Python multithreading

CHAPTER – 4
THE BASICS OF SEARCH ENGINE FRIENDLY DESIGN & DEVELOPMENT

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Python Threading
In the previous article, you have seen the threading methods. In this article, you
will see daemon threads and locks.
Daemon Thread:
So far, we have created the non-daemon thread. What is the daemon
thread? When the main thread exits, it attempts to terminate all of its daemonic
child threads.
Consider an example of GUI as shown below

Consider, by GUI input some calculation is being performed in the background and the
calculation is taking its time. If you click close button two courses of action can be
performed.
1.After clicking the close button the whole GUI window exists.
2.After clicking the close button the GUI window will wait for the completion of
background calculation.

If the first course of action is performed then Daemon thread are being used in
background calculation. If the second course of action is performed then a Non-
daemon thread is being used in background calculation.
Let us understand with the help of code
import threading
import time
def n():
print ("Non deamon start")
print ("NOn daemoon exit")

def d():
print (" daemon start")
time.sleep(5)
print (" daemon stop")
t = threading.Thread(name = "non-daemon",target=n)
d = threading.Thread(name = "daemon",target=d)
d.setDaemon(True)
d.start()
t.start()

If method isDaemon() returns True then the thread is a daemon thread. The syntax
d.setDaemon(True) or d.daemon = True can be used to make daemon thread.
Let us see the output:

Daemon thread will take 5 seconds to complete its task, but main did not wait for
the daemon thread. That’s why in the output there is no “daemon stop”
statement. Now remove the time. Sleep(5) from function d() and add it in n()
function.
See the code below.

import threading
import time
def n():
print ("Non deamon start")
time.sleep(5)
print ("NOn daemoon exit")
def d():
print (" daemon start")
print (" daemon stop")
t = threading.Thread(name = "non-daemon",target=n)
d = threading.Thread(name = "daemon",target=d)

d.setDaemon(True)
d.start()
t.start()
See the output:

In the above example, all print statements are executed. The main thread had to wait
for the non-daemon process.
Note: If you use join statement for Daemon thread then the main thread has to wait
for the completion of Daemon thread’s task.
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Locks
Locks are the most fundamental synchronization mechanism provided by the
threading module. A lock is in one of two states, locked or unlocked. If a thread
attempts to hold a lock that’s already held by some other thread, execution of the
second thread is halted until the lock is released.
lock.acquire ():
Acquire a lock, blocks others until True (Default )
lock.locked():
Returns True if lock is locked, otherwise False.
lock.release():
Unlocks the lock

Let us see one example.
import threading
import time
lock = threading.Lock()
list1 = []
def fun1(a):
lock.acquire()
list1.append(a)
lock.release()
for each in range(10):

thread1 = threading.Thread(target=fun1, args=(each,))
thread1.start()
print ("List1 is : ", list1)
The lock = threading.Lock() is used to create a lock object.
The main problem with the lock is, the lock does not remember which thread
acquired the lock. Now two problem can be aroused.

See the code below.
import threading
import time
import datetime
t1 = datetime.datetime.now()
def second(n):
lock.acquire()
print (n)
def third():

time.sleep(5)
lock.release()
print ("Thread3 ")
th1 = threading.Thread(target= second, args=("Thread1",))
th1.start()
th2 = threading.Thread(target= second, args=("Thread2",))
th2.start()
th3 = threading.Thread(target= third)
th3.start()

th1.join()
th2.join()
th3.join()
print ("Total time", t2-t1)
In the above code, a lock is acquired by thread1 and released by thread3. The
thread2 is trying to acquire the lock.
Let us see the output.

From the sequence of execution, it is clear that the lock acquired by the thread1 got
released by the thread3.
Let see second problem.

import threading
def first(n):
lock.acquire()
a =12+n
lock.release()
print (a)
def second(n):
lock.acquire()
b = 12+n

lock.release()
print (b)
def all():
lock.acquire()
first(2)
second(3)
lock.release()
th1 = threading.Thread(target= all)
th1.start()

When you run the above code, deadlock would occur. In the function all thread
will acquire a lock, after acquiring the lock the first function will be called. The
thread will see the lock.acquire() statement. As this lock itself acquired by the
same python multithreading. But lock does not remember the thread which
acquired it.
In order to overcome above problem, we use Reentrant lock (RLock).
Just replace the threading.Lock with threading.Rlock
threading.RLock() — A factory function that returns a new reentrant lock
object. A reentrant lock must be released by the thread that acquired it. Once a
thread has acquired a reentrant lock, the same thread may acquire it again
without blocking; the thread must release it once for each time it has acquired it.

Lock vs Rlock
The main difference is that a Lock can only be acquired once. It cannot be acquired
again until it is released. (After it’s been released, it can be re-acquired by any
thread).
An RLock, on the other hand, can be acquired multiple times, by the same thread. It
needs to be released the same number of times in order to be “unlocked”.
Another difference is that an acquired Lock can be released by any thread, while an
acquired RLock can only be released by the thread which acquired it.

GIL
Thread-based parallelism is the standard way of writing parallel programs. However,
the Python interpreter is not fully thread-safe. In order to support multi-threaded
Python programs, a global lock called the Global Interpreter Lock (GIL) is used. This
means that only one thread can execute the Python code at the same time; Python
automatically switches to the next thread after a short period of time or when a
thread does something that may take a while. The GIL is not enough to avoid
problems in your own programs. Although, if multiple threads attempt to access the
same data object, it may end up in an inconsistent state.

Let us see the example.
import datetime
def count(n):
while n > 0:
n = n-1
print (t2-t1)
count(100000000)

In the above code, the count function is being run the main thread. Let see the time
taken by the thread.

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I ran the code three times, every time I got a similar result.
Let us create two thread, see the code below.
import datetime
from threading import Thread
def count(n):
while n > 0:
n = n-1
def count1(n):
while n > 0:
n = n-1

thread1 = Thread(target = count, args=(100000000,))
thread2 = Thread(target = count1, args= (100000000,))
thread1.start()
thread2.start()
thread1.join()
thread2.join()
print (t2-t1)

In the above, two threads have been created to be run in parallel.
Let us see the result.

You can the above code took almost 10 seconds which is the double of the
previous program, it means, only the main thread act as multithreading. But above
experiment, we can conclude that Multithreading is defined as the ability of a
processor to execute multiple threads concurrently.
In a simple, single-core CPU, it is achieved using frequent switching between
threads. This is termed as context switching.
In context switching, the state of a thread is saved and state of another thread is
loaded whenever any interrupt (due to I/O or manually set) takes place. Context
switching takes place so frequently that all the threads appear to be running
parallelly(this is termed as multitasking)

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Python multithreading

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