如何在Python中并行运行os.walk？

Question

我在Java中编写了一个简单的应用程序，该应用程序采用路径列表并生成包含该原始列表下所有文件路径的文件。

如果我的paths.txt有：

c:\folder1\
c:\folder2\
...
...
c:\folder1000\

我的应用程序在多线程的每个路径上运行递归函数，并返回包含这些文件夹下所有文件路径的文件。

现在我想用Python编写这个应用程序。

我编写了一个简单的应用程序，它使用os.walk()来运行给定的文件夹并打印文件路径以输出。

现在我想并行运行它，我已经看到Python有一些模块： 多线程和多处理。

最好的做法是什么？在这种方式下，它是如何执行的？

Answer 1

以下是多处理解决方案：

from multiprocessing.pool import Pool
from multiprocessing import JoinableQueue as Queue
import os

def explore_path(path):
    directories = []
    nondirectories = []
    for filename in os.listdir(path):
        fullname = os.path.join(path, filename)
        if os.path.isdir(fullname):
            directories.append(fullname)
        else:
            nondirectories.append(filename)
    outputfile = path.replace(os.sep, '_') + '.txt'
    with open(outputfile, 'w') as f:
        for filename in nondirectories:
            print >> f, filename
    return directories

def parallel_worker():
    while True:
        path = unsearched.get()
        dirs = explore_path(path)
        for newdir in dirs:
            unsearched.put(newdir)
        unsearched.task_done()

# acquire the list of paths
with open('paths.txt') as f:
    paths = f.split()

unsearched = Queue()
for path in paths:
    unsearched.put(path)

pool = Pool(5)
for i in range(5):
    pool.apply_async(parallel_worker)

unsearched.join()
print 'Done'

Answer 2

这是python中的线程模式，对我有用。由于线程在CPython中的工作方式，我不确定线程​​是否会提高性能。

import threading
import Queue
import os

class PathThread (threading.Thread):
    def __init__(self, queue):
        threading.Thread.__init__(self)
        self.queue = queue

    def printfiles(self, p):
        for path, dirs, files in os.walk(p):
            for f in files:
                print path + "/" + f

    def run(self):
        while True:
            path = self.queue.get()
            self.printfiles(path)
            self.queue.task_done()

# threadsafe queue
pathqueue = Queue.Queue()
paths = ["foo", "bar", "baz"]

# spawn threads
for i in range(0, 5):
    t = PathThread(pathqueue)
    t.setDaemon(True)
    t.start()

# add paths to queue
for path in paths:
    pathqueue.put(path)

# wait for queue to get empty
pathqueue.join()

Answer 3

即使是线程也可以对目录遍历非常有帮助。我使用以下代码遍历SharePoint树，为大约50个线程获得非常显着的加速。 此特定程序返回目录结构中所有xml文件的（路径，数据）对，并且可以简单地扩展以供您使用。 （这是从我的程序中剪切并粘贴的;需要进行一些额外的编辑。）

#unique string for error passing error messages
ERROR = '\xffERROR\xff'

class ScanWorker(threading.Thread):
    """Worker class for scanning directory structures.
    pathQueue: queue for pathnames of directories
    resultQueue: results of processFile, pairs of (path, data) to be updated
    """
    lock = threading.Lock()
    dirCount = 0
    def __init__(self, pathQueue, resultQueue):
        self.pathQueue = pathQueue
        self.resultQueue = resultQueue
        super().__init__()

    def run(self):
        """Worker thread.
        Get a directory, process it, and put new directories on the
        queue."""
        try:
            while True:
                self.processDir(self.pathQueue.get())
                self.pathQueue.task_done()
        except Exception as e:
            #pass on exception to main thread
            description = traceback.format_exception(*sys.exc_info())
            description.insert(0,
                "Error in thread {}:\n".format(
                    threading.current_thread().name))
            self.resultQueue.put((ERROR, description))
            self.pathQueue.task_done()

    def processDir(self, top):
        """Visit a directory
        Call self.processFile on every file, and queue the directories.
        """
        #Wait and retry a few times in case of network errors.
        #SharePoint is not reliable, gives errors for no reason
        for retryCount in range(30):
            try:
                names = listdir(top)
                break
            except OSError as e:
                if e.errno in (2,22):
                    lastError = e
                    print(end="L", flush=True)
                    time.sleep(1)
                else:
                    raise
        else:
            print("List: too many retries")
            raise lastError
        #it is not important to worry about race conditions here
        self.__class__.dirCount += 1
        #process contents
        for name in names:
            if isdir(join(top, name)): self.pathQueue.put(join(top, name))
            else: self.processFile(join(top, name))

    def processFile(self, path):
        """Get XML file.
        """
        #only xml files
        if not path.lower().endswith('.xml'): return
        filemtime = datetime.fromtimestamp(getmtime(path))
        #SharePoint is not reliable, gives errors for no reason; just retry
        for retryCount in range(30):
            try:
                data = open(path,'rb').read()
                break
            except OSError as e:
                if e.errno in (2,22):
                    lastError = e
                    print(end="R", flush=True)
                    time.sleep(1)
                else:
                    raise
        else:
            print("Read: too many retries")
            raise lastError
        self.resultQueue.put((path, data))

class Scanner:
    """Interface to the ScanWorkers
    Sharepoint is pretty fast compared to its delay and handles 50 workers well
    Make sure you only create one instance of Scanner!
    """
    def __init__(self, workers):
        #don't restrict the path queue length; this causes deadlock
        #we use a LIFO queue to get more depth-first like search
        #reducing average queue length and hopefully improving server caching
        self.pathQueue = queue.LifoQueue()
        #this is the output queue to the main thread
        self.resultQueue = queue.Queue(5)
        self.workers = workers
        #start workers
        for i in range(workers):
            t = ScanWorker(self.pathQueue, self.resultQueue)
            t.setDaemon(True)
            t.start()

    def startWorkers(self, path):
        #add counter
        self.added = 0
        #and go
        self.pathQueue.put(path)

    def processResult(self, wait=True):
        """Get an element from the result queue, and add to the zip file."""
        path, data = self.resultQueue.get(block=wait)
        if path==ERROR:
            #process gave alarm; stop scanning
            #pass on description
            raise ScanError(data)
        <do whatever you want to do with the file>
        self.resultQueue.task_done()
        self.added += 1

#main
try:
    #set up
    scanner = Scanner(threads)
    scanner.startWorkers(rootpath)
    pathQueue, resultQueue = scanner.pathQueue, scanner.resultQueue
    #scanner is rolling; wait for it to finish
    with pathQueue.all_tasks_done:
        while pathQueue.unfinished_tasks:
            #tasks are still running
            #process results
            while True:
                try: scanner.processResult(wait=False)
                except queue.Empty: break
            #no new files found; check if scanner is ready
            done = pathQueue.all_tasks_done.wait(timeout=1)
            if not done:
                #Not yet; print something while we wait
                print(
                    "\rProcessed {} files from {} directories [{} {}]  "
                    .format(
                        scanner.added,
                        ScanWorker.dirCount,
                        pathQueue.unfinished_tasks,
                        resultQueue.unfinished_tasks,
                    ), end='\r')
    #just to make sure everybody is ready: join the path queue
    pathQueue.join()
    #process remaining of result queue
    while resultQueue.unfinished_tasks: scanner.processResult(wait=True)
    #go to new line to prevent overwriting progress messages
    print()
except ScanError as e:
    print()
    print(*e.args[0], end='')
    print("Process interrupted.")
except KeyboardInterrupt:
    print("\nProcess interrupted.")
print()