______________________________________________________________________

title: Infrastructure Implementation Roadmap - Week-by-Week
type: note
permalink: assessments/infrastructure-implementation-roadmap-week-by-week
tags:

- infrastructure
- implementation
- devops
- week-by-week
- detailed

______________________________________________________________________

# Infrastructure Implementation Roadmap - Week-by-Week Details

**Purpose:** Detailed breakdown of infrastructure tasks aligned with feature roadmap
**Timeline:** 6 weeks (Weeks 1-6 of feature roadmap)
**Owner:** DevOps/CI-CD Engineer + Backend Developer

______________________________________________________________________

## WEEK 1: Foundation & Validation

### Objectives

1. Build containerization foundation
1. Validate latency requirements (CRITICAL)
1. Set up performance testing infrastructure
1. Establish baseline metrics

### Detailed Tasks

#### 1.1 Build & Test Dockerfile (Day 1-2, 2-3 days)

**Owner:** DevOps
**Priority:** CRITICAL

```dockerfile
# /Users/feniix/src/personal/cursor/dropjump-claude/Dockerfile
FROM python:3.12-slim as builder

WORKDIR /build

# Copy dependency files
COPY pyproject.toml uv.lock ./

# Install uv and export requirements
RUN pip install uv && uv export --no-header > requirements.txt

# Final stage
FROM python:3.12-slim

WORKDIR /app

# Install system dependencies (minimal)
RUN apt-get update && apt-get install -y \
    libopencv-dev \
    libsm6 \
    libxext6 \
    && rm -rf /var/lib/apt/lists/*

# Copy requirements
COPY --from=builder /build/requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Copy application code
COPY src/kinemotion ./kinemotion

# Health check
HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
  CMD python -c "import socket; socket.create_connection(('localhost', 8000), timeout=5)" || exit 1

# Default: CLI tool
CMD ["python", "-m", "kinemotion"]
```

**Testing Checklist:**

- [ ] Build locally: `docker build -t kinemotion:test .`
- [ ] Verify size: `docker images | grep kinemotion` (target \<500MB)
- [ ] Run CLI: `docker run kinemotion:test dropjump-analyze --help`
- [ ] Test healthcheck: Add `CMD` override during Task 3

**Acceptance Criteria:**

- Image builds successfully
- Size \< 500MB
- All dependencies available
- CLI commands work inside container

#### 1.2 Set Up Docker Compose for Development (Day 2-3, 2-3 days)

**Owner:** DevOps
**Priority:** HIGH

```yaml
# /Users/feniix/src/personal/cursor/dropjump-claude/docker-compose.dev.yml
version: '3.9'

services:
  # Application (for testing)
  app:
    build: .
    ports:
      - "8000:8000"
    environment:
      - ENVIRONMENT=development
      - REDIS_URL=redis://redis:6379
      - DATABASE_URL=postgresql://postgres:postgres@postgres:5432/kinemotion
    depends_on:
      - postgres
      - redis
    volumes:
      - ./src:/app/src
    command: uvicorn kinemotion.realtime.server:app --host 0.0.0.0 --port 8000 --reload

  # PostgreSQL (for Task 5: storing analysis history)
  postgres:
    image: postgres:15-alpine
    ports:
      - "5432:5432"
    environment:
      - POSTGRES_USER=postgres
      - POSTGRES_PASSWORD=postgres
      - POSTGRES_DB=kinemotion
    volumes:
      - postgres_data:/var/lib/postgresql/data

  # Redis (for caching metrics, cross-instance communication)
  redis:
    image: redis:7-alpine
    ports:
      - "6379:6379"

  # Prometheus (monitoring)
  prometheus:
    image: prom/prometheus:latest
    ports:
      - "9090:9090"
    volumes:
      - ./prometheus.yml:/etc/prometheus/prometheus.yml
      - prometheus_data:/prometheus
    command:
      - '--config.file=/etc/prometheus/prometheus.yml'

  # Grafana (dashboards)
  grafana:
    image: grafana/grafana:latest
    ports:
      - "3000:3000"
    environment:
      - GF_SECURITY_ADMIN_PASSWORD=admin
    volumes:
      - grafana_data:/var/lib/grafana
    depends_on:
      - prometheus

volumes:
  postgres_data:
  prometheus_data:
  grafana_data:
```

