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🚀 Deployment Checklist - IntelliWeather API v3.0.0

Use this checklist to deploy Phase 3 features to production.

Pre-Deployment

Code Review

  • All Phase 3 files reviewed (routes/forecast.py, modules/weather_insights.py, routes/insights.py)
  • Modified files checked (app.py, config.py, routes/geocode.py)
  • No syntax errors (get_errors passed)
  • Import validation passed
  • Route registration verified

Testing

  • Manual testing of all 13 new endpoints
  • Test nowcast endpoint with valid coordinates
  • Test hourly forecast (24h, 48h, 168h)
  • Test daily forecast (7d, 16d)
  • Test complete forecast package
  • Test all insights endpoints
  • Test autocomplete with partial queries
  • Test popular locations endpoint
  • Test nearby cities endpoint
  • Verify caching works (hit/miss metrics)
  • Test hybrid mode with WeatherAPI fallback
  • Test error handling (invalid coordinates, API failures)

Documentation

  • PHASE3_README.md reviewed
  • QUICKSTART.md tested
  • CHANGELOG.md updated
  • API documentation accessible at /docs
  • All endpoints documented in OpenAPI spec

Environment Configuration

Required Environment Variables

# Core Settings
APP_NAME=IntelliWeather
APP_VERSION=3.0.0
DEBUG=false

# External APIs
WEATHERAPI_KEY=<your_key_here>  # Required for hybrid mode
ENABLE_FALLBACK=true

# Caching
CACHE_TTL_SECONDS=3600
CACHE_MAX_SIZE=1000
GEOCODE_CACHE_TTL_SECONDS=86400

# Rate Limiting
RATE_LIMIT_PER_MIN=60

# Popular Locations (customize)
POPULAR_LOCATIONS=40.7128,-74.0060,New York;51.5074,-0.1278,London;35.6762,139.6503,Tokyo

Optional Configuration

# Redis (for production caching)
REDIS_URL=redis://localhost:6379/0
SESSION_BACKEND=redis

# PostgreSQL (for production storage)
DB_CONNECTION_STRING=postgresql://user:pass@localhost/intelliweather

# Sentry (error tracking)
SENTRY_DSN=https://your-sentry-dsn

# CORS (adjust for your domain)
CORS_ORIGINS=["https://yourdomain.com"]

# Phase 3 Feature Flags (all enabled by default)
FEATURE_NOWCAST=true
FEATURE_EXTENDED_HOURLY=true
FEATURE_EXTENDED_DAILY=true
FEATURE_HYBRID_FORECAST=true
FEATURE_HEAT_INDEX=true
FEATURE_WIND_CHILL=true
FEATURE_FIRE_RISK=true
FEATURE_UV_EXPOSURE=true
FEATURE_TRAVEL_DISRUPTION=true
FEATURE_COMFORT_INDEX=true
FEATURE_AUTOCOMPLETE=true
FEATURE_NEARBY_CITIES=true

Deployment Methods

Method 1: Docker Compose (Recommended)

Checklist:

  • Docker and Docker Compose installed
  • docker-compose.yml configured
  • .env file created with production values
  • Build image: docker-compose build
  • Start services: docker-compose up -d
  • Check logs: docker-compose logs -f app
  • Verify health: curl http://localhost:8000/health
  • Test API: curl http://localhost:8000/docs

Commands:

# Build and start
docker-compose up -d

# View logs
docker-compose logs -f app

# Stop services
docker-compose down

# Restart after changes
docker-compose restart app

Method 2: Local Development

Checklist:

  • Python 3.8+ installed
  • Virtual environment created
  • Dependencies installed: pip install -r requirements.txt
  • .env file configured
  • Start server: python -m uvicorn app:app --host 0.0.0.0 --port 8000
  • Verify health: curl http://localhost:8000/health

Commands:

