If you've ever had a Java application slow down in production and struggled to pinpoint the cause, you know the pain of performance issues. Java is a powerful, high-level language, but it doesn’t come without challenges—especially when it comes to resource management, garbage collection, and thread handling. This guide will take you through everything you need to know about Java performance monitoring, from key metrics to tools and best practices.

Modern application observability is essential for ensuring system performance, diagnosing issues, and optimizing user experiences. OpenTelemetry (Otel) and Grafana serve as two key components in achieving end-to-end visibility. While OpenTelemetry focuses on instrumenting applications to collect telemetry data, Grafana specializes in visualizing this data, making it actionable and insightful.

If you’ve ever struggled with understanding distributed traces, managing metrics, or debugging complex applications, OpenTelemetry is your best friend. But what about the OpenTelemetry UI? How do you visualize and interact with all that telemetry data? In this guide, we’ll explore the best ways to use OpenTelemetry’s UI options, from setting up a proper observability stack to choosing the right front-end visualization tools.

Imagine this: A customer tries to log in to your app, but the page takes too long to load. Frustrated, they leave. Meanwhile, your IT team has no clue there was an issue—until complaints start pouring in. Sound familiar? Performance lags are the new downtime. Lags are not just an inconvenience—they lead to lost revenue and frustrated users. To prevent this, organizations turn to application performance monitoring (APM) and synthetic monitoring to maintain peak application performance.

In early January, we announced the launch of InfluxDB 3 Core and InfluxDB 3 Enterprise in public alpha. One of the newest included features is the InfluxDB 3 Processing Engine–a Python-based VM built to enable data transformation, enrichment, downsampling, alerting, and more, all from within the database itself. One month later, we’re excited to deliver a big update enabling new ways to interact with and transform your data.

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!