**Setup Instructions:**

```bash
cd /Users/feniix/src/personal/cursor/dropjump-claude
docker-compose -f docker-compose.dev.yml up -d

# Verify services running
docker-compose -f docker-compose.dev.yml ps

# Access points:
# App: http://localhost:8000
# Prometheus: http://localhost:9090
# Grafana: http://localhost:3000 (admin/admin)
```

**Acceptance Criteria:**

- [ ] All 5 services start successfully
- [ ] Logs show no errors
- [ ] App responds to health check
- [ ] Postgres accessible
- [ ] Redis accessible

#### 1.3 Integrate Load Testing into CI/CD (Day 2-3, 3-4 days)

**Owner:** DevOps + QA
**Priority:** CRITICAL

```bash
# Install locust
pip install locust
```

```python
# /Users/feniix/src/personal/cursor/dropjump-claude/tests/load_test_websocket.py
from locust import HttpUser, WebSocketUser, task, between, events
import time
import json
from base64 import b64encode
import cv2
import numpy as np

class RealtimeAnalysisUser(WebSocketUser):
    wait_time = between(0.5, 2)

    def on_start(self):
        """Load test video frame once"""
        self.test_frame = self._generate_test_frame()

    def _generate_test_frame(self):
        """Generate synthetic video frame"""
        frame = np.zeros((480, 640, 3), dtype=np.uint8)
        # Add some test data
        cv2.rectangle(frame, (100, 100), (500, 400), (255, 0, 0), 2)
        _, buffer = cv2.imencode('.jpg', frame)
        return b64encode(buffer).decode()

    @task
    def analyze_video_stream(self):
        """Send frames to real-time analysis endpoint"""
        with self.client.websocket_connect("/ws/analyze?sport=cmj") as websocket:
            # Send 10 frames
            for i in range(10):
                try:
                    websocket.send_json({
                        "frame": self.test_frame,
                        "frame_index": i,
                        "quality": "balanced"
                    })

                    # Receive metrics
                    response = websocket.receive_json(timeout=5)

                    if "error" in response:
                        self.user.events.request_type = "websocket"
                        self.user.events.name = "/ws/analyze"
                        self.user.events.exception = response["error"]

                    # Track latency
                    time.sleep(0.033)  # 30fps

                except Exception as e:
                    print(f"WebSocket error: {e}")

# Run command:
# locust -f tests/load_test_websocket.py --host=http://localhost:8000 -u 25 -r 5 --run-time 2m
```

```yaml
# /Users/feniix/src/personal/cursor/dropjump-claude/.github/workflows/load-test.yml
name: Load Testing

on:
  pull_request:
    paths:
      - 'src/kinemotion/**'
      - 'tests/**'
      - '.github/workflows/load-test.yml'

jobs:
  load-test:
    runs-on: ubuntu-latest

    services:
      app:
        image: kinemotion:latest
        ports:
          - 8000:8000
        options: >-
          --health-cmd "python -c 'import socket; socket.create_connection((\"localhost\", 8000), timeout=5)'"
          --health-interval 10s
          --health-timeout 5s

    steps:
      - uses: actions/checkout@v4

      - uses: actions/setup-python@v4
        with:
          python-version: '3.12'

      - name: Install dependencies
        run: |
          pip install uv
          uv sync
          pip install locust

      - name: Run load test (25 concurrent, 2 minutes)
        run: |
          locust -f tests/load_test_websocket.py \
            --headless \
            -u 25 \
            -r 5 \
            --run-time 2m \
            --csv results \
            --host http://localhost:8000

      - name: Check SLO (p95 < 200ms)
        run: |
          python scripts/check_load_test_slo.py results
```

```python
# /Users/feniix/src/personal/cursor/dropjump-claude/scripts/check_load_test_slo.py
import csv
import sys

def check_slos(csv_file):
    """Validate load test SLOs"""
    with open(f"{csv_file}_stats.csv") as f:
        reader = csv.DictReader(f)
        rows = list(reader)

    stats = rows[-1]  # Last row has aggregated stats

    # Check p95 latency
    p95_latency = float(stats.get('95%', 0))
    if p95_latency > 200:
        print(f"FAIL: p95 latency {p95_latency}ms > 200ms")
        return False

    # Check error rate
    failure_rate = float(stats.get('Failure rate', '0').strip('%')) / 100
    if failure_rate > 0.01:  # 1%
        print(f"FAIL: Error rate {failure_rate*100}% > 1%")
        return False

    print(f"PASS: p95={p95_latency}ms, errors={failure_rate*100}%")
    return True

if __name__ == "__main__":
    if not check_slos(sys.argv[1]):
        sys.exit(1)
```

