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Each key of the data default dictionary is a 2-tuple of (size, lename), where the lename does not include the path, and each value is a list of lenames (which do include their paths) Any items whose value list has more than one lename potentially has duplicates The dictionary is populated by iterating over all the les in the given path, but skipping any les we cannot get the size of (perhaps due to permissions problems, or because they are not normal les), and any that are of 0 size (since all zero length les are the same)
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work_queue = queuePriorityQueue() results_queue = queueQueue() md5_from_filename = {} for i in range(optscount): number = "{0}: "format(i + 1) if optsdebug else "" worker = Worker(work_queue, md5_from_filename, results_queue, number) workerdaemon = True workerstart()
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With all the data in place we are ready to create the worker threads We begin by creating a work queue and a results queue The work queue is a priority queue, so it will always return the lowest-priority items (in our case the smallest les) rst We also create a dictionary where each key is a lename (including its path) and where each value is the le s MD5 digest value The purpose of the dictionary is to ensure that we never compute the MD5 of the same le more than once (since the computation is expensive) With the shared data collections in place we loop as many times as there are threads to create (by default, seven times) The Worker subclass is similar to the one we created before, only this time we pass both queues and the MD5 dictionary As before, we start each worker straight away and each will be blocked until a work item becomes available
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results_thread = threadingThread( target=lambda: print_results(results_queue)) results_threaddaemon = True results_threadstart()
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Rather than creating a threadingThread subclass to process the results we have created a function and we pass that to threadingThread() The return value is a custom thread that will call the given function once the thread is started We pass the results queue (which is, of course, empty), so the thread will block immediately At this point we have created all the worker threads and the results thread and they are all blocked waiting for work
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for size, filename in sorted(data):
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names = data[size, filename] if len(names) > 1: work_queueput((size, names)) work_queuejoin() results_queuejoin()
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We now iterate over the data, and for each (size, lename) 2-tuple that has a list of two or more potentially duplicate les, we add the size and the lenames with paths as an item of work to the work queue Since the queue is a class from the queue module we don t have to worry about locking Finally we join the work queue and results queue to block until they are empty This ensures that the program runs until all the work is done and all the results have been output, and then terminates cleanly
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def print_results(results_queue): while True: try: results = results_queueget() if results: print(results) finally: results_queuetask_done()
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This function is passed as an argument to threadingThread() and is called when the thread it is given to is started It has an in nite loop because it is to be used as a daemon thread All it does is get results (a multiline string), and if the string is nonempty, it prints it for as long as results are available The beginning of the Worker class is similar to what we had before:
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class Worker(threadingThread): Md5_lock = threadingLock() def __init__(self, work_queue, md5_from_filename, results_queue, number): super()__init__() selfwork_queue = work_queue selfmd5_from_filename = md5_from_filename selfresults_queue = results_queue selfnumber = number def run(self): while True: try: size, names = selfwork_queueget() selfprocess(size, names)
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