Google Cloud Observability
1. Google Cloud Observability
If you've ever worked with on-premises environments, you know that you can physically touch the servers. If an application becomes unresponsive, someone can physically determine the cause. In the cloud though, the servers aren't yours—they belong to the cloud provider—and you can’t physically inspect them. So the question becomes: how can you know what's happening with your applications? The answer is: by using Google’s integrated observability tools. Observability involves collecting, analyzing, and visualizing data from various sources within a system to gain insights into its performance, health, and behavior. To achieve this, Google Cloud offers observability tools for monitoring, logging, and diagnostics. It’s a unified platform for managing and gaining insights into the performance, availability, and health of applications deployed on Google Cloud. Let's explore some of the managed services that constitute Google Cloud Observability. The first is Cloud Logging, which collects and stores all application and infrastructure logs. With real-time insights, Cloud Logging can help to troubleshoot issues, identify trends, and comply with regulations. Cloud Monitoring provides a comprehensive view of your cloud infrastructure and applications. It collects metrics from your applications and infrastructure, and provides insights into their performance, health, and availability. It can also be used to create alerting policies to notify you when metrics, health check results, and uptime check results meet specified criteria. Graphs produced in Cloud Monitoring provide visual indication for when events have occurred, such as performance bottlenecks or service dependencies. Cloud Monitoring can also be integrated with many third-party products. Google Cloud Managed Service for Prometheus is Google Cloud's fully managed, multi-cloud, cross-project solution for Prometheus metrics. It lets you globally monitor and alert on your workloads, using Prometheus, without having to manually manage and operate Prometheus at scale. Cloud Trace helps identify performance bottlenecks in applications. It collects latency data from applications, and provides insights into how they’re performing. Cloud Profiler identifies how much CPU power, memory, and other resources an application uses. It continuously gathers CPU usage and memory-allocation information from production applications and provides insights into how applications are using resources. And finally, there’s Error Reporting, which counts, analyzes, and aggregates the crashes in running cloud services in real-time. A centralized error management interface displays the results with sorting and filtering capabilities. A dedicated view shows the error details, which include a time chart, occurrences, affected user count, first and last-seen dates, and a cleaned exception stack trace. Error Reporting supports email and mobile alerts through its API. Google's integrated observability tools offer valuable insights into the performance and health of applications and infrastructure in the cloud. Let’s refer back to two important terms that were mentioned when introducing Cloud Monitoring, events and metrics, and take a moment to differentiate between the two. A metric represents a value that can be monitored, such as CPU or disk usage. They might be “gauge values,” which fluctuate up or down over time, or “counters,” which are values that increase over time. Monitoring metrics can help identify bottlenecks, as they’re specific to a resource. And then there are events, which represent occurrences that happen to a cluster, Pod, or container. Examples include the restart of a Pod, node, or service; when the number of deployments in a cluster is scaled up or down; or when an application responds to a request. Events typically report successes, warnings, or failures, while metrics report numerical values.2. Let's practice!
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