor is easy and fast. The editor shows sample boilerplate code when you choose language as Python or Python2 and start coding.
OneCompiler's python online editor supports stdin and users can give inputs to programs using the STDIN textbox under the I/O tab. Following is a sample python program which takes name as input and print your name with hello.
import sys
name = sys.stdin.readline()
print("Hello "+ name)
Python is a very popular general-purpose programming language which was created by Guido van Rossum, and released in 1991. It is very popular for web development and you can build almost anything like mobile apps, web apps, tools, data analytics, machine learning etc. It is designed to be simple and easy like english language. It's is highly productive and efficient making it a very popular language.
When ever you want to perform a set of operations based on a condition IF-ELSE is used.
if conditional-expression
#code
elif conditional-expression
#code
else:
#code
Indentation is very important in Python, make sure the indentation is followed correctly
For loop is used to iterate over arrays(list, tuple, set, dictionary) or strings.
mylist=("Iphone","Pixel","Samsung")
for i in mylist:
print(i)
While is also used to iterate a set of statements based on a condition. Usually while is preferred when number of iterations are not known in advance.
while condition
#code
There are four types of collections in Python.
List is a collection which is ordered and can be changed. Lists are specified in square brackets.
mylist=["iPhone","Pixel","Samsung"]
print(mylist)
Tuple is a collection which is ordered and can not be changed. Tuples are specified in round brackets.
myTuple=("iPhone","Pixel","Samsung")
print(myTuple)
Below throws an error if you assign another value to tuple again.
myTuple=("iPhone","Pixel","Samsung")
print(myTuple)
myTuple[1]="onePlus"
print(myTuple)
Set is a collection which is unordered and unindexed. Sets are specified in curly brackets.
myset = {"iPhone","Pixel","Samsung"}
print(myset)
Dictionary is a collection of key value pairs which is unordered, can be changed, and indexed. They are written in curly brackets with key - value pairs.
mydict = {
"brand" :"iPhone",
"model": "iPhone 11"
}
print(mydict)
Following are the libraries supported by OneCompiler's Python compiler
Name | Description |
---|---|
NumPy | NumPy python library helps users to work on arrays with ease |
SciPy | SciPy is a scientific computation library which depends on NumPy for convenient and fast N-dimensional array manipulation |
SKLearn/Scikit-learn | Scikit-learn or Scikit-learn is the most useful library for machine learning in Python |
Pandas | Pandas is the most efficient Python library for data manipulation and analysis |
Matplotlib | Matplotlib is a cross-platform, data visualization and graphical plotting library for Python programming and it's numerical mathematics extension NumPy |
DOcplex | DOcplex is IBM Decision Optimization CPLEX Modeling for Python, is a library composed of Mathematical Programming Modeling and Constraint Programming Modeling |