**Acceptance Criteria:**

- [ ] Load test runs successfully
- [ ] Results show p50, p95, p99 latencies
- [ ] Error rate tracked
- [ ] GitHub Actions workflow executes on PR
- [ ] Fails if SLO violated

#### 1.4 Performance Profiling Setup (Day 2-3, 3-4 days)

**Owner:** DevOps + Backend
**Priority:** HIGH

```bash
# Add profiling tools to pyproject.toml
pip install py-spy pytest-benchmark memory-profiler
```

```python
# /Users/feniix/src/personal/cursor/dropjump-claude/tests/test_performance_profile.py
import pytest
import time
import numpy as np
from kinemotion.core.pose import estimate_pose_from_frame
from kinemotion.cmj.kinematics import calculate_metrics

@pytest.mark.performance
@pytest.mark.benchmark
def test_mediapipe_latency(benchmark):
    """Benchmark MediaPipe pose detection latency

    Target: < 40ms for pose detection alone
    """
    # Load test frame
    frame = np.zeros((480, 640, 3), dtype=np.uint8)

    def run():
        return estimate_pose_from_frame(frame)

    result = benchmark(run)

    # Assert
    assert result is not None
    assert benchmark.stats.mean < 40, f"Pose detection too slow: {benchmark.stats.mean}ms"

@pytest.mark.performance
def test_kinematics_latency():
    """Assert kinematics calculation < 10ms"""
    # Load sample pose landmarks
    landmarks = np.random.rand(33, 3)  # 33 body points

    start = time.perf_counter()
    for _ in range(100):
        metrics = calculate_metrics(landmarks)
    latency_ms = (time.perf_counter() - start) / 100 * 1000

    assert latency_ms < 10, f"Kinematics too slow: {latency_ms}ms"

# Run: pytest tests/test_performance_profile.py -v
```

```yaml
# Add to .github/workflows/test.yml
- name: Run performance benchmarks
  run: |
    uv run pytest tests/test_performance_profile.py \
      --benchmark-only \
      --benchmark-json=benchmark_results.json

    # Compare to baseline (create baseline on main branch)
    uv run pytest-benchmark compare benchmark_results.json || true
```

**Acceptance Criteria:**

- [ ] Benchmarking suite runs
- [ ] Baseline latencies established (pose \<40ms, kinematics \<10ms)
- [ ] Results stored for regression detection

### Week 1 Deliverables

- [ ] Dockerfile built, tested, \<500MB
- [ ] Docker Compose working locally (all 5 services)
- [ ] Load testing framework integrated (Locust)
- [ ] Performance profiling baseline established
- [ ] GitHub Actions workflows created:
  - `.github/workflows/load-test.yml`
  - Updated `.github/workflows/test.yml` with benchmarking

### Week 1 Success Criteria

✓ Dockerfile builds successfully
✓ Docker Compose stack runs locally
✓ Load test runs 25 concurrent users
✓ Latency baseline: establish p50, p95, p99 (even if real-time not implemented)
✓ Performance profiling shows bottlenecks
✓ All CI workflows execute on PR

**Decision Point:** Latency achievable \<200ms? (Continue to Week 2, or pivot architecture)

______________________________________________________________________

## WEEK 2: Deployment Infrastructure & Monitoring

### Objectives

1. Build deployment pipeline
1. Set up monitoring (Prometheus + Grafana)
1. Provision staging environment
1. Create canary deployment strategy

