Application Monitoring, or Application Tracing, is an important piece of Observability within your application and stack. Application tracing involves installing an API and/or SDK in your application which then instruments, or wraps your application code with other code that measures the time spent in certain areas of your code, and adds important contextual information to the traces.

In a previous blog post, Adam Quan presented a great introduction to setting up observability for a Spring Boot application. For metrics, Adam used the Prometheus Java Client library and showed how to link metrics and traces using exemplars. However, the Prometheus Java Client library is not the only way to get metrics out of a Spring Boot app. One alternative is to use the OpenTelemetry Java instrumentation agent for exposing Spring’s metrics directly in OpenTelemetry format.

As more organizations adopt cloud-native technologies and microservices-based architectures, application troubleshooting is becoming increasingly complex. With so many moving parts in an environment that is both dynamic and distributed, it is difficult to get the full picture. Yet complete visibility is crucial in order to find and fix issues quickly — especially ones that impact the bottom line.

observIQ’s OpenTelemetry members contributed Riak metric monitoring support to OpenTelemetry! You can now monitor your Riak agent performance with OpenTelemetry, and deploy simply with the oIQ OpenTelemetry Collector. You can add the Riak metric receiver to any OpenTelemetry collector. This post demonstrates a configuration for shipping metrics to Google Cloud Operations with OpenTelemetry components.

Grafana Tempo 1.4 has been released and features a new optional component: metrics generator, which automatically generates RED metrics and service graphs from your traces. We’re actively rolling out the metrics-generator service to our own Grafana Cloud offering and are looking for Grafana Cloud Traces customers wanting early access. If interested, you can email our support team for more details.

As systems get ever more complicated with more layers in the application stack, it can become more difficult to keep track of what is happening at all of the different, discrete levels and this is where distributed tracing comes into play.

When do you delete instrumentation? You delete instrumentation when you delete code. Other than that, if you’re doing things right: almost never. One of the best things about honeycomb is that it completely transforms the incentives around preserving instrumentation. With metrics-based tools, the most valuable metrics are always custom metrics. You need to define a custom metric for literally any question you might ever want to ask about the app and its utilization or performance.

We’re excited to announce that we’ve recently contributed Redis monitoring support to the OpenTelemetry collector. You can check it out here! You can utilize this receiver in conjunction with any OTel collector: including the contrib collector, the observIQ’s distribution of the collector, as well as Google’s Ops Agent, as a few examples.

Spring Boot is a very popular microservice framework that significantly simplifies web application development by providing Java developers with a platform to get started with an auto-configurable, production-grade Spring application. In this blog, we will walk through detailed steps on how you can observe a Spring Boot application, by instrumenting it with Prometheus and OpenTelementry and by collecting and correlating logs, metrics, and traces from the application in Grafana Cloud.