Performance is one of those things that most people will agree is super important, but nevertheless tends to get overlooked. In light of that, I wanted to share the approach we recently took with auditing the Grafana product’s performance, and how we went about trying to improve it.
For many years I have been using an application called OSSEC for monitoring my home network. The output of the application is primarily email alerts which are perfect for seeing events in near real-time. In this post, I’ll be showing you how to build a good high-level view of these alerts over time with Loki, Prometheus, and Grafana.
As we’ve rolled out Loki internally at Grafana Labs, we wanted logs beyond just simple applications. Specifically while debugging outages due to config, Kubernetes, or node restarts, we’ve found Kubernetes events to be super useful. The Kubernetes events feature allows you to see all of the changes in a cluster, and you can get a simple overview by just retrieving them: This also captures when nodes go unresponsive and when a pod has been killed along with the reason.
Launched at KubeCon North America last December, Loki is a Prometheus-inspired service that optimizes storage, search, and aggregation while making logs easy to explore natively in Grafana. Loki is designed to work easily both as microservices and as monoliths, and correlates logs and metrics to save users money. Less than a year later, Loki has almost 6,500 stars on GitHub and is now quickly approaching GA.
Launched at KubeCon North America last December, Loki is a Prometheus-inspired service that optimizes storage, search, and aggregation while making logs easy to explore natively in Grafana. Loki is designed to work easily both as microservices and as monoliths, and correlates logs and metrics to save users money.
Launched at KubeCon North America last December, Loki is a Prometheus-inspired service that optimizes storage, search, and aggregation while making logs easy to explore natively in Grafana. Loki is designed to work easily both as microservices and as monoliths, and correlates logs and metrics to save users money. Less than a year later, Loki has almost 6,500 stars on GitHub and is now quickly approaching GA.
With the release of Grafana v6.3, we are introducing a significant improvement to Loki’s log exploration workflow in Grafana Explore. Launched at KubeCon North America last December, Loki is a Prometheus-inspired service that optimizes storage, search, and aggregation while making logs easy to explore natively in Grafana. Loki is designed to work easily both as microservices and as monoliths, and correlates logs and metrics to save users money.
Launched at KubeCon North America last December, Loki is a Prometheus-inspired service that optimizes storage, search, and aggregation while making logs easy to explore natively in Grafana. Loki is designed to work easily both as microservices and as monoliths, and correlates logs and metrics to save users money. Less than a year later, Loki has almost 6,500 stars on GitHub and is now quickly approaching GA.
Friday, August 2, marked the second beta release for Loki, a long overdue version 0.2.0. Why did it take so long? In large part this was my fault. Having done some work to create a release process for version 0.1.0, I found myself focusing on other things, so improving that process ended up on the backburner. This entire time, in the back of my mind, I was delaying a new release until I could improve that process.
Cortex, the open source, horizontally-scalable, highly-available, clustered Prometheus implementation that powers Grafana Cloud’s Hosted Prometheus cut its first release yesterday! This release was led by Chris Marchbanks from Splunk, who put together the whole release process and shepherded this first release.
