使用多处理和线程并行处理非常大的文本文件

Posted 2023-02-16

【中文标题】使用多处理和线程并行处理非常大的文本文件

【英文标题】：processing very large text files in parallel using multiprocessing and threading

【发布时间】：2020-10-01 11:51:09

【问题描述】：

我发现了其他几个与该主题相关的问题，但没有一个与我的情况非常相似。

我有几个非常大的文本文件（大小超过 3 GB）。

我想使用multiprocessing 并行处理它们（比如 2 个文档）。作为我处理的一部分（在单个进程中），我需要进行 API 调用，因此希望每个进程都有自己的 threads 以异步运行。

我想出了一个简化的例子（我已经对代码进行了注释，试图解释我认为它应该做什么）：

import multiprocessing
from threading import Thread
import threading
from queue import Queue
import time


def process_huge_file(*, file_, batch_size=250, num_threads=4):
    # create  APICaller instance for each process that has it's own Queue
    api_call = APICaller()

    batch = []

    # create threads that will run asynchronously to make API calls
    # I expect these to immediately block since there is nothing in the Queue (which is was
    # the api_call.run depends on to make a call 
    threads = []
    for i in range(num_threads):
        thread = Thread(target=api_call.run)
        threads.append(thread)
        thread.start()

    for thread in threads:
        thread.join()
    ####
    # start processing the file line by line
    for line in file_:
        # if we are at our batch size, add the batch to the api_call to to let the threads do 
        # their api calling 
        if i % batch_size == 0:
            api_call.queue.put(batch)
        else:
        # add fake line to batch
            batch.append(fake_line)


class APICaller:
    def __init__(self):
    # thread safe queue to feed the threads which point at instances
    of these APICaller objects
        self.queue = Queue()

    def run(self):
        print("waiting for something to do")
        self.queue.get()
        print("processing item in queue")
        time.sleep(0.1)
        print("finished processing item in queue")




if __name__ == "__main__":
    # fake docs
    fake_line = "this is a fake line of some text"
    # two fake docs with line length == 1000
    fake_docs = [[fake_line] * 1000 for i in range(2)]
    ####
    num_processes = 2
    procs = []
    for idx, doc in enumerate(fake_docs):
        proc = multiprocessing.Process(target=process_huge_file, kwargs=dict(file_=doc))
        proc.start()
        procs.append(proc)

    for proc in procs:
        proc.join() 

正如现在的代码，“等待某事做”打印 8 次（每个进程有 4 个线程有意义）然后它停止或“死锁”，这不是我所期望的 - 我希望它开始与一旦我开始将项目放入队列中，线程就开始了，但代码似乎并没有做到这一点。我通常会逐步找到一个挂断，但我仍然不了解如何使用Threads 进行最佳调试（另一天的另一个主题）。

与此同时，有人可以帮我弄清楚为什么我的代码没有做它应该做的事情吗？

【问题讨论】：

在process_huge_file 函数中，在for line in file_ 循环之后而不是之前加入线程。

【参考方案1】：

我已经做了一些调整和添加，代码似乎可以做到现在应该做的事情。主要调整是：添加一个CloseableQueue 类（来自 Brett Slatkins Effective Python Item 55），并确保我调用 close 并加入队列，以便线程正确退出。包含以下更改的完整代码：

import multiprocessing
from threading import Thread
import threading
from queue import Queue
import time

from concurrency_utils import CloseableQueue


def sync_process_huge_file(*, file_, batch_size=250):
    batch = []
    for idx, line in enumerate(file_):
        # do processing on the text
        if idx % batch_size == 0:
            time.sleep(0.1)
            batch = []
            # api_call.queue.put(batch)
        else:
            computation = 0
            for i in range(100000):
                computation += i
            batch.append(line)


def process_huge_file(*, file_, batch_size=250, num_threads=4):
    api_call = APICaller()

    batch = []

    # api call threads
    threads = []
    for i in range(num_threads):
        thread = Thread(target=api_call.run)
        threads.append(thread)
        thread.start()

    for idx, line in enumerate(file_):
        # do processing on the text
        if idx % batch_size == 0:
            api_call.queue.put(batch)
        else:
            computation = 0
            for i in range(100000):
                computation += i
            batch.append(line)

    for _ in threads:
        api_call.queue.close()
    api_call.queue.join()

    for thread in threads:
        thread.join()


class APICaller:
    def __init__(self):
        self.queue = CloseableQueue()

    def run(self):
        for item in self.queue:
            print("waiting for something to do")
            pass
            print("processing item in queue")
            time.sleep(0.1)
            print("finished processing item in queue")
        print("exiting run")


if __name__ == "__main__":
    # fake docs
    fake_line = "this is a fake line of some text"
    # two fake docs with line length == 1000
    fake_docs = [[fake_line] * 10000 for i in range(2)]
    ####
    time_s = time.time()
    num_processes = 2
    procs = []
    for idx, doc in enumerate(fake_docs):
        proc = multiprocessing.Process(target=process_huge_file, kwargs=dict(file_=doc))
        proc.start()
        procs.append(proc)

    for proc in procs:
        proc.join()

    time_e = time.time()

    print(f"took time_e-time_s ")


class CloseableQueue(Queue):
    SENTINEL = object()

    def __init__(self, **kwargs):
        super().__init__(**kwargs)

    def close(self):
        self.put(self.SENTINEL)

    def __iter__(self):
        while True:
            item = self.get()
            try:
                if item is self.SENTINEL:
                    return  # exit thread
                yield item
            finally:
                self.task_done()

正如预期的那样，这是同步运行的巨大加速 - 120 秒与 50 秒。