Logging has been around since programming began. We use logs to debug issues and understand how software works at the code level. After logging and debuggers, profilers are a dev’s best friend when writing code and may run in production with limits to reduce overhead. As we distributed architectures — making systems more complex — centralized log aggregation was soon necessary. At that point, we had to analyze this data. Hence, log analytics technologies were born.

Intelligent Medical Objects (IMO) and its clinical interface terminology form the foundation healthcare enterprises need, including effective management of Electronic Health Record (EMR) problem lists and accurate documentation. Over 4,500 hospitals and 500,000 physicians use IMO products on a daily basis. With Honeycomb, the engineering team at IMO was able to find hidden architectural issues that were previously obscured in their logs.

In this two-part blog post, we’ll use Elastic Observability to monitor a sample Java application. In the first blog post, we started by looking at how Elastic Observability monitors Java applications. We built and instrumented a sample Java Spring application composed of a data-access microservice supported by a MySQL backend. In this part, we’ll use Java ECS logging and APM log correlation to link transactions with their logs.

Running systems in production involves requirements for high availability, resilience and recovery from failure. When running cloud native applications this becomes even more critical, as the base assumption in such environments is that compute nodes will suffer outages, Kubernetes nodes will go down and microservices instances are likely to fail, yet the service is expected to remain up and running.

Looking for a way to proactively troubleshoot complex application performance issues? Look no further than Tanzu Observability by Wavefront, which provides easy data ingestion and preconfigured dashboards and can be set up with Spring Boot and Spring Cloud Data Flow (SCDF) integrations.

Editor’s note: This post is a collaboration between Tim Tully, Splunk CTO, and Spiros Xanthos, Splunk’s vice president of product management for observability and IT Ops and previously the founder and CEO of Omnition. My love for the open-source software movement began with Linux in the ’90s and grew during my time at Yahoo! in the early days of Hadoop.

At Honeycomb, we talk a lot about eating our own dogfood. Since we use Honeycomb to observe Honeycomb, we have many opportunities to try out UX changes ourselves before rolling them out to all of our users. UX doesn’t stop at the UI though! Developer experience matters too, especially when getting started with observability. We often get questions about the difference between using our Beeline SDKs compared with other integrations, especially OpenTelemetry (abbreviated “OTel”).