### Detailed Tasks

#### 2.1 GitHub Actions Docker Build & Push (Day 1-2, 2-3 days)

**Owner:** DevOps
**Priority:** CRITICAL

```yaml
# /Users/feniix/src/personal/cursor/dropjump-claude/.github/workflows/build-docker.yml
name: Build Docker Image

on:
  push:
    branches: [main]
  pull_request:
    paths:
      - 'src/**'
      - 'Dockerfile'
      - '.github/workflows/build-docker.yml'

jobs:
  build:
    runs-on: ubuntu-latest

    permissions:
      contents: read
      packages: write

    steps:
      - uses: actions/checkout@v4

      - name: Set up Docker Buildx
        uses: docker/setup-buildx-action@v2

      - name: Login to Docker Hub
        uses: docker/login-action@v2
        with:
          username: ${{ secrets.DOCKER_USERNAME }}
          password: ${{ secrets.DOCKER_PASSWORD }}

      - name: Build and push
        uses: docker/build-push-action@v4
        with:
          context: .
          push: ${{ github.event_name == 'push' && github.ref == 'refs/heads/main' }}
          tags: |
            kinemotion:latest
            kinemotion:${{ github.sha }}
          cache-from: type=gha
          cache-to: type=gha,mode=max
```

**Setup Secrets:**

```bash
# In GitHub Settings > Secrets and variables > Actions
DOCKER_USERNAME: <your-docker-username>
DOCKER_PASSWORD: <your-docker-token>
```

**Acceptance Criteria:**

- [ ] Docker image builds on PR (no push)
- [ ] Docker image builds and pushes on main
- [ ] Image tagged with commit SHA and "latest"
- [ ] Image available on Docker Hub

#### 2.2 Prometheus Integration (Day 2-3, 3-4 days)

**Owner:** Backend + DevOps
**Priority:** HIGH

**Step 1: Add Prometheus client to app**

```python
# /Users/feniix/src/personal/cursor/dropjump-claude/src/kinemotion/realtime/metrics.py
from prometheus_client import Counter, Histogram, Gauge, CollectorRegistry, REGISTRY

# Connection metrics
ws_active_connections = Gauge(
    'ws_active_connections',
    'Number of active WebSocket connections',
    registry=REGISTRY
)

# Analysis metrics
analyses_total = Counter(
    'analyses_total',
    'Total analyses completed',
    ['sport', 'quality'],
    registry=REGISTRY
)

analyses_failed = Counter(
    'analyses_failed',
    'Failed analyses',
    ['sport', 'error_type'],
    registry=REGISTRY
)

# Latency metrics (p50, p95, p99 via histogram)
frame_latency_ms = Histogram(
    'frame_latency_ms',
    'End-to-end frame processing latency in milliseconds',
    buckets=[10, 25, 50, 100, 150, 200, 250, 300, 500, 1000],
    registry=REGISTRY
)

mediapipe_process_ms = Histogram(
    'mediapipe_process_ms',
    'MediaPipe pose detection time in milliseconds',
    buckets=[5, 10, 20, 30, 40, 50, 100],
    registry=REGISTRY
)

kinematics_calc_ms = Histogram(
    'kinematics_calc_ms',
    'Kinematics calculation time in milliseconds',
    buckets=[1, 2, 5, 10, 20],
    registry=REGISTRY
)

# Resource metrics
memory_usage_mb = Gauge(
    'memory_usage_mb',
    'Memory usage in MB',
    registry=REGISTRY
)

cpu_usage_percent = Gauge(
    'cpu_usage_percent',
    'CPU usage percentage',
    registry=REGISTRY
)
```

**Step 2: Update FastAPI app to expose metrics**

```python
# /Users/feniix/src/personal/cursor/dropjump-claude/src/kinemotion/realtime/server.py
from fastapi import FastAPI, WebSocket
from prometheus_client import make_asgi_app, CollectorRegistry
import psutil
from kinemotion.realtime.metrics import (
    ws_active_connections, frame_latency_ms, memory_usage_mb, cpu_usage_percent
)

app = FastAPI()

# Mount Prometheus metrics endpoint
metrics_app = make_asgi_app()
app.mount("/metrics", metrics_app)

@app.on_event("startup")
async def startup_event():
    """Start resource monitoring task"""
    import asyncio
    asyncio.create_task(monitor_resources())

async def monitor_resources():
    """Periodically update resource metrics"""
    while True:
        process = psutil.Process()
        memory_usage_mb.set(process.memory_info().rss / 1024 / 1024)
        cpu_usage_percent.set(process.cpu_percent(interval=1))
        await asyncio.sleep(10)

@app.websocket("/ws/analyze")
async def websocket_endpoint(websocket: WebSocket):
    await websocket.accept()
    ws_active_connections.inc()

    try:
        while True:
            # Receive frame
            data = await websocket.receive_json()

            # Track latency
            import time
            start = time.perf_counter()

            # Process...
            metrics = process_frame(data["frame"])

            latency_ms = (time.perf_counter() - start) * 1000
            frame_latency_ms.observe(latency_ms)

            # Send response
            await websocket.send_json({"metrics": metrics})

    finally:
        ws_active_connections.dec()
        await websocket.close()

# Test: curl http://localhost:8000/metrics
```

