Threads in Python

Definition of a Thread

Threads
A Thread or a Thread of Execution is defined in computer science as the smallest unit that can be scheduled in an operating system. Threads are normally created by a fork of a computer script or program in two or more parallel (which is implemented on a single processor by multitasking) tasks. Threads are usually contained in processes. More than one thread can exist within the same process. These threads share the memory and the state of the process. In other words: They share the code or instructions and the values of its variables.

There are two different kind of threads: Kernel Threads are part of the operating system, while User-space threads are not implemented in the kernel.

In a certain way, user-space threads can be seen as an extension of the function concept of a programming language. So a thread user-space thread is similiar to a function or procedure call. But there are differences to regular functions, especially the return behaviour.

Threads and global Variables Every process has at least one thread, i.e. the process itself. A process can start multiple threads. The operating system executes these threads like parallel "processes". On a single processor machine, this parallelism is achieved by thread scheduling or timeslicing.

Advantages of Threading:

The handling of threads is simpler than the handling of processes for an operating system. That's why they are sometimes called light-weight process (LWP)

Threads in Python

There are two modules which support the usage of threads in Python: The module "thread" treats a thread as a function, while the module "threading" is implemented in an object oriented way, i.e. every thread corresponds to an object.

The thread Module

It's possible to execute functions in a separate thread with the module Thread. To do this, we can use the function thread.start_new_thread:

thread.start_new_thread(function, args[, kwargs])

This methode starts a new thread and return its identifier. The thread executes the function "function" (function is a reference to a function) with the argument list args (which must be a list or a tuple). The optional kwargs argument specifies a dictionary of keyword arguments. When the function returns, the thread silently exits. When the function terminates with an unhandled exception, a stack trace is printed and then the thread exits (but other threads continue to run).

Example for a Thread in Python:
from thread import start_new_thread

def heron(a):
    """Calculates the square root of a"""
    eps = 0.0000001
    old = 1
    new = 1
    while True:
        old,new = new, (new + a/new) / 2.0
        print old, new
        if abs(new - old) < eps:
            break
    return new

start_new_thread(heron,(99,))
start_new_thread(heron,(999,))
start_new_thread(heron,(1733,))

c = raw_input("Type something to quit.")
The raw_input() in the previous example is necessary, because otherwise all the threads would be exited, if the main program finishes. raw_input() waits until something has been typed in.

We expand the previous example with counters for the threads.
from thread import start_new_thread

num_threads = 0
def heron(a):
    global num_threads
    num_threads += 1
    
    # code has been left out, see above
    num_threads -= 1
    return new

start_new_thread(heron,(99,))
start_new_thread(heron,(999,))
start_new_thread(heron,(1733,))
start_new_thread(heron,(17334,))

while num_threads > 0:
    pass
The script above doesn't work the way we might expect it to work. What is wrong?
The problem is, that the final while loop will be reached even before one of the threads could have incremented the counter num_threads.

But there is another serious problem:
The problem arises by the assignments to num_thread
num_threads += 1
and
num_threads -= 1
These assignment statements are not atomic. Such an assignment consists of three actions:
Errors like this happen in the case of increment assignments:
The first thread reads the variable num_threads, which still has the value 0. After having read this value, the thread is put to sleep by the operating system. Now it't the second thread's turn: It also reads the value of the variable num_threads, which is still 0, because the first thread has been put to sleep too early, i.e. before it had been able to increment its value by 1. Now the second thread is put to sleep. Now it't the third thread's turn, which again reads a 0, but the counter should have been 2 by now. Each of these threads assigns now the value 1 to the counter. Similiar problems occur with the decrement operation.

Solution

Problems of this kind can be solved by defining critical sections with lock objects. These sections will be treated atomically, i.e. during the execution of such a section a thread will not be interrupted or put to sleep.
The methode thread.allocate_lock is used to create a new lock object:

lock_objekt = thread.allocate_lock()

The beginning of a critical section is tagged with lock_object.acquire() and the end with lock_object.release().
The solution with locks looks like this:
from thread import start_new_thread, allocate_lock
num_threads = 0
thread_started = False
lock = allocate_lock()
def heron(a):
    global num_threads, thread_started
    lock.acquire()
    num_threads += 1
    thread_started = True
    lock.release()
    
    ...

    lock.acquire()
    num_threads -= 1
    lock.release()
    return new

start_new_thread(heron,(99,))
start_new_thread(heron,(999,))
start_new_thread(heron,(1733,))

while not thread_started:
    pass
while num_threads > 0:
    pass

threading Module

We want to introduce the threading module with an example. The Thread of the example doesn't do a lot, essentially it just sleeps for 5 seconds and then prints out a message:
import time
from threading import Thread

def sleeper(i):
    print "thread %d sleeps for 5 seconds" % i
    time.sleep(5)
    print "thread %d woke up" % i

for i in range(10):
    t = Thread(target=sleeper, args=(i,))
    t.start()
Method of operation of the threding.Thread class: The class threading.Thread has a method start(), which can start a Thread. It triggers off the method run(), which has to be overloaded. The join() method makes sure that the main program waits until all threads have terminated.

