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.
