I’ve spent most of my career working with tech in various forms, and for the last ten years or so, I’ve focused a lot on building, maintaining, and operating robust, reliable systems. This has led me to put a lot of time into researching, evaluating, and implementing different solutions for automatic failure detection, monitoring, and more recently, observability. Before we get started: What is observability?

In this blog series, we are covering application instrumentation steps for distributed tracing with OpenTelemetry standards across multiple languages. Earlier, we covered Java Application Manual Instrumentation for Distributed Traces, Golang Application Instrumentation for Distributed Traces, and DotNet Application Instrumentation for Distributed Traces. Here we are going to cover the instrumentation for NodeJS.

Jaeger is a popular open-source tool used for distributed tracing in a microservice architecture. In a microservice architecture, a user request or transaction can travel across hundreds of services before serving what a user wants. Distributed tracing helps to track the performance of a transaction across multiple services. Before we deep dive into how Jaeger accomplishes distributed tracing for microservices-based architecture, let's take a short detour to understand distributed tracing.

We're excited to help our customers further leverage the benefits of OpenTelemetry via integration with AWS Distro for OpenTelemetry (ADOT) for traces 1.0.

Modern application architectures are complex, typically consisting of hundreds of distributed microservices implemented in different languages and by different teams. As a developer, SRE, or DevOps engineer, you are responsible for the reliability and performance of these complex systems. But while you might have metrics that will help you debug when there’s an issue, metrics alone can’t help you narrow down and ultimately identify the root cause.

Our log agent is powerful, efficient, and highly adaptable. Now, with OpenTelemetry setting new standards in the observability space, we wanted to incorporate that collaboration into our log agent and offer our users the ability to take advantage of the OpenTelemetry ecosystem. Starting today, you can upgrade the log agents in your observIQ account to the new Open Telemetry-based observIQ log agent with a single click.

Sampling is the practice of extracting a subset of data from a dataset to make conclusions about that larger dataset. It’s far from a perfect solution, but when it’s implemented with Refinery, Honeycomb’s trace-aware sampling proxy, sampling can help you manage very high volumes of complex event data.

It is no surprise that monitoring workloads are top of mind for many organizations to ensure a successful customer experience. As our applications become more distributed and cloud-native, we find that monitoring can become more complex. A single user transaction fans out to interact with tens or hundreds of microservices, each one requesting data from backend data stores or otherwise interacting with each other and other parts of your infrastructure.

Both AWS X-Ray and Jaeger are distributed tracing tools used for performance monitoring in a microservices architecture. Jaeger was originally built by teams at Uber and then open-sourced in 2015. On the other hand, AWS X-Ray is a distributed tracing tool provided by AWS specifically focused on distributed tracing for applications using Amazon Cloud Services. Jaeger is a popular open-source tool that graduated as a project from Cloud Native Computing Foundation.

In this tutorial, we will go through a working example of a Ruby application auto-instrumented with OpenTelemetry. To keep things simple, we will create a basic “Hello World” application, instrument it with OpenTelemetry’s Ruby client library to generate trace data and send it to an OpenTelemetry Collector. The Collector will then export the trace data to an external distributed tracing analytics tool of our choice.