Concurrency in Python: Threads, Processes and Async

Explore the possibilities Python offers to parallelize your long running code or to execute it asynchronously

Description

In this course you will learn about the possibilities Python offers to make use of multiprocessing, multithreading and asynchronous programming. Multiprocessing is important to parallelize long running calculations by distributing work across multiple processors. It might also turn out to be benificial not to wait for these long running calculations to terminate. Therefor asynchronous programming with Futures is important.

At the end of the training you will be able to write Python code yourself that makes effective use of the possibilities the language offers to spread your workload across Processes and Threads. Also you can make a decision when to use multiple Processes or when to use multiple Threads.

Also the async/await idiom will be covered.

Learning Goals

• Understand why and when multiprocessing has benefits in Python. [Understand]
• Apply multiprocessing in Python [Apply]
• Understand multithreading in Python. [Understand]
• Understand the problems multithreading can cause and their solutions. [Understand]
• Understand asynchrounous programming in Python [Understand]
• Understand coroutines in Python [Understand]

Subjects

• Introduction to Multiprocessing
• Multiprocessing in Python
• Global Interpreter Lock
• Pipes and Queues
• Shared Memory
• Process Pooling
• Multithreading
• Shared Data
• Thread Safe
• Synchronization
• Queues
• Threads in UI-code
• Asynchronous programming
• Futures
• ThreadPoolExecutor
• Coroutines
• Tasks
• async/await
• Asynchronous Generators