**Acceptance Criteria:**

- [ ] `/metrics` endpoint responds with Prometheus format
- [ ] Metrics visible in output
- [ ] Gauges and counters incrementing correctly

#### 2.3 Grafana Dashboards (Day 2-3, 2-3 days)

**Owner:** DevOps
**Priority:** HIGH

```yaml
# /Users/feniix/src/personal/cursor/dropjump-claude/prometheus.yml
global:
  scrape_interval: 15s
  evaluation_interval: 15s

scrape_configs:
  - job_name: 'kinemotion'
    static_configs:
      - targets: ['localhost:8000']
    metrics_path: '/metrics'
```

**Create Grafana dashboards (JSON):**

```json
# /Users/feniix/src/personal/cursor/dropjump-claude/grafana/dashboards/realtime-health.json
{
  "dashboard": {
    "title": "Kinemotion Real-Time Health",
    "panels": [
      {
        "title": "Active WebSocket Connections",
        "targets": [{"expr": "ws_active_connections"}],
        "type": "gauge"
      },
      {
        "title": "P95 Frame Latency (ms)",
        "targets": [{"expr": "histogram_quantile(0.95, rate(frame_latency_ms_bucket[5m]))"}],
        "type": "graph"
      },
      {
        "title": "Error Rate",
        "targets": [{"expr": "rate(analyses_failed_total[5m])"}],
        "type": "graph"
      },
      {
        "title": "Memory Usage (MB)",
        "targets": [{"expr": "memory_usage_mb"}],
        "type": "graph"
      },
      {
        "title": "CPU Usage (%)",
        "targets": [{"expr": "cpu_usage_percent"}],
        "type": "graph"
      }
    ]
  }
}
```

**Acceptance Criteria:**

- [ ] Grafana dashboard displays real-time metrics
- [ ] Charts update every 15 seconds
- [ ] No errors in dashboard rendering

#### 2.4 Set Up AWS ECS Staging Environment (Day 3-4, 2-3 days)

**Owner:** DevOps
**Priority:** CRITICAL

```bash
# Create ECS cluster and service via AWS Console or Terraform
# For simplicity, use AWS Console:
# 1. Create ECS cluster: "kinemotion-staging"
# 2. Create ECR repository: "kinemotion"
# 3. Create RDS PostgreSQL instance
# 4. Create ElastiCache Redis instance
```

```terraform
# Alternative: /Users/feniix/src/personal/cursor/dropjump-claude/terraform/staging.tf
provider "aws" {
  region = "us-east-1"
}

# ECR Repository
resource "aws_ecr_repository" "kinemotion" {
  name                 = "kinemotion"
  image_tag_mutability = "MUTABLE"
}

# ECS Cluster
resource "aws_ecs_cluster" "staging" {
  name = "kinemotion-staging"
}

# RDS PostgreSQL
resource "aws_db_instance" "postgres" {
  allocated_storage    = 20
  engine              = "postgres"
  engine_version      = "15.0"
  instance_class      = "db.t3.micro"
  db_name             = "kinemotion"
  username            = "postgres"
  password            = var.db_password
  skip_final_snapshot = true
}

# ElastiCache Redis
resource "aws_elasticache_cluster" "redis" {
  cluster_id           = "kinemotion-redis"
  engine              = "redis"
  node_type           = "cache.t3.micro"
  num_cache_nodes     = 1
  parameter_group_name = "default.redis7"
  engine_version       = "7.0"
  port                 = 6379
}

output "ecr_repository_url" {
  value = aws_ecr_repository.kinemotion.repository_url
}

output "rds_endpoint" {
  value = aws_db_instance.postgres.endpoint
}

output "redis_endpoint" {
  value = aws_elasticache_cluster.redis.cache_nodes[0].address
}
```