# Create virtual environment
python -m venv venv
source venv/bin/activate  # or venv\Scripts\activate on Windows

# Install dependencies
pip install -r requirements.txt

# Start development server
python -m uvicorn app:app --reload --host 0.0.0.0 --port 8000

# Or with gunicorn (production)
gunicorn app:app -w 4 -k uvicorn.workers.UvicornWorker -b 0.0.0.0:8000

Method 3: Kubernetes

Checklist:

  • Kubernetes cluster ready
  • Container image built and pushed
  • ConfigMap created for environment variables
  • Secret created for sensitive values (API keys)
  • Deployment manifest created
  • Service manifest created
  • Ingress configured (optional)
  • Apply manifests: kubectl apply -f k8s/
  • Verify pods: kubectl get pods
  • Check logs: kubectl logs -f deployment/intelliweather

Example Deployment:

# k8s/deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: intelliweather
spec:
  replicas: 3
  selector:
    matchLabels:
      app: intelliweather
  template:
    metadata:
      labels:
        app: intelliweather
    spec:
      containers:
      - name: api
        image: intelliweather:3.0.0
        ports:
        - containerPort: 8000
        env:
        - name: APP_VERSION
          value: "3.0.0"
        envFrom:
        - configMapRef:
            name: intelliweather-config
        - secretRef:
            name: intelliweather-secrets

Post-Deployment Verification

Health Checks

# Basic health
curl http://your-domain.com/health

# Metrics
curl http://your-domain.com/metrics

# API docs
curl http://your-domain.com/docs

Endpoint Testing

1. Nowcast

curl "http://your-domain.com/api/v3/forecast/nowcast?latitude=40.7128&longitude=-74.0060" | jq .

Expected: JSON with 15-minute intervals

2. Hourly Forecast

curl "http://your-domain.com/api/v3/forecast/hourly?latitude=40.7128&longitude=-74.0060&hours=48" | jq .

Expected: JSON with 48 hourly data points

3. Daily Forecast

curl "http://your-domain.com/api/v3/forecast/daily?latitude=40.7128&longitude=-74.0060&days=16" | jq .

Expected: JSON with 16 daily forecasts

4. Weather Insights

curl "http://your-domain.com/api/v3/insights/current?latitude=40.7128&longitude=-74.0060" | jq .

Expected: JSON with calculated insights

5. Fire Risk

curl "http://your-domain.com/api/v3/insights/fire-risk?latitude=34.0522&longitude=-118.2437&days_since_rain=7" | jq .

Expected: JSON with fire risk score and category

6. Autocomplete

curl "http://your-domain.com/geocode/autocomplete?q=New%20York" | jq .

Expected: JSON with location suggestions

Performance Testing

Response Times

# Test nowcast speed
time curl -s "http://your-domain.com/api/v3/forecast/nowcast?latitude=40.7128&longitude=-74.0060" > /dev/null

# Test caching (2nd request should be faster)
time curl -s "http://your-domain.com/api/v3/forecast/nowcast?latitude=40.7128&longitude=-74.0060" > /dev/null

Targets:

  • Nowcast: < 200ms
  • Hourly: < 500ms
  • Insights: < 100ms
  • Autocomplete: < 100ms

Cache Hit Ratio

# Check Prometheus metrics
curl http://your-domain.com/metrics | grep cache_hits_total
curl http://your-domain.com/metrics | grep cache_misses_total

Target: > 70% hit ratio

Monitoring Setup

Prometheus Metrics

  • Prometheus scraping configured
  • Metrics endpoint accessible: /metrics
  • Custom metrics visible:
    • api_requests_total
    • cache_hits_total
    • cache_misses_total
    • api_request_duration_seconds

Sentry Error Tracking

  • Sentry DSN configured
  • Test error reporting
  • Alerts configured

Logging

  • Log aggregation configured (if applicable)
  • JSON logging enabled
  • Log levels appropriate for environment

Security Checklist

API Security

  • Rate limiting enabled
  • CORS configured for production domains
  • HTTPS/TLS enabled (production)
  • API keys hashed (if using API key auth)
  • Session cookies secured (HTTPONLY, SECURE in production)

Secret Management

  • No secrets in code
  • Environment variables used for sensitive data
  • .env file in .gitignore
  • Production secrets in secure vault (AWS Secrets Manager, etc.)

