gcloud-usage

Cloud Logging queries filter logs by severity, resource, and time range using structured syntax. Use filtering expressions like `severity=ERROR AND resource.type="gce_instance"` to isolate relevant events, then export results to CSV or other formats for analysis.

Structured JSON logging with consistent field schemas improves queryability by enabling precise filtering and aggregation. Define a severity taxonomy, include resource identifiers, timestamps, and trace IDs, then ingest logs into Cloud Logging for efficient correlation across metrics and traces.

Alert policies combine multiple conditions and thresholds to detect production issues early. Define multi-condition patterns tied to metrics or log-based measurements, attach runbooks with remediation steps, and route notifications to incident response teams for faster resolution.

Metrics aggregate system behavior over time and detect anomalies; logs provide detailed event context; traces show request flow across services. Start with metrics to identify the issue, correlate with logs for details, then use traces to understand service dependencies and latency.

Cost optimization involves excluding verbose or low-value logs, applying sampling strategies to high-volume services, and using log exclusion filters in Cloud Logging. Retain ERROR and critical logs at full fidelity while sampling DEBUG and INFO levels based on traffic patterns.