When it comes to monitoring Golang applications, there are various tools and practices you can use to gain insights into your application's performance, resource usage, and potential issues. By using OpenTelemetry for monitoring in your Go applications, you can gain valuable insights into the behavior, performance, and resource utilization of your distributed systems, allowing you to troubleshoot issues, optimize performance, and improve the overall reliability of your software.

When your system shows a "no healthy upstream" error, it usually means your load balancer can't connect to any of your backend servers. Let's get straight to fixing this problem.

Jaeger, a renowned distributed tracing system, has been a trusted companion for developers and operations teams seeking to unravel the complexities of microservices architectures. However, as the landscape continues to evolve, the time has come to explore Jaeger alternatives that offer distinct features and advantages.

This is an updated version of an earlier blog post to reflect current definitions of a telemetry pipeline and additional capabilities available in Mezmo Our recent blog post about observability pipelines highlighted how they centralize and enable telemetry data actionability. A key benefit of telemetry pipelines is users don't have to compare data sets manually or rely on batch processing to derive insights, which can be done directly while the data is in motion.

As more organizations move their databases to cloud-native environments, effectively managing and monitoring these systems becomes crucial. According to Coroot’s anonymous usage statistics, 64% of projects use PostgreSQL, making it the most popular RDBMS among our users, compared to 14% using MySQL. This is not surprising since it is also the most widely used open-source database worldwide.

As the Product Manager for Honeycomb’s new frontend product, Honeycomb for Frontend Observability, I’ve had the joy this past year of speaking to dozens of frontend engineering teams about observability. Many frontend teams come from worlds where they either rely on QA and customer reports to identify issues in production, or they use real use monitoring (RUM) and error monitoring tools to catch the most egregious issues.

Just like how the filling makes a sandwich, an application's performance is closely tied to how effectively its middle-tier components function. While the front-end is what users see and interact with (UI), and the back-end deals with data storage, the middle tier forms the vital core where the real magic happens—processing, logic implementation, and enforcement of business rules.

DataDog and Prometheus are both popular monitoring solutions used to collect and analyze metrics and monitor the performance of systems, but Prometheus is open source and Datadog is proprietary. Datadog provides a unified platform for monitoring, troubleshooting, and optimizing modern cloud-native applications and infrastructure. Prometheus is the most popular tool for monitoring time series metrics. So, how to choose between Datadog and Prometheus?

The stress, sudden disruptions, and high stakes of resolving issues while on call is one of the most challenging aspects of an engineer’s job. Many organizations, from startups to large enterprises, still struggle with their on-call experience, which leads to longer resolution times and lower employee retention rates. Constant context switching, managing multiple tools, and racing against time to resolve issues can cause frustration, burnout, and inefficiency.

Jaeger and Prometheus are both popular open source monitoring systems, but they serve different purposes and focus on different aspects of observability in a distributed system. Learn how these tools are similar, how they're different, and when you should use each one.