Operations | Monitoring | ITSM | DevOps | Cloud

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.

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.)

It’s no secret that AWS Lambda adoption has grown steadily since AWS first released it in 2015—and for good reason. The benefits of adopting Lambda are many: leveraging Lambda eliminates the need to provision and manage servers, enabling teams to just focus on their code without the mental and operational overhead of worrying about the underlying infrastructure.

Large-scale cloud applications are usually built using interconnected services that can be rather hard to troubleshoot. When a service is scaled, simple logging doesn’t cut it anymore and a more in-depth view into system’s flow is required. That’s where distributed tracing comes into play; it allows developers and SREs to get a detailed view of a request as it travels through the system of services.