GrafanaCONline is live! We hope you’re able to catch the great sessions we have planned over the next couple of weeks. The conference kicked off yesterday with a keynote from Grafana creator Torkel Ödegaard, Grafana Labs CEO Raj Dutt, and VP of Product (and Cortex co-creator) Tom Wilkie.
In today’s post, we’re going to demystify how the number of JavaScript dependencies grows while we’re working on a relatively simple project. Should you be worried about the number of dependencies? Keep in mind that this blog post is related to the Ride Down The JavaScript Dependency Hell blog post that was released a while back. We’ll show a “real-world” example of how a project’s dependencies can grow from zero to 13K.
Today’s release of Node.js integration supports PostgreSQL as well as all the consumers of the pg library.
Nowadays, Slack is often the default choice for an online communication tool in the tech industry. Startups, enterprises, open-source projects - they all use Slack to communicate within their organization but sometimes also with their users and community. Our startup was no different. In this article, I explain what made us look for Slack alternative and what are our impressions after a month of switching to Discord, so you can learn from those before you make your decision.
This article will describe the process of installing Rancher 2.4 on a highly available K3s Kubernetes cluster in Microsoft Azure. It will also take advantage of Microsoft Azure Database for MySQL, which removes the dependency on etcd and provides us all the additional features that Azure delivers with this service. You will learn how to deploy the infrastructure to support this pattern using only the Azure Cloud Shell.
Considering the scale of log data that modern cloud environments generate, it’s oftentimes prohibitively expensive to index all of it. For monitoring and logging, cost management is just as important as in other parts of the business. Whether sudden spikes of log data overwhelm databases or good business generates more activity in your environment, teams should anticipate and mitigate the steep costs that result from high log volumes.
Since launching in 2014, AWS Lambda has dramatically grown in popularity. Lambda allows you to run code 7without having to manage the underlying compute resources. From a monitoring standpoint, you no longer have the need or ability to observe traditional performance metrics like CPU and memory. That doesn't mean you don't need to keep an eye on other things, though. So what metrics should you monitor for your Lambda functions?
