Running the JFrog DevOps Platform on Kubernetes in your enterprise can mean serving millions of artifacts to developers and customers each day. But operating at top performance requires being able to answer some vital questions. Like what is the most requested artifact? What is the most popular repo? Who are your heaviest users? For security, which users are doing bad things, and from which IPs?

Most modern software today has moved aggressively into using third-party open source dependencies to reduce duplication and accelerate development by using pre-existing code.

We’re excited to announce that the KUDO ( Kubernetes Universal Declarative Operator) project is now officially accepted as a CNCF Sandbox project. This was the culmination of two years of hard work from the team and is a fantastic step forward for the project.

Driven by digital market shifts, organizations are adopting cloud and cloud-native technologies to deliver a better end-user experience, scale efficiently — both up and down —and increase innovation velocity. While distributed cloud architecture brings agility, it also brings operational complexity. Therefore, developing effective observability practices is all the more important for delivering a flawless end-user experience for cloud applications.

I can tell you the day I knew I would be a Systems Administrator (the term SRE hadn’t been invented yet.) My Linux professor, a brilliant engineer at NASA, said: "The best system administrators are the laziest." He went on to qualify that statement but I had stopped listening. My fate was sealed.

With the difficulties we’re facing during this time, it can be difficult to keep up with the increasingly vast demand for our services. You need to make use of all the tools in your toolbelt in order to conserve your team’s cognitive resources. Two ways you can do this are through automating toil from your processes and prioritizing with SLOs.

Developers are building and deploying to production with greater frequency. Elite organizations are deploying to production multiple times per day. All the while we continue to distribute our applications even wider with the adoption of micro-services, and global deployments. This consistent churn and increasing code complexity create the perfect storm that makes finding problems even harder. How do you know the changes just committed actually deployed? How do you know the changes worked?

Ever experienced bad logging- whether it’s the wrong log, the wrong information, or a multitude of other logging woes? We aren’t able to count the number of times anymore that we’ve happily gone and set log lines, only to find out that it was all for naught. The frustrations are endless. What is meant to be magic for your code, the ultimate savior when debugging, has become the ultimate frustration.

The HAProxy Process Manager allows you to start external programs that are managed under HAProxy. Not everything is compiled directly into HAProxy’s C code. Some components are written using other programming languages and run alongside the load balancer.

Out-of-memory (OOM) errors take place when the Linux kernel can’t provide enough memory to run all of its user-space processes, causing at least one process to exit without warning. Without a comprehensive monitoring solution, OOM errors can be tricky to diagnose. In this post, you will learn how to use Datadog to diagnose OOM errors on Linux systems.