Operations | Monitoring | ITSM | DevOps | Cloud

When discussing observability, the “big 3” - logs, metrics, and traces, always get mentioned. But for some, more data doesn’t always mean better. Our lead engineer, JJ, had some advice to share about how logs may not be necessary for everyone. Simplifying your observability stack isn’t difficult - you just need to be intentional with implementation. Check out more MetricFire blog posts below, and our hosted Graphite service! Get a free trial and start using MetricFire now!

As modern applications grow more complex, debugging becomes increasingly challenging. Applications consist of multiple parts which can generate enormous amounts of log data, making debugging difficult. SolarWinds Loggly can help store, manage, and sift through this data. To demonstrate, we’ll set up an application built on.NET Core 9.0 and MongoDB; then, we’ll walk through how to export its logs to Loggly.

The dreaded 500 internal server error is a common challenge for web administrators, often signaling a disruption in server operations. Diagnosing the root cause requires in-depth visibility into both web server and application behavior. In this blog, we’ll explore how log management tools simplify the diagnosis and resolution of 500 errors by leveraging insights from both Apache and Tomcat logs.

Apache ActiveMQ is a widely used message broker that enables seamless communication between distributed applications. However, as the volume of messages increases, performance bottlenecks can arise, leading to slow message processing, high latency, broker crashes, and out of memory (OOM) errors. One of the most critical issues affecting ActiveMQ is OOM errors, which occur when the broker exceeds its allocated heap memory. This can result in service failures, message loss, and prolonged downtime.

We're only one month into 2025, but the momentum keeps building at Bindplane. In January, we rebranded our company as Bindplane, aligning our company name with our core mission: delivering the best OpenTelemetry-native telemetry pipeline on the market. Building on that excitement, we have another announcement: we've expanded and extended our partnership with Google Cloud.

While Kubernetes aims to streamline containerized application management, its multi-layered architecture creates potential points of failure. Problems in any of these layers can manifest as application crashes, resource overutilization, or failed deployments, making cluster maintenance a persistent challenge. Kubernetes meticulously logs all aspects of cluster activity and application output, from individual Pods to ReplicaSets.