Processes and Threading in Java

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def __init__(self, work_queue, word, number): super()__init__() selfwork_queue = work_queue selfword = word selfnumber = number def run(self): while True: try: filename = selfwork_queueget() selfprocess(filename) finally: selfwork_queuetask_done()
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The __init__() method must call the base class __init__() The work queue is the same queueQueue shared by all the threads We have made the run() method an in nite loop This is common for daemon threads, and makes sense here because we don t know how many les the thread must process At each iteration we call queueQueueget() to get the next le to work on This call will block if the queue is empty, and does not have to be protected by a lock because queueQueue handles that automatically for us Once we have a le we process it, and afterward we must tell the queue that we have done that particular job calling queueQueuetask_done() is essential to the correct working of queueQueuejoin() We have not shown the process() function, because apart from the def line, the code is the same as the code used in grepword-p-childpy from the previous = "" line to the end (443 ) One nal point to note is that included with the book s examples is grepwordmpy, a program that is almost identical to the grepword-tpy program reviewed here, but which uses the multiprocessing module rather than the threading module The code has just three differences: rst, we import multiprocessing instead of queue and threading; second, the Worker class inherits multiprocessingProcess instead of threadingThread; and third, the work queue is a multiprocessingJoinableQueue instead of a queueQueue The multiprocessing module provides thread-like functionality using forking on systems that support it (Unix), and child processes on those that don t (Windows), so locking mechanisms are not always required, and the processes will run on whatever processor cores the operating system has available The package provides several ways of passing data between processes, including using a queue that can be used to provide work for processes just like queueQueue can be used to provide work for threads
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The chief bene t of the multiprocessing version is that it can potentially run faster on multicore machines than the threaded version since it can run its processes on as many cores as are available Compare this with the standard Python interpreter (written in C, sometimes called CPython) which has a GIL (Global Interpreter Lock) that means that only one thread can execute Python code at any one time This restriction is an implementation detail and does not necessarily apply to other Python interpreters such as Jython
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Example: A Threaded Find Duplicate Files Program
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The second threading example has a similar structure to the rst, but is more sophisticated in several ways It uses two queues, one for work and one for results, and has a separate results processing thread to output results as soon as they are available It also shows both a threadingThread subclass and calling threadingThread() with a function, and also uses a lock to serialize access to shared data (a dict) The findduplicates-tpy program is a more advanced version of the findduppy program from 5 It iterates over all the les in the current directory (or the speci ed path), recursively going into subdirectories It compares the lengths of all the les with the same name (just like findduppy), and for those les that have the same name and the same size it then uses the MD5 (Message Digest) algorithm to check whether the les are the same, reporting any that are We will start by looking at the main() function, split into four parts
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def main(): opts, path = parse_options() data = collectionsdefaultdict(list) for root, dirs, files in oswalk(path): for filename in files: fullname = ospathjoin(root, filename) try: key = (ospathgetsize(fullname), filename) except EnvironmentError: continue if key[0] == 0: continue data[key]append(fullname)
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For a brief explanation of why CPython uses a GIL see wwwpythonorg/doc/faq/library/#can-t-weget-rid-of-the-global-interpreter-lock and docspythonorg/api/threadshtml
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