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The latest News and Information on Distributed Tracing and related technologies.

Proactively troubleshoot with synthetic testing and distributed tracing

As your application grows in complexity, identifying the root cause of issues becomes increasingly difficult. Many monitoring strategies make this even harder by siloing frontend and backend data. To effectively troubleshoot problems that spread across your app, you need visibility not just into each part of your stack, but also into how these parts interact.

Jaeger Metrics: Internal Operations and Service Performance Monitoring

You're monitoring a microservices-based system. Alerts trigger when response times exceed 2 seconds. But when you open Jaeger, you're faced with thousands of traces. Identifying which service or operation is responsible becomes time-consuming. Jaeger metrics help reduce this friction by exposing aggregated telemetry. Instead of scanning individual traces, you get service-level and operation-level performance metrics, latency, throughput, and error rates that highlight where the issue lies.

Datadog vs Jaeger - Features, Pricing & Use Cases [Updated for 2025]

Datadog and Jaeger are both leading tools in the observability space, but they represent two fundamentally different philosophies. Datadog is a commercial, all-in-one SaaS platform that unifies metrics, traces, and logs. Jaeger is a popular, open-source project focused specifically on distributed tracing. Choosing between them isn't just a technical decision; it's about balancing the convenience of a fully managed, integrated platform against the power and control of a self-hosted, specialized tool.

What Are Traces? A Developer's Guide to Distributed Tracing

One of the most common challenges in modern software engineering today is understanding how requests flow through applications. As system architectures shift to favor widely distributed, cloud-native designs, keeping track of how an application processes user actions is more difficult than ever. A single user action may trigger events processed in dozens of backend services. Traces are helping software developers today with this challenge.

OpenTelemetry Collector: A Complete Guide [2025]

The OpenTelemetry Collector is a stand-alone service that acts as a powerful, vendor-neutral pipeline for your telemetry data. It can receive, process, and export logs, metrics, and traces, giving you full control over your observability data before it reaches a backend. This guide will provide a comprehensive overview of the OpenTelemetry Collector, its architecture, deployment patterns, and how to configure it for production use.

Improve Consistency Across Signals with OTel Semantic Conventions

It’s 2 AM. Your API is timing out. Logs show a slow query. Metrics flag a spike in DB connections. Traces reveal a 5-second delay on a database call. But then the questions start:- Which database?- Does the query match the delay?- Why doesn’t this align with the connection pool metrics? Each tool uses different labels, db.name, database, sometimes nothing at all. Without a shared schema, connecting the dots is slow and frustrating.

Perform Distributed Tracing for your MCP system with OpenTelemetry

2025 has truly been the year of Agentic AI, with MCP (Model Context Protocol) emerging as one of its flashy and most talked-about innovations. While many products have seamlessly integrated MCP servers into their systems, these servers are increasingly being labelled as black boxes, opaque components that handle critical tasks but offer little visibility into what’s happening under the hood. We prompt an agent, a tool gets invoked, and a response is generated. But what really happens in between? And when something breaks, how do we trace the failure and debug it effectively?

Faster incident response through distributed tracing: Inside Glovo's use of Traces Drilldown

It’s almost 1 p.m. on a Monday afternoon and you’re hungry. You pull up your meal delivery app and select your favorite restaurant and dish. Then you go to check out and nothing happens. Your frustration mounts as you get hungrier by the minute. But there’s frustration on the other side of that transaction as well—engineers are scrambling to figure out what’s wrong as orders drop and revenue losses rise.