**Deployment workflow:**

```yaml
# /Users/feniix/src/personal/cursor/dropjump-claude/.github/workflows/deploy-staging.yml
name: Deploy to Staging

on:
  push:
    branches: [main]

jobs:
  deploy:
    runs-on: ubuntu-latest

    steps:
      - uses: actions/checkout@v4

      - name: Configure AWS credentials
        uses: aws-actions/configure-aws-credentials@v2
        with:
          aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
          aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
          aws-region: us-east-1

      - name: Login to ECR
        run: |
          aws ecr get-login-password --region us-east-1 | \
            docker login --username AWS --password-stdin ${{ secrets.ECR_REGISTRY }}

      - name: Build and push to ECR
        run: |
          docker build -t ${{ secrets.ECR_REGISTRY }}/kinemotion:latest .
          docker push ${{ secrets.ECR_REGISTRY }}/kinemotion:latest

      - name: Update ECS service
        run: |
          aws ecs update-service \
            --cluster kinemotion-staging \
            --service realtime-api \
            --force-new-deployment

      - name: Wait for deployment
        run: |
          aws ecs wait services-stable \
            --cluster kinemotion-staging \
            --services realtime-api
```

**Acceptance Criteria:**

- [ ] ECS cluster created
- [ ] RDS PostgreSQL accessible
- [ ] ElastiCache Redis accessible
- [ ] Docker image pushed to ECR
- [ ] ECS service running

#### 2.5 Create Deployment Runbook (Day 1-2, 1-2 days)

**Owner:** DevOps
**Priority:** HIGH

````markdown
# /Users/feniix/src/personal/cursor/dropjump-claude/docs/deployment/runbook.md
## Deployment Runbook

### Staging Deployment (Automatic on main push)

1. GitHub Actions builds Docker image
2. Image pushed to ECR
3. ECS service updated automatically
4. New container starts within 2 minutes

### Production Deployment (Manual, Canary Strategy)

1. **Pre-deployment checks:**
   ```bash
   # All tests passing?
   # Coverage >= 80%?
   # Load test p95 < 200ms?
   # No critical SonarCloud issues?
````

1. **Canary deployment (10% traffic):**

   ```bash
   aws ecs update-service \
     --cluster kinemotion-prod \
     --service realtime-api \
     --desired-count 1
   ```

1. **Monitor (5 minutes):**

   - Check error rate (should be \< 1%)
   - Check latency (p95 \< 200ms)
   - Check logs for errors

1. **If canary successful, scale to 50% traffic:**

   ```bash
   aws ecs update-service \
     --cluster kinemotion-prod \
     --service realtime-api \
     --desired-count 2
   ```

1. **Monitor (10 minutes):**

   - Same checks

1. **If still successful, scale to 100%:**

   ```bash
   aws ecs update-service \
     --cluster kinemotion-prod \
     --service realtime-api \
     --desired-count 4
   ```

### Automatic Rollback (Triggered if error rate > 5%)

```bash
# AWS Lambda function monitors metrics
# If error rate spikes, triggers rollback:
aws ecs update-service \
  --cluster kinemotion-prod \
  --service realtime-api \
  --task-definition kinemotion:$(( CURRENT_VERSION - 1 ))
```

### Manual Rollback

```bash
# Get previous task definition
PREVIOUS=$(aws ecs describe-services \
  --cluster kinemotion-prod \
  --services realtime-api | \
  jq -r '.services[0].deployments[1].taskDefinition')

# Rollback
aws ecs update-service \
  --cluster kinemotion-prod \
  --service realtime-api \
  --task-definition $PREVIOUS
```

### Monitoring Dashboards

- [Grafana Real-Time Health](http://grafana-endpoint/d/realtime-health)
- [CloudWatch Logs](https://console.aws.amazon.com/logs)
- [SonarCloud Quality](https://sonarcloud.io/project/overview?id=feniix_kinemotion)

### Escalation

- **Latency SLO violation (p95 > 200ms):** Page on-call engineer
- **Error rate > 1%:** Page on-call engineer
- **Database connection errors:** Check RDS status
- **Redis connection errors:** Check ElastiCache status

````

**Acceptance Criteria:**
- [ ] Runbook documented
- [ ] Team trained on procedures
- [ ] Test rollback manually (in staging)

