Python Concurrency and Parallelism

Python Concurrency and Parallelism - Important Points

A. A global interpreter lock that ensures only one thread can execute Python bytecode at a time.

B. A threading mechanism that allows Python to execute multiple threads in parallel.

C. A module for managing concurrent I/O in Python.

D. A library for creating and managing parallel processes in Python.

A. Using multiprocessing instead of threading

B. Using asyncio instead of threading

C. Using coroutines instead of threads

D. Using locks to manage access to shared resources

A. To provide a way to communicate between threads.

B. To prevent race conditions in multithreaded code.

C. To ensure only one thread can access a shared resource at a time.

D. To provide a way to limit the number of threads that can access a resource at a time.

A. Dask

B. Flask

C. Django

D. Pyramid

A. To provide a way to execute code concurrently using coroutines.

B. To provide a way to execute code concurrently using threads and processes.

C. To provide a way to manage and control concurrency in Python code.

D. To provide a way to create and manage threads and processes.