Logging in Kubernetes can help you track the health of your cluster and its applications. Logs can be used to identify and debug any issues that occur. Logging can also be used to gain insights into application and system performance. Moreover, collecting and analyzing application and cluster logs can help identify bottlenecks and optimize your deployment for better performance.
A database is one of the most critical components for almost every application. Making sure it is running with the expected read and write latencies is paramount. This can be the difference between a smooth, pleasing user experience and a slow, error-filled one that makes your customers turn their back on a product — and never come back.
Apache Cassandra is a highly scalable, open source NoSQL database system designed to handle large amounts of data across multiple commodity servers with no single point of failure. Apache Cassandra can be run as a single node but starts making sense when its run in a cluster setup. The system is optimized for high write throughput and is known for its ability to handle big data workloads with ease at super-low latencies.
As more organizations adopt cloud-based services like Microsoft Azure Kubernetes Service (AKS), it becomes increasingly important to monitor and manage the performance and reliability of these services. If you’re using AKS today, then Grafana Cloud provides the flexibility, performance, and visualizations you need to monitor your distributed applications.
Grafana Loki 2.8 is here — and it’s at least 0.1 better than Loki 2.7! Jokes aside, this release includes a number of improvements users will appreciate. In addition to graduating our TSDB index from Experimental to General Availability, we’ve added a number of nifty LogQL features, and we’ve made the Loki deployment and management experience much easier. This also marks the release of Grafana Enterprise Logs (GEL) 1.7.
We often see questions about how to template alerts. In Grafana, you can template information about your alerts with custom labels and annotations, and you can also template how notifications look and what information they contain with notification templates. Many users confuse the two, despite being separate features with different use cases.
As a developer, I’m always thinking about ways I can make my work on projects better, faster, and more organized. Over the years, I’ve learned that tracking development metrics is a really helpful way to do that.
There are many aspects of application performance, but they broadly fall into two categories: frontend performance and backend performance. As a tester, it’s important to know the differences between the two and how that impacts the way you approach your tests. In this blog, I’ll provide a high-level overview of frontend performance testing and backend performance testing, including pros and cons of each one.