### Week 2 Deliverables

- [ ] GitHub Actions build and push Docker image
- [ ] Docker image in ECR registry
- [ ] Prometheus metrics integrated
- [ ] Grafana dashboards created and displaying data
- [ ] AWS staging environment provisioned
- [ ] Deployment runbook documented
- [ ] Automated deploy to staging working

### Week 2 Success Criteria

✓ Docker image builds and pushes on main
✓ Metrics endpoint available
✓ Grafana dashboards show real-time data
✓ Staging environment accessible
✓ Manual deployment works
✓ Monitoring alerts configured

---

## WEEK 3: Real-Time Service Active Deployment

### Objectives
1. Deploy real-time service to staging
2. Run load testing on staging
3. Validate latency meets SLOs
4. Prepare for production deployment

### Key Tasks

#### 3.1 Deploy to Staging (Day 1, 1 day)

```bash
# Automated by GitHub Actions
# Just merge PR to main, watch deployment
````

#### 3.2 Load Test Staging (Day 2-3, 2 days)

```bash
# Run load test from local or CI
locust -f tests/load_test_websocket.py \
  --headless \
  -u 100 \
  -r 10 \
  --run-time 5m \
  --csv results_staging \
  --host https://staging.kinemotion.example.com

# Check results
python scripts/check_load_test_slo.py results_staging
```

**Acceptance Criteria:**

- [ ] p95 latency \< 200ms
- [ ] Error rate \< 1%
- [ ] No crashes or memory leaks
- [ ] Throughput > 100 concurrent

#### 3.3 Set Up Production Environment (Day 2-3, 2 days)

Similar to staging, but with higher resource limits and multi-AZ deployment.

#### 3.4 Canary Deployment Plan (Day 4, 1 day)

Document and test canary deployment steps.

### Week 3 Deliverables

- [ ] Real-time service running on staging
- [ ] Load test validates \<200ms latency
- [ ] Production environment ready
- [ ] Canary deployment strategy tested
- [ ] Monitoring alerts validated

______________________________________________________________________

## WEEK 4-6: Ongoing Monitoring & Optimization

### Objectives

1. Monitor production latency and error rates
1. Optimize based on real metrics
1. Auto-scaling policies tuned
1. Incident response procedures proven

### Continuous Tasks

- [ ] Monitor SLOs hourly
- [ ] Investigate any SLO violations
- [ ] Optimize bottlenecks
- [ ] Tune auto-scaling thresholds
- [ ] Weekly performance reviews

______________________________________________________________________

## Infrastructure Checklist

### Pre-Week 3 (Before Real-Time Goes Live)

- [ ] Dockerfile tested and \<500MB
- [ ] Docker Compose running locally
- [ ] Load tests passing (p95 \< 200ms)
- [ ] Performance profiling baseline established
- [ ] Docker image in ECR
- [ ] GitHub Actions workflows working
- [ ] Prometheus + Grafana running
- [ ] Staging environment accessible
- [ ] Canary deployment automated
- [ ] Monitoring dashboards live
- [ ] Alert rules configured
- [ ] Runbook documented and tested
- [ ] Team trained on procedures

### Pre-Production (Before Deploy to Prod)

- [ ] All staging tests passing
- [ ] Load test: 100 concurrent, \<200ms
- [ ] Error rate: \<1% under load
- [ ] Monitoring: All metrics visible
- [ ] Alerts: Tested and trigger correctly
- [ ] Rollback: Practiced and automated
- [ ] On-call: Team trained and scheduled
- [ ] Security: TLS, rate limiting, auth enabled
- [ ] Backup: Automated, restore tested
- [ ] Cost: Budget approved, monitoring in place

______________________________________________________________________

## Risk Mitigation Timeline

| Week | Risk                | Mitigation                                 |
| ---- | ------------------- | ------------------------------------------ |
| 1    | Latency > 200ms     | Performance testing, profile bottlenecks   |
| 2    | Deployment failures | Test in staging, automate rollback         |
| 3    | Scaling bottleneck  | Load test, monitor CPU/latency correlation |
| 4-6  | Production issues   | 24/7 monitoring, alert on SLO violation    |

______________________________________________________________________

**Status:** Detailed implementation guide ready
**Timeline:** 6 weeks total (Weeks 1-6 aligned with feature roadmap)
**Owner:** DevOps/CI-CD Engineer + Backend Developer
