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.

OpenTelemetry is an open-source standard for instrumenting cloud-native applications for generating different types of telemetry data. A robust observability framework set up using OpenTelemetry can help tremendously while troubleshooting software in production. Nginx is one of the most widely adopted web servers. Most often, nginx is used as a reverse proxy. It serves the frontend or backend applications behind the reverse proxy.

OpenTelemetry consists of several main components. But to understand the architecture, we will start by understanding some of the key design principles behind the design and architecture of OpenTelemetry. But before that, let us have a brief overview of OpenTelemetry.

Any application you build has three distinct layers - a server-side interface, a client-side interface and the central codebase. In a monolith application, all these programs are written in a single language, and placed in the same web stack as well. Earlier web applications were written this way.

As an open source company that grew out of a side project in 2008 to an application and performance monitoring platform (APM) used by over 3.5 million developers, Sentry is committed to open source and the community of developers maintaining and building in the open. Similarly, we take a public approach to building our software, which is why it’s a natural extension of our values to announce our support for OpenTelemetry (or OTel), the leading open standard for observability.

In this blog, we will explore how distributed tracing works in Application Insights and how to use it to diagnose the issues in a distributed application. Azure Application Insights is a powerful tool for monitoring and diagnosing application performance issues and supports distributed tracing. It is an extension of Azure Monitor and provides Application Performance Monitoring features out of the box.

Instrumenting your code is essential to understanding your system’s performance and diagnosing issues as they arise. Traditionally, this was accomplished using proprietary vendor libraries, causing major lock-in. Enter OpenTelemetry. OpenTelemetry is an open-source project that provides a set of APIs, SDKs, and integrations for instrumenting code.

Serverless developers are undoubtedly familiar with the challenge of cold starts, which describe spikes in latency caused by new function containers being initialized in response to increasing traffic. Though cold starts are usually rare in production deployments, it’s still important to understand their causes and how to mitigate their impact on your workload.

This post was originally published on the Tracetest blog. Want to run trace-based tests with Elastic APM? Today is your lucky day. We're happy to announce that Tracetest now integrates with Elastic Observability APM. Check out this hands-on example of how Tracetest works with Elastic Observability APM and OpenTelemetry! Tracetest is a CNCF project aiming to provide a solution for deep integration and system testing by leveraging the rich data in distributed system traces.

OpenTelemetry (OTel) and distributed tracing can be handy tools when you’re a developer facing what we call app flow blindness – or not being able to see your application flows and microservices components in a distributed cloud environment. In distributed environments, application flows are handled by various services and cloud entities which are generally siloed.

Jaeger’s HotROD demo has been around for a few years. It was written with OpenTracing-based instrumentation, including a couple of OSS libraries for HTTP and gRPC middleware, and used Jaeger’s native SDK for Go, jaeger-client-go. The latter was deprecated in 2022, so we had a choice to either convert all of the HotROD app’s instrumentation to OpenTelemetry, or try the OpenTracing-bridge, which is a required part of every OpenTelemetry API / SDK.

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 with Amazon AWS Fargate, a container orchestration service that allows you to run and scale containerized applications without managing the underlying infrastructure.

At Grafana Labs, we love tracing, which is why we’ve been hard at work on Grafana Tempo, an open source, highly scalable distributed tracing backend. Tempo just had its 2.0 release. In conjunction with that release, we are excited to show off TraceQL — a powerful new query language designed for distributed tracing. In this blog, we’ll provide an overview of why we created TraceQL, how it works, how you can put it to use today, and what we have planned for future iterations.

As engineering organizations transitioned from monolith to microservices architectures, they sought to make their development efforts more scalable and manageable. The microservices paradigm promised to increase development speed, reduce MTTR and improve quality while cutting down on maintenance costs. However, in real life, there are inherent quality caveats when it comes to developing microservices.

OpenTelemetry (OTel) is an open source, vendor-neutral observability framework that supplies APIs, SDKs, and tools for the instrumentation of cloud-native applications and services. OTel enables you to collect metrics, logs, and traces from a variety of sources and route them to various backends. By itself, however, it can’t help you analyze this data or correlate telemetry from different parts of your stack.