Multithreading in python python tutorial day 97

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multithreading in python: a comprehensive tutorial

introduction to multithreading

multithreading is a programming technique that allows concurrent execution of two or more threads (lightweight processes) to maximize cpu utilization. in python, multithreading can be particularly useful for i/o-bound tasks, such as network operations or file i/o, where the program can perform other tasks while waiting for the i/o operations to complete.

why use multithreading?

1. **concurrency**: threads can run concurrently, making programs faster and more responsive.
2. **resource sharing**: threads share the same memory space, which makes it easier to share data between threads.
3. **simplicity**: multithreading can simplify program structure when tasks can be performed simultaneously.

python's threading module

python provides the `threading` module for creating and managing threads. here are some key components:

**thread**: represents a single thread of execution within a program.
**lock**: a synchronization primitive that is used to ensure that only one thread can access a shared resource at a time.
**event**: a simple way to communicate between threads, allowing one thread to signal one or more other threads to proceed.

basic example of multithreading

let's create a simple example where we simulate two tasks running concurrently using threads.

step 1: import the threading module



step 2: define the tasks



step 3: create threads



step 4: start the threads



step 5: wait for threads to complete



full code example

putting it all together, here’s the complete code:



explanation of the code

1. **defining the task**: we define a simple function `task` that takes a name and a delay. it simulates a task by sleeping for the given delay.
2. **creating threads**: we create two threads, `thread1` and `thread2`, targeting the `task` function with different arguments.
3. **starting threads**: we call `start()` on each thre ...

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