GCP Observability Best Practices

Structured Logging

JSON Log Format

Use structured JSON logging for better queryability:

json
{
  "severity": "ERROR",
  "message": "Payment failed",
  "httpRequest": { "requestMethod": "POST", "requestUrl": "/api/payment" },
  "labels": { "user_id": "123", "transaction_id": "abc" },
  "timestamp": "2025-01-15T10:30:00Z"
}

Severity Levels

Use appropriate severity for filtering:

• DEBUG: Detailed diagnostic info
• INFO: Normal operations, milestones
• NOTICE: Normal but significant events
• WARNING: Potential issues, degraded performance
• ERROR: Failures that don't stop the service
• CRITICAL: Failures requiring immediate action
• ALERT: Person must take action immediately
• EMERGENCY: System is unusable

Log Filtering Queries

Common Filters

# By severity
severity >= WARNING

# By resource
resource.type="cloud_run_revision"
resource.labels.service_name="my-service"

# By time
timestamp >= "2025-01-15T00:00:00Z"

# By text content
textPayload =~ "error.*timeout"

# By JSON field
jsonPayload.user_id = "123"

# Combined
severity >= ERROR AND resource.labels.service_name="api"

Advanced Queries

# Regex matching
textPayload =~ "status=[45][0-9]{2}"

# Substring search
textPayload : "connection refused"

# Multiple values
severity = (ERROR OR CRITICAL)

Metrics vs Logs vs Traces

When to Use Each

Metrics: Aggregated numeric data over time

• Request counts, latency percentiles
• Resource utilization (CPU, memory)
• Business KPIs (orders/minute)

Logs: Detailed event records

• Error details and stack traces
• Audit trails
• Debugging specific requests

Traces: Request flow across services

• Latency breakdown by service
• Identifying bottlenecks
• Distributed system debugging

Alert Policy Design

Alert Best Practices

• Avoid alert fatigue: Only alert on actionable issues
• Use multi-condition alerts: Reduce noise from transient spikes
• Set appropriate windows: 5-15 min for most metrics
• Include runbook links: Help responders act quickly

Common Alert Patterns

Error rate:

• Condition: Error rate > 1% for 5 minutes
• Good for: Service health monitoring

Latency:

• Condition: P99 latency > 2s for 10 minutes
• Good for: Performance degradation detection

Resource exhaustion:

• Condition: Memory > 90% for 5 minutes
• Good for: Capacity planning triggers

Cost Optimization

Reducing Log Costs

• Exclusion filters: Drop verbose logs at ingestion
• Sampling: Log only percentage of high-volume events
• Shorter retention: Reduce default 30-day retention
• Downgrade logs: Route to cheaper storage buckets

Exclusion Filter Examples

# Exclude health checks
resource.type="cloud_run_revision" AND httpRequest.requestUrl="/health"

# Exclude debug logs in production
severity = DEBUG

Debugging Workflow

1. Start with metrics: Identify when issues started
2. Correlate with logs: Filter logs around problem time
3. Use traces: Follow specific requests across services
4. Check resource logs: Look for infrastructure issues
5. Compare baselines: Check against known-good periods