Troubleshooting production issues with virtual machines (VMs) can be complex and often requires correlating multiple data points and signals across infrastructure and application metrics, as well as raw logs. When your end users are experiencing latency, downtime, or errors, switching between different tools and UIs to perform a root cause analysis can slow your developers down.
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.
When you grow your peak concurrent users by 5x nearly overnight, ensuring that your operations can successfully support that growth can be a make or break for your success. Rocket League is a popular online multiplayer game created by Psyonix described as arcade-style soccer and vehicular mayhem. In the summer of 2020, the game maker decided to switch the business model of the game from an upfront purchase to a free to play model.
The observability of metrics is a key factor for a successful operations team, allowing for increasingly effective visualizations, analysis, and troubleshooting. Google Cloud works with third-party partners, such as Grafana Labs, to make it easy for customers to create their desired observability stack leveraging a combination of different tools. More than two years ago, we collaborated with Grafana Labs to introduce the Cloud Monitoring plugin for Grafana.
Customers need scale and flexibility from their cloud and this extends into supporting services such as monitoring and logging. Google Cloud’s Monitoring and Logging observability services are built on the same platforms used by all of Google that handle over 16 million metrics queries per second, 2.5 exabytes of logs per month, and over 14 quadrillion metric points on disk, as of 2020.
