OpenTelemetry is an open-source observability framework that provides a vendor-neutral and language-agnostic way to collect and analyze telemetry data. This tutorial will show you how to integrate OpenTelemetry on Kubernetes, a popular container orchestration platform. Prerequisites.

OpenTelemetry (OTel) is an open-source observability framework that provides a standardized way of collecting, processing, and exporting telemetry data (metrics, traces, and logs) from distributed systems. It was born by a merger between two previously separate observability projects, OpenCensus and OpenTracing, and it is currently maintained by the Cloud Native Computing Foundation (CNCF).

Adopting a distributed tracing solution to make an application more observable and maintainable is one of the most common key initiatives modern R&D teams have on their plates currently. With the move to microservices architectures, development teams are finding that it’s taking them longer to build applications due to tasks that are growing in complexity.

OpenTelemetry and DataDog are both used for monitoring applications. While OpenTelemetry is an open source observability framework, DataDog is a cloud-monitoring SaaS service. OpenTelemetry is a collection of tools, APIs, and SDKs that help generate and collect telemetry data (logs, metrics, and traces). OpenTelemetry does not provide a storage and visualization layer, while DataDog does.

Distributed Tracing, combined with end-to-end monitoring, lets organizations keep track of their business transactions and receive alerts when anomalies happen. End-to-end monitoring benefits business users and functional administrators more than developers and technical administrators. Nowadays, for developing integration solutions, microservice architectures are becoming the norm. A solution based on such an architecture relies on different services that shape a specific solution together.

OpenTelemetry (OTel in short) is an open-source observability framework that provides a standard set of vendor-agonistic SDKs, APIs, and tools to connect with observability backends. It supports all major programming languages, including Java, Python, Node.js, and Go. However, Golang tracing by integrating OTel with Golang is particularly challenging due to several reasons.

So you're used to debugging systems using a distributed trace, but your system is about to introduce a message queue—and that will work the same… right? Unfortunately, in a lot of implementations, this isn't the case. In this post, we'll talk about trace propagation (manual and OpenTelemetry), W3C tracing, and also where a trace might start and finish.

Distributed tracing enables you to monitor and observe requests as they flow through your distributed systems to understand whether these requests are behaving properly. You can compare tiny differences between multiple traces coming through your microservices-based applications every day to pinpoint areas that are affecting performance. As a result, debugging and troubleshooting are simpler and faster.

Monitoring and troubleshooting distributed systems like those built with microservices is challenging. Traditional monitoring tools struggle with distributed systems as they were made for a single component. Distributed tracing solves this problem by tracking a transaction across components. In this article, we will implement distributed tracing for a Java Spring Boot application with three microservices.