When we eliminate complex, shared state and design around non-strict processing, we can leverage parallelism to improve performance. In this chapter, we'll look at the multiprocessing and multithreading techniques that are available to us. Python library packages become particularly helpful when applied to algorithms that permit lazy evaluation.

The central idea here is to distribute a functional program across several threads within a process or across several processes. If we've created a sensible functional design, we don't have complex interactions among application components; we have functions that accept argument values and produce results. This is an ideal structure for a process or a thread.

We'll focus on the multiprocessing and concurrent.futures modules. These modules allow a number of parallel execution techniques.

We'll also focus on process-level parallelism instead of multithreading. The idea behind process parallelism allows...