Operations | Monitoring | ITSM | DevOps | Cloud

In monitoring setups, working with a single metric rarely tells the complete story. The real power of Prometheus lies in its ability to query multiple metrics simultaneously, creating connections between different data points that reveal the true state of your systems. This guide will walk you through everything you need to know about crafting effective multi-metric queries in Prometheus – from basic concepts to advanced techniques that will help you monitor and troubleshoot your infrastructure.

In cloud environments, logs are often spread across numerous services, making it difficult to track down issues or gather meaningful insights. For AWS users, this challenge can become especially time-consuming. Centralized logging in AWS helps by bringing all your logs into a single platform, making management and analysis easier.

In modern microservices architectures, container observability is crucial for maintaining reliability and performance. It helps teams detect issues early and optimize distributed systems. This guide will walk you through the essentials of container observability, including advanced techniques and troubleshooting strategies to ensure your containerized applications run smoothly.

Logs play a crucial role in DevOps and software development, especially when troubleshooting issues. However, raw, unformatted logs can quickly become overwhelming and difficult to navigate. This is where logging formatters help by turning messy log entries into clear, structured data, making it easier to pinpoint problems. In this guide, we’ll cover everything you need to know about logging formatters—how they work, why they matter, and tips for implementing them effectively in your workflow.

When Java web applications experience slowdowns or crashes, the culprit is often the Tomcat server. For DevOps engineers overseeing critical applications, proactive monitoring is crucial for ensuring optimal performance and reliability. In this guide, we'll explore the essential aspects of monitoring Apache Tomcat servers, focusing on the key metrics to track, setting up robust monitoring systems, and troubleshooting common performance issues that could impact your application’s stability.

Understanding what’s happening inside your applications is key to keeping them performing well and reliably. OpenTelemetry tracing is an open-source, flexible solution that lets you monitor your distributed systems without locking you into a specific vendor. reliably This guide walks you through everything you need to know about OpenTelemetry tracing, from the basics to more advanced techniques, with practical tips for troubleshooting common issues along the way.

When your microservices architecture starts growing, tracking requests as they bounce between services becomes a real headache. You know the feeling—a user reports a slow checkout process, and you're left wondering which of your twenty services is the bottleneck. That's where distributed tracing with Prometheus comes in.

In today's connected world, APIs are the backbone of modern applications. Whether you're working on a microservices architecture, a mobile app, or a SaaS platform, APIs are what keep everything talking to each other. But how do you know if your APIs are healthy, performing well, and delivering what your users need? That's where API monitoring comes in. Let's break down what API monitoring is, why it matters, and how you can build effective API metrics dashboards to keep your systems running smoothly.

Modern systems throw off a lot of data—metrics, traces, logs—sometimes more than we know what to do with. When you're trying to understand how values spread out over time (like response times, memory usage, or queue lengths), averages alone don’t tell the full story. OpenTelemetry histograms help fill in those gaps. This guide walks through what they are, why they matter, and how DevOps engineers can use them to improve observability in real systems.

You’re staring at logs, trying to figure out what caused that odd error in the middle of the night. Or maybe you're following a chain of requests across services, hoping to understand how one user action triggered a series of unexpected behaviors. That’s where distributed tracing and request tracking—specifically, correlation IDs and trace IDs—are invaluable. It’s the kind of detail that can make debugging faster and less painful.