When you’re troubleshooting an application on Google Kubernetes Engine (GKE), the more context that you have on the issue, the faster you can resolve it. For example, did the pod exceed it’s memory allocation? Was there a permissions error reserving the storage volume? Did a rogue regex in the app pin the CPU? All of these questions require developers and operators to build a lot of troubleshooting context.
Logs are an essential part of troubleshooting applications and services. However, ensuring your developers, DevOps, ITOps, and SRE teams have access to the logs they need, while accounting for operational tasks such as scaling up, access control, updates, and keeping your data compliant, can be challenging. To help you offload these operational tasks associated with running your own logging stack, we offer Cloud Logging.
Visualizing trends in your logs is critical when troubleshooting an issue with your application. Using the histogram in Logs Explorer, you can quickly visualize log volumes over time to help spot anomalies, detect when errors started and see a breakdown of log volumes. But static visualizations are not as helpful as having more options for customization during your investigations.
Running and troubleshooting production services requires deep visibility into your applications and infrastructure. While basic logs and metrics are available out of the box with Google Cloud Compute Engine (GCE), capturing advanced data used to require the installation of both a metrics agent and a logging agent.
Being alerted to an issue with your application before your customers experience undue interruption is a goal of every development and operations team. While methods for identifying problems exist in many forms, including uptime checks and application tracing, alerts on logs is a prominent method for issue detection. Previously, Cloud Logging only supported alerts on error logs and log-based metrics, but that was not robust enough for most application teams.
Setting up Cloud Monitoring dashboards for your team can be time consuming because every team's needs are different. Picking the right metrics, using the right visualizations to represent these metrics, deciding what metrics can go on the same chart, and determining the right pre-processing steps for metrics requires background and experience that may not yet exist among your development and operations teams.