The previous script returns the following output:
thread 0 sleeps for 5 seconds
thread 1 sleeps for 5 seconds
thread 2 sleeps for 5 seconds
thread 3 sleeps for 5 seconds
thread 4 sleeps for 5 seconds
thread 5 sleeps for 5 seconds
thread 6 sleeps for 5 seconds
thread 7 sleeps for 5 seconds
thread 8 sleeps for 5 seconds
thread 9 sleeps for 5 seconds
thread 1 woke up
thread 0 woke up
thread 3 woke up
thread 2 woke up
thread 5 woke up
thread 9 woke up
thread 8 woke up
thread 7 woke up
thread 6 woke up
thread 4 woke up
The next example shows a thread, which determines, if a number is prime or not. The Thread is defined with the threading module:
import threading 
 
class PrimeNumber(threading.Thread): 
  def __init__(self, number): 
    threading.Thread.__init__(self) 
    self.Number = number
 
  def run(self): 
    counter = 2 
    while counter*counter < self.Number: 
      if self.Number % counter == 0: 
        print "%d is no prime number, because %d = %d * %d" % ( self.Number, self.Number, counter, self.Number / counter) 
                return 
            counter += 1 
        print "%d is a prime number" % self.Number
threads = [] 
while True: 
    input = long(raw_input("number: ")) 
    if input < 1: 
        break 
 
    thread = PrimeNumber(input) 
    threads += [thread] 
    thread.start() 
 
for x in threads: 
    x.join()
With locks it should look like this:
class PrimeNumber(threading.Thread):
    prime_numbers = {} 
    lock = threading.Lock()
    
    def __init__(self, number): 
        threading.Thread.__init__(self) 
        self.Number = number
        PrimeNumber.lock.acquire() 
        PrimeNumber.prime_numbers[number] = "None" 
        PrimeNumber.lock.release() 
 
    def run(self): 
        counter = 2
        res = True
        while counter*counter < self.Number and res: 
            if self.Number % counter == 0: 
               res = False 
            counter += 1 
        PrimeNumber.lock.acquire() 
        PrimeNumber.prime_numbers[self.Number] = res 
        PrimeNumber.lock.release() 
threads = [] 
while True: 
    input = long(raw_input("number: ")) 
    if input < 1: 
        break 
 
    thread = PrimeNumber(input) 
    threads += [thread] 
    thread.start() 
 
for x in threads: 
    x.join()

Pinging with Threads

Ping in a network The previous examples of this chapter are of purely didactical interst, and have no practical applicabillity. The following example shows an interesting application, which can be easily used. If you want to determine in a local network which addresses are active or which computers are active, this script can be used. But you have to be careful with the range, because it can jam the network, if too many pings are started at once. Manually we would do the following for a network 192.168.178.x: We would ping the addresses 192.168.178.0, 192.168.178.1, 192.168.178.3 until 192.168.178.255 in turn. Every time we would have to wait a few seconds for the return values. This can be programmed in Python with a for loop over the address range of the IP addresses and a os.popen("ping -q -c2 "+ip,"r").

A solution without threads is highly inefficient, because the script will have to wait for every ping.

Solution with threads:
import os, re

received_packages = re.compile(r"(\d) received")
status = ("no response","alive but losses","alive")

for suffix in range(20,30):
   ip = "192.168.178."+str(suffix)
   ping_out = os.popen("ping -q -c2 "+ip,"r")
   print "... pinging ",ip
   while True:
      line = ping_out.readline()
      if not line: break
      n_received = received_packages.findall(line)
      if n_received:
         print ip + ": " + status[int(n_received[0])]
To understand this script, we have to look at the results of a ping on a shell command line:
$ ping -q -c2 192.168.178.26
PING 192.168.178.26 (192.168.178.26) 56(84) bytes of data.

--- 192.168.178.26 ping statistics ---
2 packets transmitted, 2 received, 0% packet loss, time 999ms
rtt min/avg/max/mdev = 0.022/0.032/0.042/0.010 ms
Falls ein Ping nicht zum Erfolg führt, gibt es folgende Ausgabe:
$ ping -q -c2 192.168.178.23
PING 192.168.178.23 (192.168.178.23) 56(84) bytes of data.

--- 192.168.178.23 ping statistics ---
2 packets transmitted, 0 received, +2 errors, 100% packet loss, time 1006ms

This is the fast solution with threads:
import os, re, threading

class ip_check(threading.Thread):
   def __init__ (self,ip):
      threading.Thread.__init__(self)
      self.ip = ip
      self.__successful_pings = -1
   def run(self):
      ping_out = os.popen("ping -q -c2 "+self.ip,"r")
      while True:
        line = ping_out.readline()
        if not line: break
        n_received = re.findall(received_packages,line)
        if n_received:
           self.__successful_pings = int(n_received[0])
   def status(self):
      if self.__successful_pings == 0:
         return "no response"
      elif self.__successful_pings == 1:
         return "alive, but 50 % package loss"
      elif self.__successful_pings == 2:
         return "alive"
      else:
         return "shouldn't occur"
received_packages = re.compile(r"(\d) received")

check_results = []
for suffix in range(20,70):
   ip = "192.168.178."+str(suffix)
   current = ip_check(ip)
   check_results.append(current)
   current.start()

for el in check_results:
   el.join()
   print "Status from ", el.ip,"is",el.status()