In this tutorial, we will go through a working example of a NestJS application auto-instrumented with OpenTelemetry. In our example we will use a simple application that outputs “Hello World!” when we call it in the browser. We will instrument this application with OpenTelemetry’s Node.js 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.

OpenMetrics and OpenTelemetry are popular standards for instrumenting cloud-native applications. Both projects are part of the Cloud Native Computing Foundation (CNCF) and aim to simplify how we generate, collect and monitor services in a modern cloud-native distributed application environment. Let's have a look at how both the standards are aiming to help solve the observability conundrum.

Application performance monitoring (APM) solutions were designed to catch anomalies in an application or website's backend and provide meaningful insights to rectify issues in real time. Lately, though, APM solution providers have been left playing catch-up to be more inclusive of newly emerging technologies and the operational challenges they bring. OpenTelemetry (OTel) simplifies the issues caused by the demands of modern applications.

We’re excited to announce that we’ve recently contributed Active Directory Domain Services (abbreviated Active Directory DS) monitoring support to the OpenTelemetry collector. You can check it out here! You can utilize this receiver in conjunction with any OTel collector: including the contrib collector, the observIQ’s distribution of the collector, as well as Google’s Ops Agent, as a few examples.

Microservices are a popular architectural style for building applications that are resilient, highly scalable, independently deployable, and able to evolve quickly. But a successful microservices architecture requires a different approach to designing and building applications. A microservices architecture consists of a collection of small, autonomous services. Each service is self-contained and should implement a single business capability within a bounded context.

MongoDB is a document-oriented and cross-platform database that maintains its documents in the binary encoded JSON format. Mongo’s replication capabilities and horizontal capability using sharding make MongoDB highly available. An effective monitoring solution can make it easier for you to identify issues with MongoDB such as resource availability, execution slowdowns, and scalability. observIQ recently built and contributed a MongoDB metric receiver to the OpenTelemetry contrib repo.

Efficient use of observability statistics is essential to any microservice architecture. OpenTelemetry is a project supported by the Cloud Native Computing Foundation (CNCF) to enhance the observability of microservice projects. AWS Distro for OpenTelemetry (ADOT) is an AWS-supported distribution of the OpenTelemetry project specifically designed to improve the observability of AWS projects.

Dear Miss O11y, I’ve been told I need to use the OpenTelemetry Collector, but I have no idea what it is, or why I need it? Should I be using it? What value does it add? What the hell am I missing?

Splunk is all-in on OpenTelemetry, as exemplified by our native support for it in Observability Cloud, Splunk Enterprise and Enterprise Cloud’s usage of the OpenTelemetry Collector with Splunk Connect for OpenTelemetry Kubernetes, our long-term ambition to use OpenTelemetry as the main way that all Splunk Products capture data from customers’ infrastructure and applications for analysis, and our massive level of contribution to the project.