Deepak Soundappan - Multi Threading

Multi Threading Multi threading is a very important aspect of programming.

Multithreading isn't a data structure but a computational concept— it refers to multiple threads (lightweight processes) running concurrently within a program. Real-life equivalents of multithreading are situations where multiple tasks are performed simultaneously, often sharing resources. Some relatable analogies include:

Chef in a Kitchen: A chef (CPU) is cooking multiple dishes (threads) at once—boiling pasta, chopping vegetables, and baking—all switching between tasks to get them done faster.
Call Center with Multiple Agents: Multiple agents (threads) handle different calls (tasks) at the same time, improving overall efficiency.
Office Worker with Multiple Tabs Open: A person working on emails, spreadsheets, and a meeting call—switching back and forth rapidly. It's like context-switching in threads.

The process in python to use treading :

We primarily starts with using the threading library which has the Thread class allowing us to add the threads.
We add the Threads into a list which is like the list of the functions that has to be done. We pass the parameters that need to be passed to each of the individual thread function
Then we wait all the treads to complete using the join command.

Sample code for downloading multiple files parallely.

What This Does:

Starts 5 threads in parallel, each simulating a 2-second download.
Without threads, total time = 2 × 5 = 10 seconds (serial).
With threads, total time ≈ 2 seconds (parallel, assuming I/O-bound).

When to Use Multithreading in Python?

Best for I/O-bound tasks: web requests, file operations, waiting for external resources.
For CPU-bound tasks, consider using multiprocessing instead (due to the Global Interpreter Lock, or GIL).

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