Monitoring and analytics have been an issue for Serverless systems since they were invented. While it’s easy to attach an agent like NewRelic or DataDog to a server or container, function monitoring requires a different approach. Serverless applications, where logic is distributed over a large number of functions, attaching agents or wrappers leads to cost increase and development overhead.

Dave Widener is CTO of MyHealthMath, a provider of health plan decision support tools which simplify every aspect of open enrollment. Widener is executive leader for the company's entire technology function including R&D, strategic planning, cybersecurity, and technical product execution. Widener’s career encompasses IT management and leadership roles in the energy, life sciences, consumer products and banking sectors.

From digital transformation to infrastructure optimization, business analytics and data visualization allow IT stakeholders to make sense of complex situations. You can’t improve what you can’t measure, right? More and more, we see IT organizations combining different sources of information to uncover unique insight, allowing them to detect areas of improvement, find new opportunities, optimize processes and gain that competitive edge.

Securing access to business resources has always been of high priority for admins and IT teams. In the wake of the pandemic, workforces are more distributed than ever before, and 76 percent of global office workers state that they would like to work from home even when the pandemic is over.

With the advanced containerization that has become the norm in the modern cloud, your infrastructure is likely more distributed, and thus more exposed to networking issues, than ever before. When troubleshooting application performance issues, this can make it difficult to link the symptoms you observe through monitoring the “golden signals” (requests, latency, and errors) on individual endpoints in your application to their underlying root causes.

Grafana Tempo, the recently announced distributed tracing backend, relies on integrations with other data sources for trace discovery. Tempo’s job is to store massive amounts of traces, place them in object storage, and retrieve them by ID. Logs and other data sources allow users to quickly and more powerfully jump directly to traces than ever before. Previously we investigated discovering traces with Loki and exemplars.

Performance monitoring is great because it lets you see whether your application is fast or slow, and which parts need speeding up. For Node developers, those “parts” are most often endpoints handling incoming requests. Since the introduction of our performance monitoring offering in July 2020, Node devs have been able to use the Sentry SDK, @sentry/node, to measure the total time it takes to process each request, but we made some significant improvements since then.