In this chapter, we have examined the underlying structure of the Python memory model, as well as how the language manages its values and variables in a concurrent programming context. Given the way memory management in Python is structured and implemented, the reasoning for the behaviors of a concurrent program can be significantly easier than doing the same in another programming language. The ease in understanding and debugging concurrent programs in Python, however, also comes with a decrease in performance.

Atomic operations are instructions that cannot be interrupted during their execution. Atomicity is a desirable characteristic of concurrent operations, as it guarantees the safety of data shared across different threads. While there are operations in Python that are innately atomic, synchronization mechanisms such as locking are always recommended to guarantee the atomicity of a given operation.

In the next chapter, we will be looking into how to build a concurrent server from scratch. Through this process, we will learn more about implementing communication protocols as well as applying concurrency to an existing Python application.