Operations | Monitoring | ITSM | DevOps | Cloud

As a cloud project owner, you want your environment to run smoothly and efficiently. At Google Cloud, one of the ways we help you do that is through a family of tools we call Recommenders, which leverage analytics and machine learning to automatically detect issues and present you with optimizations that you can act on.

When we graduated Loki into a GA release last year, there were more than 137 contributors who already made more than 1,000 contributions to the project. We also added hosted Loki to the lineup of Grafana Cloud offerings after it proved to be stable internally for our ops cluster, storing 40TB and half a trillion log lines each month. There was, however, one persistent problem that kept surfacing, especially for developers who were writing applications in Go: The regex package was slow.

Last week, the first OpenObservability conference took place. This event had amazing content contributions from open source project leaders, users, and influencers. We’ve seen massive growth and adoption in the open source observability space from the inspiring work being done across tracing, logging, and especially metrics. The new data stores and capabilities are growing at breakneck speed. There are more choices— yet more complexity—than ever before.

At STRABAG, we are using Elastic Cloud Enterprise (ECE) for two main use cases within our on-premises web applications. One to power different kinds of search and a second for operations where we ship more than 25,000 log entries per minute to Elastic from our load balancers. The ECE platform runs in an air-gapped environment, and we would still like to be able to use our corporate logins for the ECE platform.

Logs are an important part of troubleshooting and it’s critical to have them when you need them. When it comes to logging, Google Kubernetes Engine (GKE) is integrated with Google Cloud’s Logging service. But perhaps you’ve never investigated your GKE logs, or Cloud Logging? Here’s an overview of how logging works in GKE, and how to configure, find, and interact effectively with the GKE logs stored in Cloud Logging.

This post is the second in our series on system metrics where we cover: In the previous post, we went through some built-in tools and methods for identifying key metrics and values on your systems. In this post, we'll provide a tutorial on how to use Metricbeat to consolidate metrics, store and analyze them in the long term, and discuss some of the benefits of a centralized metric store.