Network Security

  • Firewall rules configured
  • Only necessary ports exposed
  • DDoS protection enabled (if applicable)

Rollback Plan

If Issues Occur:

Option 1: Feature Flags

# Disable Phase 3 features via environment
FEATURE_NOWCAST=false
FEATURE_EXTENDED_HOURLY=false
FEATURE_EXTENDED_DAILY=false
# ... etc

# Restart service
docker-compose restart app

Option 2: Git Rollback

# Revert to previous version
git revert <commit-hash>
git push origin main

# Redeploy
docker-compose up -d --build

Option 3: Container Rollback

# Use previous image
docker pull intelliweather:2.0.0
docker-compose down
# Update docker-compose.yml to use 2.0.0
docker-compose up -d

Load Testing (Optional)

Using Apache Bench

# Test nowcast endpoint
ab -n 1000 -c 10 "http://your-domain.com/api/v3/forecast/nowcast?latitude=40.7128&longitude=-74.0060"

# Test autocomplete
ab -n 1000 -c 10 "http://your-domain.com/geocode/autocomplete?q=New"

Using Locust

# locustfile.py
from locust import HttpUser, task, between

class WeatherUser(HttpUser):
    wait_time = between(1, 3)
    
    @task(3)
    def nowcast(self):
        self.client.get("/api/v3/forecast/nowcast?latitude=40.7128&longitude=-74.0060")
    
    @task(2)
    def hourly(self):
        self.client.get("/api/v3/forecast/hourly?latitude=40.7128&longitude=-74.0060&hours=48")
    
    @task(1)
    def insights(self):
        self.client.get("/api/v3/insights/current?latitude=40.7128&longitude=-74.0060")

Documentation Deployment

API Documentation

  • Swagger UI accessible: /docs
  • ReDoc accessible: /redoc
  • OpenAPI spec downloadable: /openapi.json

User Documentation

  • PHASE3_README.md published
  • QUICKSTART.md accessible
  • CHANGELOG.md updated
  • API examples provided

Communication Plan

Internal Team

  • Notify developers of new endpoints
  • Share QUICKSTART.md
  • Update internal wiki/docs

External Users (if applicable)

  • Announce new features
  • Update public API documentation
  • Send migration guide (if needed)
  • Provide example code

Success Criteria

Functional

  • ✅ All 13 new endpoints responding
  • ✅ No 500 errors under normal load
  • ✅ Caching working correctly
  • ✅ Error handling graceful

Performance

  • ✅ Response times within targets
  • ✅ Cache hit ratio > 70%
  • ✅ No memory leaks
  • ✅ CPU usage reasonable

Business

  • ✅ Feature parity with roadmap
  • ✅ Documentation complete
  • ✅ No breaking changes
  • ✅ Ready for user adoption

Final Sign-Off

  • Development Lead: Code reviewed and approved
  • QA Lead: Testing complete
  • DevOps Lead: Deployment successful
  • Product Owner: Features validated
  • Security: Security review passed

Post-Deployment Tasks

Week 1

  • Monitor error rates
  • Check performance metrics
  • Review user feedback
  • Fix any critical bugs

Week 2-4

  • Analyze usage patterns
  • Optimize slow endpoints
  • Plan LEVEL 2 features
  • Write unit tests

Month 2

  • Performance tuning
  • Documentation improvements
  • Feature enhancements
  • Begin LEVEL 2 implementation

Deployment Date: _______________
Deployed By: _______________
Version: 3.0.0
Status: ⬜ Planning ⬜ In Progress ⬜ Complete

🎉 Good luck with your deployment!