Add some definitions for parallelism in Python

boilerplates-concurrence/README.md

@@ -1,3 +1,35 @@
-### Concurrence in Python
+## Concurrency and Parallelism in Python
 
-Examples for my Medium article: [Python + Concurrence: A Mnemonic Guide🚦](https://medium.com/python-for-the-utopian/python-concurrence-a-mnemonic-guide-7304867cbfb7).
+
+### 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](https://python-rq.org/) 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.