web3-starter-py/boilerplates-concurrency/README.md

Concurrency and Parallelism in Python

• Read a detailed explanation on threads and multiprocessing in Python in my book

Threading

• Threading is a feature usually provided by the operating system.
• Threads are lighter than processes, and share the same memory space.
• With threading, concurrency is achieved using multiple threads, but due to the GIL only one thread can be running at a time.
• If your code is IO-heavy (like HTTP requests), then multithreading will still probably speed up your code.

Multi-processing

• In multiprocessing, the original process is forked process into multiple child processes bypassing the GIL.
• Each child process will have a copy of the entire program's memory.
• If your code is performing a CPU bound task, such as decompressing gzip files, using the threading module will result in a slower execution time. For CPU bound tasks and truly parallel execution, use the multiprocessing module.
• Higher memory overhead than threading.

RQ: queueing jobs

• RQ is aimple but powerful library.
• You first enqueue a function and its arguments using the library. This pickles the function call representation, which is then appended to a Redis list.

Celery: queueing jobs

• Celery is one of the most popular background job managers in the Python world.
• Compatible with several message brokers like RabbitMQ or Redis and can act as both producer and consumer.
• Asynchronous task queue/job queue based on distributed message passing. It is focused on real-time operations but supports scheduling as well.

concurrent.futures

• Using a concurrent.futures.ThreadPoolExecutor makes the Python threading example code almost identical to the multiprocessing module.