Python multithreaded programming

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Python - Multithreaded
Programming

Running several threads is similar to running
several different programs concurrently, but with
the following benefits
Multiple threads within a process share the
same data space with the main thread and can
therefore share information or communicate
with each other more easily than if they were
separate processes.
Threads sometimes called light-weight
processes and they do not require much
memory overhead; they are cheaper than
processes.
Python - Multithreaded Programming

A thread has a beginning, an execution sequence,
and a conclusion.
It has an instruction pointer that keeps track of
where within its context it is currently running.
It can be pre-empted (interrupted)
It can temporarily be put on hold (also known
as sleeping) while other threads are running -
this is called yielding.

To spawn another thread, you need to call
following method available in thread module
Starting a New Thread
thread.start_new_thread ( function, args[, kwargs] )
This method call enables a fast and efficient way
to create new threads in both Linux and Windows.
The method call returns immediately and the
child thread starts and calls function with the
passed list of args.
When function returns, the thread terminates.

The Threading Module

The newer threading module included with
Python 2.4 provides much more powerful, high-
level support for threads than the thread module
discussed in the previous section.
The threading module exposes all the methods of
the thread module and provides some additional
methods
threading.activeCount() − Returns the number
of thread objects that are active.
threading.currentThread() − Returns the
number of thread objects in the caller's thread
control.
threading.enumerate() − Returns a list of all
thread objects that are currently active.

In addition to the methods, the threading module
has the Thread class that implements threading.
The methods provided by the Thread class are as
follows
run() − The run() method is the entry point for
a thread.
start() − The start() method starts a thread by
calling the run method.
join([time]) − The join() waits for threads to
terminate.
isAlive() − The isAlive() method checks whether
a thread is still executing.
getName() − The getName() method returns
the name of a thread.
setName() − The setName() method sets the
name of a thread.

Creating Thread Using Threading Module
To implement a new thread using the threading
module, you have to do the following
Define a new subclass of the Thread class.
Override the __init__(self [,args]) method to add
additional arguments.
Then, override the run(self [,args]) method to
implement what the thread should do when
started.
Once you have created the new Thread subclass,
you can create an instance of it and then start a
new thread by invoking the start(), which in turn
calls run() method.

Synchronizing Threads
The threading module provided with Python
includes a simple-to-implement locking
mechanism that allows you to synchronize
threads.
A new lock is created by calling the Lock() method,
which returns the new lock.
The acquire(blocking) method of the new lock
object is used to force threads to run
synchronously.
The optional blocking parameter enables you to
control whether the thread waits to acquire the
lock.

If blocking is set to 0, the thread returns
immediately with a 0 value if the lock cannot be
acquired and with a 1 if the lock was acquired.
If blocking is set to 1, the thread blocks and wait
for the lock to be released.

Multithreaded Priority Queue

The Queue module allows you to create a new
queue object that can hold a specific number of
items. There are following methods to control the
Queue.
get() − The get() removes and returns an item
from the queue.
put() − The put adds item to a queue.
qsize() − The qsize() returns the number of
items that are currently in the queue.
empty() − The empty( ) returns True if queue is
empty; otherwise, False.
full() − the full() returns True if queue is full;
otherwise, False.

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