Tutorials on Rest Api

An important chore that gets neglected by developers is writing documentation for their RESTful APIs. Often, this task ends up being assigned lower priority than other tasks, such as building a new feature or modifying an existing feature. Although it delivers no immediate tangible value to end users like features, documentation produces intangible value for developers and their companies. By having detailed information about a RESTful API's endpoints, developers can quickly know how to obtain authorization for protected endpoints, access and interact with certain resources, format the data that's required in a request's body, etc. Ultimately, the value in documentation comes from increased developer productivity and saved development time. The more developers the documentation serves, the more value the documentation produces. Fortunately, you don't have to spend any time or effort to manually build and maintain documentation from scratch. There are open source tools like Swagger that automate the process of designing and generating RESTful API documentation for developers. These tools: Best of all, most of these tools only need, as input, the source code of the RESTful API or a JSON representation of the RESTful API that's compliant with the OpenAPI Specification . Or, these tools can get fed other representations of the RESTful API that are based on alternative API specifications like RAML ( R ESTful A PI M odeling L anguage).

The amount of time and effort a developer dedicates towards writing a function depends on the details they choose to focus on: coding conventions, structure, programming style, etc. Suppose a group of developers is presented a high-level prompt to write the same function: given some input, return some output. For example, given a list of numbers, return a sorted list of numbers. The actual implementation of the function is left entirely to the discretion of the developer. A quick, mathematical way to evaluate each developer's implementation of this function, without any additional code, is by its time complexity . Particularly, knowing each implementation's Big-O complexity tells us how it might perform in the worst case scenario, commonly when the size of the input is very large. However, time complexity fails to account for the hardware the function is executed upon, and it does not provide any tangible, quantifiable metrics to base decisions on. Metrics such as operation speed and total execution time assign real numerical values to the performance of a function. By adding benchmarks , developers can leverage these metrics to better inform them on how to improve their code. The Go programming language has a benchmarking utility in its built-in, standard library package testing . To benchmark code in Go, define a function with a name prefixed with Benchmark (followed by a capitalized segment of text) and accepts an argument of struct type B , which contains methods and values for determining the number of iterations to run, running multiple benchmarks in parallel, timing execution times, etc. Example :

Learn the basics of APIs and what makes an API "RESTful"

The Go programming language is designed to address the types of software infrastructure issues Google faced back in late 2007. At this time, Google predominantly built their infrastructure using the programming languages C++, Java and Python. With a large codebase and having to adapt to rapid technological changes in both computing and key infrastructure components (e.g., multi-core processors, computer networks and clusters), Google found it difficult to remain productive within this environment using these languages. Not only does Go's design focus on improving the productivity of developers/engineers, but Go's design incorporates the best features of C++, Java and Python into a single statically-typed, compiled, high-performance language: Using Go and a lightweight router library, such as chi , writing and launching a simple RESTful API service requires little time and effort, even if you have no experience with Go. chi 's compatibility with the Go standard library package net/http , optional subpackages (middleware, render and docgen) and consistent API allows you to compose mantainable, modular services that run fast. Plus, these services can be deployed to and thrive in a production environment . If you have previously built RESTful APIs using Node.js and a minimal web application framework, such as Express.js , then you will find this to be quite similar! Below, I'm going to show you:

Testing plays a fundamental role in the development of quality software. Shipping and deploying software with undetected bugs and regressions opens up a can of terrible consequences such as losing the trust of end users or costing the business time and resources. In a large collaborative setting, having developers manually test each and every feature and user flow for bugs and regressions wastes valuable time that can be put towards improving other aspects of the software. As the codebase and team grows, this approach will not scale. By writing unit/integration/end-to-end tests, identifying and catching bugs and regressions throughout an entire codebase becomes a painless, automatable task that can easily be integrated into any continuous integration pipeline. Unlike most other languages, the Go programming language provides a built-in, standard library package for testing: testing . The testing package offers many utilities for automating the testing of Go source files. To write a test in Go, define a function with a name prefixed with Test (followed by a capitalized segment of text) and accepts an argument of struct type T , which contains methods for failing and skipping tests, running multiple tests in parallel, formatting test logs, etc. Example :

RESTful APIs adhere to a reliable architectural standard for transferring data statelessly over the HTTP protocol. Every endpoint of an API semantically describes how a resource should be created ( POST ), read ( GET ), updated ( PUT / PATCH ), deleted ( DELETE ), etc. Large, data-driven applications consume data from multiple third-party/in-house sources, and each one exposes a unique set of endpoints to manage different resources. Adapting these applications to support a wide range of platforms and device sizes (commonly mobile, desktop and web) may present several problems: Using Facebook's GraphQL query language, the client specifies its exact data requirements to the server via a single endpoint. Establishing a schema (written with the syntax of the GraphQL Schema Definition Language) creates a contract between the client and server that defines what data can be read from and written to the data graph by the client. This data graph centralizes all of the APIs consumed by your application by mapping each field to a resolver that populates it with a value retrieved from an endpoint of one of these APIs, a database, etc. A client can fetch data from a GraphQL server via plain HTTP and then manually update the UI accordingly. However, GraphQL clients such as Apollo Client abstract away the low-level implementation details of these features underneath a declarative API. Built by the Apollo GraphQL team, Apollo Client is an open-source GraphQL client that provides a lot of out-of-the-box functionality for communicating with a GraphQL server:

In this post, I want to show you how to quickly put together a complete REST API using Flask. As you might know, Flask is a perfect fit for the job! Our example creates a movies API but you can follow this for any API you want to create! Here's what we'll cover: