Blog 1 | Crafting an API Endpoint | at @AsyncAPI

Blog 1 | Crafting an API Endpoint | at @AsyncAPI

First and foremost, I want to extend my gratitude to my mentor, David Pereira, for his invaluable guidance throughout this journey. I'm immensely grateful for his assistance and mentorship. Working with him has been a fantastic opportunity for me. Additionally, I’d like to extend my heartfelt thanks to my sister, Princy Rajpoot, for her tremendous support throughout this journey.

Now, let’s delve into the details of the project.

My Project Idea

My project idea is to develop an endpoint, tentatively named help/{command}, which will provide users with instructions corresponding to a given command. The primary help endpoint would list all available endpoints, whereas specific commands like help/generate would return detailed information about that particular command, such as available templates and parameters.

Getting Started with server-api

My journey began with a deep dive into the server-api project. Analyzing the codebase, I identified six existing endpoints: /validate, /parse, /generate, /convert, /bundle, /diff

Upon familiarizing myself with these endpoints, I proceeded to set up the project locally. Interestingly, all existing endpoints employ the POST method. Hence, to invoke them, one must use POSTMAN. Since they all use the POST method, sending a request body is essential to obtain a response.

Having successfully set up the project on my local machine, I delved deeper into understanding its codebase, the overarching project structure, and its inherent workflow.

Designing the /help Endpoint: Key Decisions

I began thinking about the structure of the /help endpoint. Given that our objective is to provide help for multiple endpoints, let's start by considering how we would pass data to the /help endpoint. The project idea already suggests utilizing the URL approach, as indicated byhelp/{command}. You might be wondering why the project idea leaned in that direction, especially when the request body is such a viable option. Let's first understand the differences between the two, and then delve into why we chose URL over the request body.

  • URL-based data transmission is ideal when:

    • The data amount is small, ensuring URLs don’t get too lengthy.

    • The information isn't sensitive. For context, transmitting sensitive data, like passwords, in URLs can expose them.

  • Request body data transmission becomes crucial when:

    • Data size exceeds the URL’s character limit (usually around 2048 characters).

    • There's a need to send confidential information, which won't be visible in the URL.

Considering both the non-sensitive nature of the data and its compactness, using the URL method is the best choice.

Enhance User Experience

Enhance user experience by providing relative links to all the endpoints directly within the /help section, which contains a list of all the endpoints. Here is a snippet of /help response:


        "command": "parse",
        "url": "/help/parse"
        "command": "generate",
        "url": "/help/generate"


Users can now conveniently click on links like /help/generate to access specific functionalities, making navigation straightforward and user-friendly.

Ensuring Flexibility

During discussions with my mentor David, he highlighted that hardcoding data for the help command wasn’t ideal. Instead, sourcing it from a GitHub repository would facilitate easier updates and maintenance. Therefore, I decided to source our /help data from GitHub repo.

Debugging Dilemmas: The Unexpected Rate Limit Riddle

After researching and building the MVP structure, I delved deep into the testing and debugging stage. Full of confidence in my code, I encountered an unexpected hurdle. Initially, I was met with an error, and while troubleshooting it, another error cropped up — all within the confines of my previous codebase. The scenario was mystifying. It was like playing a game of digital whack-a-mole. 🤔

I decided to step back for a moment. After a calming tea break, I revisited the issue with a renewed mindset. Much to my surprise, the root cause wasn't a glitch in my code but an external limitation I hadn't accounted for: the GitHub API's rate limit. I had inadvertently overlooked GitHub's rate restriction of 60 requests per hour for each user. Amid my enthusiasm to refine the endpoint, I quickly consumed these requests, resulting in the unforeseen errors.

But here's the twist: I was in a race against time, needing to test repeatedly. While I couldn't beat time, I found a way to switch my user identity. 😄 Wondering how? By using a VPN, I was able to mask my IP address. This trick allowed me to simulate different users, bypassing the rate limit. A little creative problem-solving, and I was back on track, proving once again that there’s always a way around a roadblock, sometimes just by changing lanes!😌

Current Progress 🏗️

As of now, I'm in the process of finalizing the structure and defining the data for all the commands.

To facilitate this, I've employed axios.get() to fetch the openapi.yml file of the project, which contains essential data for the help command.

Here's a brief on the tools and libraries in use:

  • Axios: It's a promise-based HTTP client that's both lightweight and efficient. I've specifically used it to make an HTTP request to GitHub's API and retrieve the raw contents of the openapi.yml file.

  • js-yaml: YAML is a human-friendly data format widely adopted for configuration files and data storage/transmission. The js-yaml library facilitates the parsing and stringifying of YAML content. With its yaml.load() function, I've converted the raw YAML content (fetched using Axios) into a workable JavaScript object. This transformed object now serves as the foundation for subsequent operations in the endpoint, such as resolving references and pinpointing matching paths.

What’s next?

In my next blog post, I’ll explore different challenges faced. As this phase wraps up, my attention will shift towards creating unit tests.

Till next time,