AWS X-Rays

Modern cloud-native architectures often consist of multiple microservices working together to fulfill a single user request. Pinpointing the root cause can feel like untangling spaghetti when something goes wrong. Distributed tracing helps unravel this complexity by showing how requests flow through our services.

Distributed tracing helps unravel this complexity by showing a request’s complete, end-to-end journey as it flows through each service in an application.

This differs from traditional logging, which provides individual snapshots of events at specific moments. Logs can be unstructured, stored in various locations, and often lack connections between services. Tracing, on the other hand, gives us a contextual view of the entire request flow. It links together how multiple services collaborate to process a single request. While we still need logs for detailed debugging and auditing, tracing offers an overall perspective that logging alone cannot provide.

In AWS, X-Ray provides a powerful way to implement distributed tracing, offering deep insights into application behavior, performance bottlenecks, and service dependencies.

However, before exploring AWS X-Ray, let’s first understand what distributed systems are.

Distributed systems: Checkout flow for an e-commerce application

Let’s explore a common scenario in distributed systems using an e-commerce checkout flow to understand the challenges and how distributed tracing helps.

Imagine being part of a development team managing an e-commerce application on AWS. The checkout process seems straightforward to the user, but behind the scenes, it involves a complex series of interactions across multiple services.

Here’s the typical flow:

1. An API Gateway endpoint receives the checkout request.

2. This triggers a Lambda function in the order-service, which processes payments using Stripe.

3. Once payment is successful, the inventory-service (hosted on Fargate and backed by Amazon RDS) confirms stock and locks the items.

4. Finally, a notification-service sends a confirmation email via SNS and Lambda.

On the surface, everything appears fine. However, users start reporting that their orders aren’t going through, with no error messages on the frontend. The logs for the order-service indicate that the request was processed, and Stripe confirms successful transactions. Yet, some orders simply disappear; they never make it to the database or trigger email confirmations.

We check CloudWatch Logs across services, but nothing obvious stands out. Each service is logging its part of the process, but we lack the complete picture—the full flow of the request from one service to the next. This leaves us wondering: Where exactly is the request getting lost? This is the kind of ambiguity and difficulty in pinpointing the root cause that distributed tracing is designed to resolve.

Distributed tracing

Distributed tracing is a method of ...