Professional Cloud DevOps Engineer

Collecting and Importing Logs

Logs record what happens in your applications and infrastructure, and getting them into a central place is the first step in understanding system behavior. Ops Agent gathers logs from Compute Engine VMs and sends them to Cloud Logging, acting as a bridge between individual machines and the centralized logging system. OpenTelemetry provides a standard way to collect traces, metrics, and logs from applications, so teams can instrument code once and send data to multiple backends without rewriting the collection logic. Cloud Audit Logs automatically records who did what, when, and where across Google Cloud services, tracking administrative actions separately from regular data access. VPC Flow Logs captures network traffic moving in and out of your Virtual Private Cloud, showing which instances communicate with each other and with the outside world. Cloud Service Mesh collects telemetry from service-to-service communication, giving visibility into how microservices interact without requiring changes to application code.

Optimizing Logs

Not all logs are equally important, and sending everything everywhere quickly becomes expensive and overwhelming. Filtering lets you keep only the log entries that matter for a particular purpose, such as errors or requests matching a certain pattern. Sampling reduces volume by recording only a percentage of similar events, which works well for high-volume debug logs where you need to see patterns rather than every single entry. Exclusions prevent unwanted logs from being ingested at all, saving storage costs and reducing noise in your analysis tools. When deciding where to collect logs from, consider which sources contain the most useful information for troubleshooting and compliance, because collecting from everything equally often wastes resources on data that never gets used.

Collecting Metrics

Metrics give you numbers you can graph, alert on, and track over time, making them essential for understanding system health. Applications can emit custom metrics from within their code, while platforms like Google Cloud Managed Service for Prometheus scrapes metrics from workloads running on GKE or elsewhere. Platform-level metrics come from the underlying infrastructure, such as CPU usage, memory consumption, and disk I/O on Compute Engine instances. Networking metrics track how data moves through your VPC, load balancers, and Cloud CDN, showing latency, throughput, and error rates. In hybrid or multi-cloud environments, you need agents or collectors that can pull metrics from on-premises systems and other cloud providers so you have a single dashboard for everything. Cloud Service Mesh also provides metrics about service communication, showing request rates, latencies, and failure percentages between microservices.

Creating Synthetic Monitors

Real user traffic doesn't always reveal problems that are about to happen, so synthetic monitors let you test your systems proactively. A synthetic monitor regularly sends test requests to your application endpoints from Google Cloud's worldwide locations, simulating what a real user would experience. These tests can verify that specific workflows complete successfully, such as logging in and completing a purchase, rather than just checking if a single page loads. When a synthetic monitor detects a failure or slowdown, it alerts you before your users notice, giving teams time to fix issues during maintenance windows rather than during outages. You can create monitors for HTTP endpoints, HTTPS URLs, TCP ports, and even gRPC services, choosing the protocol that matches what your application exposes.

Creating Custom Metrics

Standard metrics cover the basics, but custom metrics let you track what matters specifically to your application and business. Log-based metrics transform log entries into metrics by counting how often certain log messages appear, such as counting error messages per minute to create an error rate metric. Custom metrics can track business-level data like the number of orders processed, user sign-ups completed, or queue depths in message processing systems. Once created, custom metrics behave like any other metric: you can graph them, set alerts on them, and use them in dashboards. The key advantage is that you define what success looks like for your specific system, rather than relying only on generic infrastructure measurements.