Operations | Monitoring | ITSM | DevOps | Cloud

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.

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.

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.

We are back with a simplified configuration for another critical open-source component, Hadoop. Monitoring Hadoop applications helps to ensure that the data sets are distributed as expected across the cluster. Although Hadoop is considered to be very resilient to network mishaps, monitoring Hadoop clusters is inevitable. Hadoop is monitored using the JMX receiver. The configuration detailed in this post uses observIQ’s distribution of the OpenTelemetry collector.

Although we’ve encouraged employees to take plenty of time off this summer to relax, recharge, and enjoy time with family, Cribl certainly hasn’t been on a summer holiday as a company. After the big announcement in late May with Cribl Search and our Series D funding round, we moved right into the announcement of Cribl Stream 3.5, Cribl Edge 3.5, massive upgrades to Cribl.Cloud, and the launch of our Cribl Certified Observability Program.