06.07.Monitoring Analysis

06.07. Monitoring Analysis

1. Executive Summary

CPT is a distributed application running on Google Cloud Run. Current observability is limited to default Cloud Logging. For a "world-class" application, we need deep visibility into request traces, proactive alerting for external API failures (Tesla/Stripe), and centralized log management.

2. Current Status

Logging: ✅ JSON-structured logging via CloudJsonFormatter in app/core/logging_config.py. Outputs to stdout with Cloud Logging severity mapping. Context fields (session_id, payment_id, vin_suffix, request_id) propagated via ContextVar.
Tracing: ✅ OpenTelemetry instrumentation via app/core/tracing.py. Auto-instruments FastAPI, httpx (Tesla/Stripe/PayPal), and Redis. Exports to Google Cloud Trace via OTLP in deployed envs, console in local dev.
Alerting: ✅ Terraform step 140-gcp-cloud-monitor provisions log-based metrics and alert policies for Tesla API errors, payment failures, PDF generation errors, and 5xx server errors. Email notifications via Cloud Monitoring.
Metrics: ✅ Default Cloud Run metrics (Request count, Latency, CPU/Memory) + custom log-based metrics for Tesla API errors, payment errors, PDF errors, and 5xx errors.

3. Gap Analysis

~~The "PDF Black Box"~~: ✅ Resolved — OpenTelemetry distributed tracing now spans from API request through PDF generation to GCS write. PDF error alert policy fires when generation failures exceed threshold.
~~Tesla API Blindness~~: ✅ Resolved — Log-based metric tesla-api-errors tracks all Tesla API failures. Alert policy fires on >10 errors in 5 minutes. Health dashboard visualizes error rate.
~~Log Noise~~: ✅ Resolved — JSON structured logging implemented with context field filtering.

4. Recommendations

✅ Structured Logging: JSON logging implemented (CloudJsonFormatter, request context middleware, session context in TeslaFleetService).
✅ Distributed Tracing: OpenTelemetry implemented in app/core/tracing.py. Auto-instruments FastAPI, httpx, and Redis. Exports to Cloud Trace.
✅ SLA Monitoring: Health dashboard created via Terraform step 140-gcp-cloud-monitor with Tesla API error rate, payment error rate, API latency p95/p99, and resource utilization panels.
✅ Proactive Alerting: Cloud Monitoring alert policies provisioned via Terraform for Tesla API errors, payment failures, PDF generation errors, and 5xx server errors. Email notifications configured.

5. Implementation Progress

[x] Phase 1: Structured JSON logging (app/core/logging_config.py, request context middleware, session context propagation)
[x] Phase 2: Distributed tracing — app/core/tracing.py with OpenTelemetry SDK, Cloud Trace exporter, auto-instrumentation for FastAPI/httpx/Redis
[x] Phase 3: Proactive alerting — Terraform step 140-gcp-cloud-monitor with log-based metrics + alert policies (Tesla, payment, PDF, 5xx)
[x] Phase 4: Health dashboard — Terraform step 140-gcp-cloud-monitor with 10-panel mosaic dashboard (throughput, latency, errors, resources)