Operations | Monitoring | ITSM | DevOps | Cloud

Sentry uses Issue Grouping to aggregate identical errors and prevent duplicate issues from being created, and duplicate alerts being sent. One of the chief complaints we’ve heard from our users is that in some cases the existing algorithm did not sufficiently group similar errors together, and Sentry would create separate issues and alerts, causing unnecessary disruption–or at least annoyance–to developers.

Amazon CloudWatch is a monitoring and observability service that provides real-time insights into AWS resources and applications. While CloudWatch provides many default metrics, sometimes you need custom metrics to monitor specific aspects of your infrastructure or applications. This guide covers everything you need to know about CloudWatch custom metrics, from basics to advanced use cases.

Understanding how your applications perform is crucial. OpenTelemetry has emerged as a powerful observability framework, offering a standardized approach to collecting telemetry data such as metrics, logs, and traces. For Java developers, the OpenTelemetry Java SDK provides the tools necessary to instrument applications effectively. This guide is all about the OpenTelemetry Java SDK, exploring its components, configuration, and advanced features to help you harness its full potential.

This post was authored by Mitra Goswami, Ralph Bird, Everaldo Aguiar, and Scott Sieper. Over the past two years, generative AI (GenAI) has come a long way, from the early excitement of ChatGPT to early explorations and more and more companies deploying GenAI-powered features into production.

We're excited to announce that AppSignal now offers improved monitoring for long-running streaming Rack responses. Our improved Rack response monitoring means you can gain deeper visibility into the health of your Ruby application's long-running responses, allowing you to catch errors that may arise minutes or even hours after a request's body is served. This new layer of observability results from a valuable contribution from Julik Tarkhanov, Director of Engineering at Cheddar Payments.

What happens if we put OpenTelemetry, Prometheus 2.x, Prometheus 3.x, and vmagent together for comparison in scraping and pushing data to remote storage?

Every server administrator’s nightmare starts with a message: “CPU usage at 100%” It’s that critical moment when your Linux server transforms from a reliable workhorse into a sluggish mess, taking your applications and user experience down. We’ve all been there… staring at a terminal, watching load averages climb, while frantically trying to figure out which process decided to throw a CPU-hungry party on our server.

Imagine you’ve got a leaky faucet in your kitchen. At first, it’s just a drip here and there—annoying, sure, but not enough to ruin your day. But leave it unchecked, and soon that drip turns into a steady trickle. Your water bill skyrockets, the sink overflows, and before you know it, you’re ankle-deep in chaos. Now, replace that faucet with a Linux system, and you’ve got a memory leak.

Disk IO (Input/Output) is a core aspect of system performance. Whether you’re managing a database, a web application, or a cloud server, how efficiently your system reads and writes data affects everything from response times to stability. Unlike high CPU usage or memory bottlenecks that often manifest immediately, disk IO issues tend to creep up silently—until they slow down critical processes.

One of the hardest parts of my job is to get people to appreciate just how much of a difference Honeycomb/observability 2.0 is compared to their current way of working. It’s not just a small step up or a linear improvement. Rather, it’s an entire step change in the way that you write, deploy, and operate software for your customers.