Stackery can be used to create a new CloudFormation template or to quickly visualize an existing one. Code is automatically generated as you simply drag-and-drop resources on a graphical grid. The experience is much more intuitive than previous generation tools like AWS CloudFormation Designer. Stackery visualizes resources the way a human would perceive them, grouping related resources together.
As with all start-ups, especially for a cloud provider, access to funds is imperative to build and scale quickly – after all building out new data centre regions doesn't come cheap! So in recent months we quietly opened a seed round to acquire $2.8m worth of funding – giving Civo a pre-money valuation of $16,800,000. Since launching into beta nearly 2 years ago, we’ve had tons of VC companies knocking on our door, but at this stage we decided not to take VC money.
Not all the waves we make are extreme. This is our fancy way of saying that we’ve been making a lot of small, behind the scenes updates lately targeting improvements to reporting and performance. For a full rundown, check out our release updates.
During the next six weeks, our team will work to improve the overall experience of Qovery. We gathered all your feedback (thank you to our wonderful community 🙏), and we decided to make significant changes to make Qovery a better place to deploy and manage your apps. This series will reveal all the changes and features you will get in the next major release of Qovery. Let's go!
Logz.io has pressed hard to align our tracing and metrics analytics capabilities over the past year. And as our technology advances, so does our service. We are announcing Multiple Tracing Accounts with Logz.io Distributed Tracing, aligning it with our logging and metrics tools. Complementing multiple data sources for metrics and logs, Logz users can segment their data according to sources and teams for better organization.
For the first time, a software load balancer exceeds 2-million RPS on a single Arm instance. A few weeks ago, while I was working on an HAProxy issue related to thread locking contention, I found myself running some tests on a server with an 8-core, 16-thread Intel Xeon W2145 processor that we have in our lab. Although my intention wasn’t to benchmark the proxy, I observed HAProxy reach 1.03 million HTTP requests per second.
One of the big questions in monitoring can be summed up as: Who watches the watchers? If you rely on Prometheus for your monitoring, and your monitoring fails, how will you know? The answer is a concept known as metamonitoring. At Grafana Labs, a handful of geographically distributed metamonitoring Prometheus servers monitor all other Prometheus servers and each other cross-cluster, while their alerting chain is secured by a dead-man’s-switch-like mechanism.
Any production application needs to be monitored for its uptime. Let’s say you’ve developed a stock market statistics application, for example, using Spring Boot for your client. This application has to be up all the time while the stock market is open. If it’s down at a crucial time, it could mean huge losses for relevant stakeholders.
Being mobile is in large part about communication. Our app for mobile access to Alloy Navigator™ ITSM platform gives IT people the freedom to communicate with customers and teammates, notify stakeholders, submit approval requests, assign work, and escalate incidents from anywhere, anytime. All those communication tasks typically involve choosing the right person from a list.
