线程队列挂起 Python

Question

我正在尝试通过队列使解析器成为多线程。它似乎有效，但我的队列挂起。如果有人能告诉我如何解决这个问题，我将不胜感激，因为我很少编写多线程代码。

此代码从问题中读取：

from silk import *
import json
import datetime
import pandas
import Queue
from threading import Thread

l = []
q = Queue.Queue()

def parse_record():
    d = {}
    while not q.empty():
        rec = q.get()
        d['timestamp'] = rec.stime.strftime("%Y-%m-%d %H:%M:%S")
        # ... many ops like this
        d['dport'] = rec.dport
        l.append(d) # l is global

这就填满了问题：

def parse_records():
    ffile = '/tmp/query.rwf'
    flows = SilkFile(ffile, READ)
    numthreads = 2

    # fill queue
    for rec in flows:
        q.put(rec)
    # work on Queue    
    for i in range(numthreads):
        t = Thread(target = parse_record)
        t.daemon = True
        t.start()

    # blocking
    q.join()

    # never reached    
    data_df = pandas.DataFrame.from_records(l)
    return data_df

我只在 main 中调用 parse_records()。它永远不会终止。

Answer 1

Queue.empty doc 说：

...if empty() returns False it doesn’t guarantee that a subsequent call to get() will not block.

您至少应该使用 get_nowait 否则可能会丢失数据。但更重要的是，仅当所有排队的项目都已通过 Queue.task_done 调用标记为完成时，连接才会释放：

If a join() is currently blocking, it will resume when all items have been processed (meaning that a task_done() call was received for every item that had been put() into the queue).

附带说明，l.append(d) 不是原子的，应该用锁保护。

from silk import *
import json
import datetime
import pandas
import Queue
from threading import Thread, Lock

l = []
l_lock = Lock()
q = Queue.Queue()

def parse_record():
    d = {}
    while 1:
        try:
            rec = q.getnowait()
            d['timestamp'] = rec.stime.strftime("%Y-%m-%d %H:%M:%S")
            # ... many ops like this
            d['dport'] = rec.dport
            with l_lock():
                l.append(d) # l is global
            q.task_done()
        except Queue.Empty:
            return

您可以通过使用标准库中的线程池来大大缩短您的代码。

from silk import *
import json
import datetime
import pandas
import multiprocessing.pool

def parse_record(rec):
    d = {}
    d['timestamp'] = rec.stime.strftime("%Y-%m-%d %H:%M:%S")
    # ... many ops like this
    d['dport'] = rec.dport
    return d

def parse_records():
    ffile = '/tmp/query.rwf'
    flows = SilkFile(ffile, READ)
    pool = multiprocessing.pool.Pool(2)
    data_df = pandas.DataFrame.from_records(pool.map(parse_record), flows)
    pool.close()
    return data_df

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