Tutorials on Chi

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).

Accessing an e-mail account anywhere in the world on any device requires authenticating yourself to prove the data associated with the account (e.g., e-mail address and inbox messages) actually belongs to you. Often, you must fill out a login form with credentials, such as an e-mail address and password, that uniquely identify your account. When you first create an account, you provide this information in a sign-up form. In some cases, the service sends either a confirmation e-mail or an SMS text message to ensure that you own the supplied e-mail address or phone number. Because it is highly likely that only you know the credentials to your account, authentication prevents unwanted actors from accessing your account and its data. Each time you log into your e-mail account and read your most recent unread messages, you, and like many other end users, don't think about how the service implements authentication to protect/secure your data and hide your activity history. You're busy, and you only want to spend a few minutes in your e-mail inbox before closing it out and resuming your day. For developers, the difficulty in implementing authentication comes from striking a balance between the user experience and the strength of the authentication. For example, a sign up form may prompt the user to enter a password that contains not only alphanumeric characters, but also must meet other requirements such as a minimum password length and containing punctuation marks. Asking for a stronger password decreases the likelihood of a malicious user correctly guessing it, but simultaneously, this password is increasingly more difficult for the user to remember. Keep in mind that poorly designed authentication can easily be bypassed and introduce more vulnerabilities into your application. In most cases, applications implement either session-based or token-based authentication to reliably verify a user's identity and persist authentication for subsequent page visits. Since Go is a popular choice for building server-side applications, Go's ecosystem offers many third-party packages for implementing these solutions into your applications.

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 :

Disclaimer - If you are unfamiliar with writing a simple unit test for a route handler of a Go and chi RESTful API, then please read this blog post before proceeding on. Go's testing library package provides many utilities for automating the testing of Go code. To write robust tests for Go code, you must already understand how to write a basic Go test suite that contains several TestXxx functions. Writing tests for code, especially within the context of test-driven development (TDD), prioritizes the code's correctness over the code's flexibility to adapt to new/updated requirements. The guarantee of less unexpected regressions offsets the upfront cost of spending more time to write tests alongside application code. In a fast-paced, high-pressure environment, it can be difficult to convince other team members and stakeholders of the value in testing code when time is an extremely limited resource. Another factor that must be considered is the amount of code covered by the tests. If the tests cover only a small percentage of the application code (or a small subset of use cases), then the benefits of having these tests probably won't outweigh the benefits of adding new features or improving existing features. Plus, anytime you decide to refactor the application code, you will also have to update the corresponding tests to reflect these changes. When time is so valuable, the time spent on writing tests could have been spent elsewhere. Therefore, to fully benefit from tests, you must write enough tests such that they cover a large percentage of the application code.

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 :