Threading queue hangs in Python

Question

I am trying to make a parser multi-threaded via a Queue. It seems to work, but my Queue is hanging. I'd appreciate if someone could tell me how to fix this, since I have rarely written multi-threaded code.

This code reads from the Q:

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

And this fills the Q:

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

I only call parse_records() in my main. It never terminates.

Answer 1

The Queue.empty doc says:

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

As a minimum you should use get_nowait or risk data loss. But more importantly, the join will only release when all of the queued items have been marked complete with a Queue.task_done call:

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).

As a side note, l.append(d) is not atomic and should be protected with a lock.

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

You could shorten your code considerably by using a thread pool from the standard libs.

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