If you’re looking to streamline your Node.js application’s logging process by incorporating request IDs, then cls-rtracer is your go-to solution! In this blog, we’ll delve into how to implement cls-rtracer to improve your logging capabilities in an Express, Koa, Fastify, or Hapi application.

What is cls-rtracer?

cls-rtracer is a middleware and plugin that generates unique request IDs using UUID V1 and stores them using the AsyncLocalStorage API. This allows you to trace logs related to particular requests, making debugging and monitoring a breeze.

Let’s Get Started!

Follow these steps to integrate cls-rtracer into your application:

• Step 1: Installation

Begin by installing the cls-rtracer package:

npm install --save cls-rtracer

• Note for TypeScript users: Typings are included!
• Step 2: Usage Instructions
• Make sure you use any third-party middleware or plugins that don’t require access to request IDs before using cls-rtracer’s middleware.

Implementation Based on Framework

1. For Express Users

In your Express app, use the middleware like so:

const express = require('express');
const rTracer = require('cls-rtracer');
const app = express();

app.use(rTracer.expressMiddleware());
app.get('/api/v1/entity/:id', (req, res) => {
    const requestId = rTracer.id();
    console.log(`Request ID: ${requestId}`);
    // Handling request...
});

2. For Fastify Users

For Fastify, you will need to register it as a plugin:

const fastify = require('fastify')();
const rTracer = require('cls-rtracer');

fastify.register(rTracer.fastifyPlugin);
fastify.get('/test', async (request, reply) => {
    const requestId = rTracer.id();
    console.log(`Request ID: ${requestId}`);
    // Handling request...
});

3. For Koa Users

Here’s how you can use it in a Koa application:

const Koa = require('koa');
const rTracer = require('cls-rtracer');
const app = new Koa();

app.use(rTracer.koaMiddleware());
app.use(async (ctx) => {
    const requestId = rTracer.id();
    console.log(`Request ID: ${requestId}`);
    // Handling request...
});

4. For Hapi Users

In Hapi, you would similarly use it as follows:

const Hapi = require('@hapi/hapi');
const rTracer = require('cls-rtracer');

const init = async () => {
    const server = Hapi.server({ port: 3000 });
    await server.register(rTracer.hapiPlugin);
    
    server.route({
        method: 'GET',
        path: '/test',
        handler: async (request, h) => {
            const requestId = rTracer.id();
            console.log(`Request ID: ${requestId}`);
            // Handling request...
        }
    });

    await server.start();
};

init();

Understanding the Code: An Analogy

Think of your Node.js application as a restaurant. Each request (customer) that comes in wants a unique identifier (request ID) to track their orders (logs). The cls-rtracer library acts like a well-organized host who assigns a table number (UUID) to each customer as they enter, ensuring their experience is smoothly logged and tracked throughout their meal. This way, any issues can be traced back to the specific customer without confusion!

Troubleshooting Tips

To avoid weird behavior when using cls-rtracer, remember to:

• Use any third-party middleware or plugin that does not need access to request IDs before you set up cls-rtracer.
• If you encounter issues with the context (and thus, the request ID) being lost, consider checking for conflicting libraries that do not play well with the Async Hooks API. If the problem persists, we encourage you to submit an issue on GitHub.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Performance Considerations

It’s worth noting that while cls-rtracer simplifies the logging process, it does introduce a performance overhead due to its reliance on the Async Hooks API. Consider weighing the benefits of efficient request tracking against this performance hit.

Conclusion

Incorporating cls-rtracer into your Node.js application is a wise decision for enhancing logging practices and debugging. By assigning unique request IDs to each request, you can significantly improve the traceability of your logs, thereby enabling better monitoring and troubleshooting capabilities.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.