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In a distributed system, things break in unexpected ways. That’s why observability isn’t optional—it’s how you understand what’s going on under the hood. If you’re comparing tools to instrument your services, OpenTelemetry and Micrometer are two names you’ll run into. Both are used to collect metrics, but they take very different approaches—especially when it comes to flexibility, vendor support, and what you can do with the data.

In this guide, we'll harness AppSignal to detect, diagnose, and remove performance bottlenecks and employ proper tracing in a Ruby on Rails application. From setting up tracing to capturing errors and logging, we’ve got you covered. We'll ensure our application runs smoother than ever, even under the heaviest loads! But first, let's quickly touch on how to define tracing and its benefits.

Both Grafana Tempo and Jaeger are distributed tracing tools designed for modern microservice architectures. Jaeger, released as an open-source project by Uber in 2015, has matured into a graduated CNCF project. Tempo, announced by Grafana Labs in October 2020, is a newer entrant focused on high-volume tracing with a unique storage architecture. Before comparing these tools in detail, let's quickly review what distributed tracing is and why it matters.

With the 3.0 release, Prometheus firmly established itself as the leading metrics database for OpenTelemetry. A lot of work has gone into integrating the two open source projects, including a major Prometheus enhancement we’re really excited about: resource attribute promotion.

Hariom Gupta Follow 4 min read· 1 hour ago -- Listen Share Starting this journey was both exciting and fulfilling — and now, here I am at the finish line, having successfully completed the LFX Mentorship Program and reflecting on the experience through this blog. The past three months have been incredible — surpassing my expectations in so many ways.

Distributed tracing has long been the go-to for understanding the performance of microservices and asynchronous systems. But as systems grow in complexity, simply viewing individual traces and spans isn’t enough. Teams need to answer questions like: SigNoz Tracing Funnels is here to change that, bringing the clarity of product analytics-style funnel analysis to backend traces, and doing so in a way that’s never been available before.

Modern engineering teams spend a lot of time and resources in setting up monitoring of their production systems - tracking uptime, catching errors, and responding to incidents before customers ever notice. But what about the journey before code reaches production? For most teams, observing the CI/CD pipeline is either an afterthought or completely overlooked. While we recognize its importance, do we truly understand how well our CI/CD process is functioning?

In today’s cloud-native world, third-party APIs are everywhere. Payments, notifications, search, AI, analytics as modern applications are built on a web of external services. But what happens when one of those APIs slows down, starts throwing errors, or gets rate-limited? Suddenly, your users are facing issues, and you’re stuck asking.

For a while, tracing in Sentry was... fine. You could open up a slow transaction, poke around, find the N+1, and feel like a hero. But if you wanted to answer more complex questions - like why your payment API was getting slower in Europe, or which CDN was silently tanking your image loads - things got harder. We didn't really build it to help with answering broad questions.

Implementing observability in Angular applications presents unique challenges. Understanding how users experience your application and identifying performance bottlenecks requires specialized tools and approaches. This guide covers implementing OpenTelemetry in Angular applications, with practical code examples for instrumentation, data collection, and integration with observability backends.