Complete Guide to Python Multithreading and Multiprocessing

Learn to define and use Python functions, including default parameters, multiple arguments, and returning values. Practice with printing, lists, and dictionaries to display results.

Learn to organize code with Python modules and packages by importing modules, attributes, and aliases, creating custom modules, and using os and command-line arguments, while handling import errors.

Explore defining an animal class with name and sound, extending it with a bird subclass, and using class and static methods to compute rectangle area and square checks.

Explore the difference between threads and processes in Python, and how concurrency uses shared memory for IO-bound tasks versus independent memory spaces for CPU-bound work.

Explain the global interpreter lock (GIL) in Python, its memory safety role and its bottleneck for CPU-bound threads, while showcasing concurrency for I/O-bound tasks and alternatives like multiprocessing.

Learn the basics of threading in Python by creating your first thread, using the threading module, defining a function, starting the thread, and joining to wait for completion.

Demonstrate creating, starting, and joining a Python thread to run a function with time.sleep between prints in parallel with the main program, illustrating the basic threading steps.

Learn how Python's threading join method synchronizes multiple threads by blocking the calling thread until each thread finishes, ensuring ordered execution, data integrity, and safe program shutdown.

Demonstrates joining threads in Python by running two threads that print numbers with a one-second delay; using join ensures the main program waits for both threads before printing finish.

Explore python daemon threads and background execution, showing how setting thread.daemon to true lets the main program exit without waiting, unlike non-daemon threads that join and block termination.

Learn how daemon threads enable background execution in Python. A daemon thread runs a background task, printing a message, and terminates when the main program ends.

Explore thread synchronization with locks to manage data, prevent race conditions, and avoid deadlock in multi-threaded applications, while learning to acquire and release locks safely.

Learn how to use thread locks in Python to synchronize updates to a shared bank balance, prevent race conditions, and ensure consistent results.

Identify deadlocks in concurrent programming by understanding the four conditions, and apply prevention techniques such as priority-based resource ordering and consistent locking orders.

Explore how condition variables, with a mutex, let threads wait for a condition and notify changes. Learn practical patterns like producer-consumer and wait groups, plus deadlock avoidance tips.

Learn how condition variables synchronize access to a shared resource between producer and consumer threads, using wait and notify to coordinate data addition and consumption.

Coordinate multiple threads with barriers in Python to wait for each other at a specific point, release all once the required number arrives, and apply best practices to avoid deadlocks.

Implement a barrier in Python threading to synchronize three threads. Show how barrier.wait blocks each thread until all three reach the barrier, then releases them to continue.

Learn how Python handles processes and when to choose multiprocessing over threading, including isolation, memory space, and bypassing the gil for cpu-bound tasks.

Create and run a simple process using Python's multiprocessing module by defining a process task, starting it, and joining to confirm the process completes.

Create and manage multiple Python processes in parallel using the multiprocessing module; start five worker processes that sleep for two seconds and join them to demonstrate concurrent execution.

Demonstrates sharing a numeric value between processes using the multiprocessing value type, synchronizing increments with a shared lock to achieve an atomic count of 20.

Demonstrates memory sharing between processes by using a shared array from the multiprocessing module, synchronized with a lock, while two processes increment distinct ranges and reveal the final state.

Master inter-process communication in Python using pipes for point-to-point data exchange and queues for multi-producer, multi-consumer workflows, with examples of send/receive and put/get semantics.

Leverage queues for inter-process communication to implement a producer–consumer pattern in multiprocessing, where producers put items into a queue and consumers retrieve them, with pipes to be explored next.

demonstrate inter-process communication via a multiprocessing pipe between a producer and a consumer. send and receive across the two ends, with both processes closing their connections.

Learn how Python uses threads and processes to speed up file searching, and decide when to favor IO bound threads or CPU bound processes for large file systems.

Explore parallel file searching with multiprocessing and threading, using os.walk to locate files by name across directories. Learn to switch between processes and threads and optimize search performance.

Explore Python matrix manipulation by comparing single-threaded and multi-threaded approaches, illustrate row-wise threading, and discuss performance, profiling, and best practices for selecting thread counts.

Learn the Python executor framework with thread pool and process pool executors for asynchronous calls using futures. Master choosing threads or processes, submit, and managing max workers with context manager.

Use thread pool executor to perform a synchronization task asynchronously, submit it, and retrieve the result with future.result, illustrating concurrent execution for io bound tasks.

Explore real-world uses of thread pools and process pools to accelerate web scraping, data processing, and image processing; parallelize I/O and cpu-bound tasks across multiple cores using executor pools.

Demonstrates resizing a batch of images in parallel using a process pool executor with Pillow, producing 100x100 outputs saved to an output directory.

Master debugging for Python multithreading and multiprocessing by using logging, conditional breakpoints, and dump analysis to detect race conditions, deadlocks, and non-deterministic behavior.

Learn how to debug a multi-threaded Python application by adding logging to track thread start and end, revealing race conditions and synchronization issues.

Learn to build a custom logger with Python's logging library, featuring a console handler and formatter that shows time, module, function, and message, with info, debug, and error levels.

Review python parallel programming from virtual environments to threading and multiprocessing basics, including synchronization, deadlocks, condition variables, barriers, and pooling with thread and process pool executors for real world scenarios.