Effective Python: 90 Specific Ways to Write Better Python, Second Edition (Nazeer Hussain Shaik's Library) by Brett Slatkin

Author:Brett Slatkin
Language: eng
Format: mobi, epub
Publisher: Addison-Wesley Professional
Published: 2020-05-12T16:00:00+00:00

✦ Class decorators are useful when you want to modify every method or attribute of a class with minimal boilerplate.

✦ Metaclasses can’t be composed together easily, while many class decorators can be used to extend the same class without conflicts.

7. Concurrency and Parallelism

Concurrency enables a computer to do many different things seemingly at the same time. For example, on a computer with one CPU core, the operating system rapidly changes which program is running on the single processor. In doing so, it interleaves execution of the programs, providing the illusion that the programs are running simultaneously.

Parallelism, in contrast, involves actually doing many different things at the same time. A computer with multiple CPU cores can execute multiple programs simultaneously. Each CPU core runs the instructions of a separate program, allowing each program to make forward progress during the same instant.

Within a single program, concurrency is a tool that makes it easier for programmers to solve certain types of problems. Concurrent programs enable many distinct paths of execution, including separate streams of I/O, to make forward progress in a way that seems to be both simultaneous and independent.

The key difference between parallelism and concurrency is speedup. When two distinct paths of execution in a program make forward progress in parallel, the time it takes to do the total work is cut in half; the speed of execution is faster by a factor of two. In contrast, concurrent programs may run thousands of separate paths of execution seemingly in parallel but provide no speedup for the total work.

Python makes it easy to write concurrent programs in a variety of styles. Threads support a relatively small amount of concurrency, while coroutines enable vast numbers of concurrent functions. Python can also be used to do parallel work through system calls, subprocesses, and C extensions. But it can be very difficult to make concurrent Python code truly run in parallel. It’s important to understand how to best utilize Python in these different situations.

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