How To Speed Up Slow Python Code With Concurrent Programming And The Cutting-edge Asyncio Library

Discover just how to quicken sluggish Python code with simultaneous programming and also the advanced asyncio collection.

Usage coroutines as well as jobs along with async/await syntax to run code concurrently
Construct internet APIs as well as make concurrency internet requests with aiohttp
Run hundreds of SQL inquiries simultaneously
Develop a map-reduce job that can refine gigabytes of information simultaneously
Use threading with asyncio to mix obstructing code with asyncio code

Python is versatile, versatile, and easy to find out. It can also be extremely sluggish contrasted to lower-level languages. Python Concurrency with asyncio instructs you how to improve Python's performance by applying a range of concurrency methods. You'll find out just how the complex-but-powerful asyncio library can achieve concurrency with simply a solitary thread and use asyncio's APIs to run several internet demands and data source questions all at once. The book covers utilizing asyncio with the entire Python concurrency landscape, consisting of multiprocessing and also multithreading.

About the technology
It's easy to overload typical Python as well as watch your programs reduce to a crawl. The asyncio collection was constructed to resolve these troubles by making it easy to split and arrange jobs. It flawlessly deals with numerous procedures simultaneously, causing applications that are lightning quick as well as scalable.

Concerning the book
Python Concurrency with asyncio introduces asynchronous, parallel, and concurrent programming with hands-on Python examples. Hard-to-grok concurrency topics are broken down right into basic flowcharts that make it simple to see exactly how your tasks are running. You'll discover exactly how to get rid of the limitations of Python utilizing asyncio to speed up sluggish web servers and microservices. You'll also combine asyncio with traditional multiprocessing methods for big improvements to efficiency.

What's inside

Develop internet APIs and also make concurrency web demands with aiohttp
Run thousands of SQL inquiries simultaneously
Develop a map-reduce job that can refine gigabytes of data concurrently
Use threading with asyncio to blend blocking code with asyncio code

Concerning the reader
For intermediate Python programmers. No previous experience of concurrency needed