For those who have limited experience with OpenTelemetry, it can be intimidating to instrument.NET applications. But the OpenTelemetry community created a welcome shortcut with the first stable release of.NET Automatic Instrumentation. It simplifies the process of collecting metrics, logs, and traces from your.NET applications, without applying any changes to the source code or adding any dependencies to the OSS project.

In the intricate web of modern software systems and full-stack observability, knowing how requests flow and interact across distributed components is paramount. Distributed tracing tools can help you. To better understand how distributed tracing works and benefits, here’s our selection of top distributed tracing tools to choose from.

Maciej Nawrocki, Senior Backend Developer at Bright Inventions, is a backend developer focused on DevOps and monitoring. Adam Waniak, Senior Backend Developer at Bright Inventions, is a backend developer with a keen interest in DevOps. Bright Inventions is a software consulting studio based in Gdansk, Poland, with expertise in mobile, web, blockchain, and IOT systems. At Bright Inventions, we always prioritize app optimization when we develop software solutions for our clients.

As the second largest and active Cloud Native Computing Foundation (CNCF) project, OpenTelemetry is well on its way to becoming the ubiquitous, unified standard and framework for observability. OpenTelemetry owes this success to its comprehensive and feature-rich toolset that allows users to retrieve valuable observability data from their applications with low effort. The OpenTelemetry Java agent is one of the most mature and feature-rich components in OpenTelemetry’s ecosystem.

In an era where “cloud-native” has become synonymous with complexity and distribution, the world of application monitoring faces a profound challenge.

As distributed, interconnected microservices have replaced monolithic applications, application monitoring has had to evolve to support these modern, complex architectures. Rather than monitoring a single application and code base, organizations need to monitor the performance and network connectivity of multiple services that interact with each other.

This is part three in a series where I learn OpenTelemetry (OTEL) from scratch. If you haven't yet seen them yet, part 1 is about setting up auto-instrumented tracing for Node.js and part 2 is where I initially implemented the OTEL collector. Today we are going to begin experimenting with sampling. We need to sample traces because we capture so much data! It would be impractical to process and store it all (in most cases).

Massdriver is a cloud operations platform that makes it easier for engineering teams to build, deploy, and scale cloud-native applications. While many companies use this lofty language to make similar promises, Dave Williams, CTO and co-founder at Massdriver, means it. Before Massdriver, Dave worked in product engineering where he was constantly bogged down with DevOps toil. He spent his time doing everything except what he was hired to do: write software.

Who is software for? It’s an interesting question, because there’s an obvious answer. It’s for the users, right? If your job is to write software, then it’s implied that the most important thing you should care about is the experience people have when they use your software.

Distributed transaction tracing (DTT) is a way of following the progress of message requests as they permeate through distributed cloud environments. Tracing the transactions as they make their way through many different layers of the application stack, such as from Kafka to ActiveMQ to MQ or any similar platform, is achieved by tagging the message request with a unique identifier that allows it to be followed.

AWS’s serverless technologies are popular because they provide cost effective scaling and great separation of concerns. However, observing serverless architectures like Lambda is challenging due to their transient nature and abstracted infrastructure. Unlike traditional systems with consistent hosts, serverless functions are ephemeral, often scaling rapidly and operating in isolation.

In the previous article I covered how to set up auto-instrumented tracing for a Node.js app using OpenTelemetry (OTEL). We then sent the spans directly to the open source tracing tool Jaeger. I recommend you give that a read first before walking through this guide because we're going to re-use the instrumentation we set up last time. Today we're going to take things a step further by introducing the OpenTelemetry Collector.

OpenTelemetry (OTel) is an open-source, vendor-neutral observability solution that provides a suite of components—including APIs, SDKs, and a data collector—that enable teams to collect and communicate telemetry data from cloud-native applications and services. OTel also defines the OpenTelemetry Protocol (OTLP), a standard for the encoding and transfer of telemetry data.

Kubernetes is one of the most important and influential technologies for building and operating software today because it’s so incredibly capable. It’s flexible, available, resilient, scalable, feature-rich and backed by a global community of innovators — that’s a pretty impressive list of intangibles to apply to any particular capability.