Python multithreaded programming (1)

Virtual machine level

The Python virtual machine uses the Gil (Global interpreter lock, the Universal interpreter lock) to mutually exclusive threads access to shared resources, temporarily unable to take advantage of multiprocessor benefits.

Language level

At the language level, Python provides good support for multithreading, including: Thread,threading,queue, a multithreaded-related module in Python. It is easy to support the creation of threads, mutexes, semaphores, synchronization and other features.

Thread: The underlying support module for multithreading is generally not recommended for use.

Threading: Thread is encapsulated, and some threading operations are instantiated, providing the following classes:

Thread Threading Class

The timer is similar to thread, but waits a while before it starts to run

Lock the original language

Rlock locks can be re-entered. Enables a single thread to obtain a lock already acquired again

Condition A conditional variable that allows a thread to stop waiting for another thread to satisfy a "condition"

The condition variable that is common to the Event. Multiple threads can wait for an event to occur, and all threads are activated after the event occurs

Semaphore provides a "waiting room" structure for the thread that waits for the lock

Boundedsemaphore similar to semaphore but not allowed to exceed the initial value

Queue: Implements multi-producer (Producer), multi-Consumer (Consumer) queues, supports lock primitives, and provides good synchronization support across multiple threads. The classes provided:

Queue queues

Lifoqueue after in first out (LIFO) queue

Priorityqueue Priority Queue

Where the thread class is your primary thread class, you can create a process instance. The functions provided by this class include:

GetName (self) Returns the name of the thread

The IsAlive (self) Boolean flag that indicates whether the thread is still running

Isdaemon (self) Returns the daemon flag for the thread

The Join (self, timeout=none) program hangs until the thread ends and, if a timeout is given, blocks timeout seconds

Run (self) defines the function function of the thread

Setdaemon (self, daemonic) sets the thread's daemon flag to Daemonic

SetName (self, name) sets the name of the thread

Start (self) starts thread execution

Python's threading. The thread class has a run method that defines a function function for the thread that can be overridden in its own thread class. After creating your own thread instance, through the start method of the thread class, you can start the thread and hand it over to the Python virtual machine and call the Run method execution thread when the thread gets the chance to execute.

Import Threadingimport Timeclass MyThread (threading. Thread):d EF Run: For I in range (3): Time.sleep (1) msg = "I ' m" +self.name+ ' @ ' +str (i) Print Msgdef test (): For I in range (5): T = MyThread () t.start () if __name__== ' __main__ ': Test ()

　　

Execution Result:

I ' m Thread-1 @ 0
I ' m Thread-2 @ 0
I ' m Thread-5 @ 0
I ' m Thread-3 @ 0
I ' m Thread-4 @ 0
I ' m Thread-3 @ 1
I ' m Thread-4 @ 1
I ' m Thread-5 @ 1
I ' m Thread-1 @ 1
I ' m Thread-2 @ 1
I ' m Thread-4 @ 2
I ' m Thread-5 @ 2
I ' m Thread-2 @ 2
I ' m Thread-1 @ 2
I ' m Thread-3 @ 2

From the code and execution results, we can see that the order of execution of multi-threaded routines is indeterminate. When executed to the sleep statement, the thread is blocked (Blocked), and after sleep ends, the thread enters the ready (Runnable) state and waits for dispatch. Thread scheduling will choose a thread to execute itself. The above code only guarantees that each thread runs the full run function, but the start order of the thread and the order of execution of each loop in the run function are not determined.
It is also important to note that:
1. Each thread must have a name, although the above example does not specify the name of the thread object, but Python automatically assigns a name to the thread.
2. When the thread's run () method ends, the thread finishes.
3. The thread scheduler cannot be controlled, but there are other ways in which thread scheduling can be affected.

The above example simply demonstrates the creation of threads, active suspend, and exit threads

Python multithreaded programming (1)