Devouring APIs with Python

Your Python API calls could be 10x faster. Learn to switch from synchronous requests to a truly non-blocking asynchronous library.

#1about 2 minutes

Understanding the role of an application programming interface (API)

An API acts as a communication layer that allows different software applications, like a taxi app and Google Maps, to exchange data and functionality.

#2about 4 minutes

Deconstructing the components of a REST API URL

A REST API URL is broken down into the protocol, server host, resource path, and optional parameters for filtering data.

#3about 1 minute

Distinguishing between client libraries and API frameworks

Client libraries like `requests` are used to send HTTP requests to endpoints, while API frameworks like `Flask` provide tools to build your own API endpoints.

#4about 5 minutes

Making synchronous requests with the Python requests library

The `requests` library simplifies making HTTP GET and POST requests in Python with a clean, one-line syntax.

#5about 6 minutes

Using the pycurl library as a libcurl wrapper

The `pycurl` library provides a Python interface to `libcurl` for making API calls, offering more control but with a more complex syntax than `requests`.

#6about 5 minutes

Building a simple API with the Flask microframework

Flask allows developers to quickly define API routes and handle different HTTP methods using decorators on Python functions.

#7about 3 minutes

Structuring APIs with the Flask-RESTful extension

The Flask-RESTful extension adds a layer of abstraction for building REST APIs by organizing endpoints into resources, leading to cleaner code.

#8about 8 minutes

Demonstrating synchronous API calls with a live coding example

A synchronous script using the `requests` library fetches 100 URLs sequentially, highlighting the performance bottleneck of waiting for each request to complete.

#9about 3 minutes

Explaining multiprocessing and multithreading with analogies

Multiprocessing achieves true parallelism with multiple CPUs, while multithreading creates the illusion of parallelism on a single CPU, constrained by Python's GIL.

#10about 13 minutes

Achieving high performance with asyncio and aiohttp

Using `asyncio` with the `aiohttp` library enables non-blocking API calls, drastically reducing execution time by performing other tasks while waiting for I/O.

#11about 1 minute

Exploring resources for further API development learning

Cisco DevNet offers learning labs, code samples, and automation exchanges to help developers expand their Python and REST API skills.

#12about 7 minutes

Q&A on concurrency models and developer tools

The discussion covers when to choose different concurrency models, favorite VS Code plugins like Tabnine and Acrolinx, and best practices for error handling.