Operations | Monitoring | ITSM | DevOps | Cloud

Log tracking, trace log, or logging traces… Although these three terms are easy to interchange (the wordplay certainly doesn’t help!), compare tracing vs. logging, and you’ll find they are quite distinct. Logs, traces, and metrics are the three pillars of observability, and they all work together to measure application performance effectively. Let’s first understand what logging is.

Enterprises are increasingly adopting Kubernetes for the value that it brings to their organizations, from IT cost savings to improved time to market for application development. But with this shift comes a fundamental challenge: how to gain comprehensive visibility into your Kubernetes applications, when most existing monitoring tools are hard to scale or provide little or no visibility into Kubernetes? This challenge stems from two unique characteristics of Kubernetes. One, it is ephemeral.

With the release of Splunk 9.0 came support for SmartStore in Azure. Previously to achieve this, you’d have to use some form of S3-compliant broker API, but now we can use native Azure APIs. The addition of this capability means that Splunk now offers complete SmartStore support for all three of the big public cloud vendors. This blog will describe a little bit about how it works, and help you set it up yourself.

If you’re new to the concept or just trying to keep up with the conversation, Gartner defines Observability as the evolution of monitoring into a process that offers insight into digital business applications, speeds innovation and enhances customer experience. Some folks think that Observability is a new buzzword, but in fact the term was coined in 1960 by Rudolf E. Kalman, a Hungarian-American engineer.

Have you ever had a tough time debugging your Python code? If yes, learning how to set up logging in Python can help you streamline your debugging workflow. As a beginner programmer, you’ll have likely used the print() statement—to print out certain values across runs of your program—to check if the code is working as expected. Using print() statements to debug could work fine for smaller Python programs.

Tracing, or more specifically distributed tracing or distributed request tracing, is the ability to follow a request through a system, joining the dots between all the individual system calls required to service a particular request. Although tracing logs have been around for some time, the trend toward distributed architectures, microservices, and containerization has elevated it from nice-to-have status to an essential piece of the observability puzzle.

Today we are rolling out our new "operation log viewer." This feature is not just a new page to show logs but a whole new way to find logs for actions taken by you and your team on your applications. The new log viewer page supports historical deployment logs, allowing you to see logs from previous actions.