Operations | Monitoring | ITSM | DevOps | Cloud

You’ve pored over the MongoDB documentation, crafted highly polished and well-tuned queries, and confidently deployed your new code to production. Everything ran great at first, but once CPU or RAM usage hit a certain point, your queries suddenly slowed to a crawl. What happened, and how can you prepare for situations like this in the future? This is an unfortunate but common scenario with databases like MongoDB.

Machine Learning (and deep learning) applications are quickly gaining in popularity, but keeping the process agile by continuously improving it is getting more and more complex. There are many reasons for this, but primarily, behaviors are complex and difficult to anticipate, making them resistant to proper testing, harder to explain, and thus not easy to improve.

Alien wavelengths are commonplace today. Here a transponder pair from one optical system vendor connects to, and transmits over, the optical line system (OLS) – constituting fixed/reconfigurable multiplexer and amplification elements primarily – from another vendor. (While it can be technically feasible to pair transponders from different vendors, typically this is not done for commercial and operational reasons.)

I’m a bit of a data nerd. And like many fortunate people, I’m still working from and generally staying home and have a lot spare time on my hands. So what does that combination result in? Graphs!

The very first Civo Community Meetup is here! Episode #1 featured talks from Civo CTO Andy Jeffries and Developer Advocate Kai Hoffman, alongside #KUBE100 community members Saiyam Pathak and Alistair Hey. This meetup not only focussed on the direction of Civo and what's in store for the future for us, but also shared insights into the Civo marketplace and Terraform provider, plus k3s development using OpenFaaS with Civo k3s.

Since almost the beginning of programming, the idea of write-once and deploy everywhere, on all platforms, has been an unreachable ideal to minimize development costs for cross-platform applications, drive UI consistency and reduce security service area. In programming, the cross-platform languages Java and Python have topped developer utilization charts for decades.

Deploying and monitoring performance for an entire Kubernetes cluster can be complex. To simplify the process, we’ve added service discovery functionality to eliminate complex configuration, in addition to more advanced monitoring for viewing activity inside containers. Service discovery identifies k8s pods running on a cluster and immediately starts monitoring system performance. All containers are identified, regardless of complexity.