Serverless computing is a cloud computing execution model where the cloud provider dynamically manages the allocation and provisioning of computing resources. Here are the key characteristics of serverless computing and how it differs from traditional server-based architectures:
No server management: In serverless computing, developers don’t have to worry about server provisioning, configuration, or maintenance. The cloud provider abstracts away the underlying infrastructure, allowing developers to focus solely on writing and deploying code.
Event-driven execution: Serverless architectures are typically event-driven. Functions or microservices are triggered by specific events or requests, such as an HTTP request, database update, or a file upload. The cloud provider automatically scales the infrastructure up or down based on the incoming events.
Pay-per-use pricing: Serverless platforms usually follow a pay-per-use pricing model. You only pay for the actual compute resources consumed during the execution of your functions or applications. This allows for cost efficiency, as you don’t have to pay for idle server time.
Automatic scalability: Serverless platforms automatically scale the execution environment based on the incoming workload. When there are many concurrent requests or events, the provider spins up additional instances of the function to handle the load. Conversely, when the workload decreases, the platform scales down or even shuts down the instances, saving resources and costs.
Stateless execution: Serverless functions are stateless, meaning they don’t maintain any persistent state between invocations. Any necessary state or data must be stored in external services, such as databases or object storage. This statelessness simplifies the scaling and distribution of requests.
Event-driven ecosystem: Serverless computing encourages a highly modular and event-driven architecture. Applications can be built by composing smaller, loosely coupled functions or microservices that communicate through events. This enables flexibility, scalability, and composability of different services.
To get started with Vercel, it’s helpful to understand projects and deployments:
Projects: A project is the application that you have deployed to Vercel. You can have multiple projects connected to a single repository (for example, a monorepo), and multiple deployments for each project. You can view all your projects on the dashboard, and configure your settings through the project dashboard.
Deployments: A deployment is the result of a successful build of your project. A deployment is triggered when you import an existing project or template, or when you push a Git commit through your connected integration or use vercel deploy from the CLI. Every deployment generates a URL automatically.
APIs (Application Programming Interfaces) are sets of rules and protocols that allow different software applications to communicate with each other. They define how applications can interact and exchange data.
In Python applications, APIs are utilized to access and manipulate data from external sources. This is done through the following steps:
HTTP Requests: Python’s requests library is used to send HTTP requests to API endpoints, allowing developers to interact with the API and retrieve data.
Authentication: APIs often require authentication for secure access. Python libraries like OAuthlib and Authlib handle authentication mechanisms such as OAuth, JWT, and basic authentication.
Data Parsing: APIs commonly return data in formats like JSON or XML. Python’s built-in json library is used to parse and manipulate JSON data, while xml.etree.ElementTree is used for XML data.
API Wrappers: Some APIs have Python libraries specifically designed to interact with them. These wrappers provide higher-level abstractions, simplifying the integration process by handling authentication, request formatting, and response parsing.
API Documentation: APIs provide documentation that explains their endpoints, request parameters, and response formats. Developers should refer to this documentation to understand how to construct requests and interpret the returned data.
The Requests library is a popular third-party library in Python for making HTTP requests and interacting with APIs. The library abstracts the complexities of working with low-level HTTP protocols, making it easier to work with APIs and retrieve data from web services.
Using Requests Library to Interact with APIs
To use the Requests library, you’ll need to install it first by running pip install requests. Once installed, you can import it into your Python script using import requests. If the request is successful, you can parse the response data using the json() method to obtain a Python object (e.g., a dictionary or a list) representing the response data. From there, you can process and work with the data according to your application’s needs.