# Infrastructure Monitoring Guidelines and Standards

## Overview

This document establishes comprehensive monitoring standards for infrastructure and applications, ensuring observability, performance tracking, and proactive issue detection across all environments.

## Monitoring Strategy

### Three Pillars of Observability

**1. Metrics Collection**
- Infrastructure metrics (CPU, memory, disk, network)
- Application performance metrics (response time, throughput, errors)
- Business metrics (user activity, transactions, revenue)
- Custom metrics for domain-specific monitoring

**2. Logging Standards**
- Structured logging with consistent formats
- Centralized log aggregation and analysis
- Log retention policies and compliance
- Security and audit logging

**3. Distributed Tracing**
- End-to-end request tracing
- Service dependency mapping
- Performance bottleneck identification
- Error propagation analysis

## Infrastructure Monitoring

### System Metrics

**Required Infrastructure Metrics:**
```yaml
# Prometheus configuration
global:
  scrape_interval: 15s
  evaluation_interval: 15s

rule_files:
  - "infrastructure_rules.yml"
  - "application_rules.yml"

scrape_configs:
  - job_name: 'node-exporter'
    static_configs:
      - targets: ['localhost:9100']
    scrape_interval: 5s
    metrics_path: /metrics

  - job_name: 'application'
    static_configs:
      - targets: ['app:8080']
    scrape_interval: 10s
```

**Critical Infrastructure Alerts:**
```yaml
# infrastructure_rules.yml
groups:
  - name: infrastructure
    rules:
      - alert: HighCPUUsage
        expr: 100 - (avg by(instance) (rate(node_cpu_seconds_total{mode="idle"}[5m])) * 100) > 80
        for: 5m
        labels:
          severity: warning
        annotations:
          summary: "High CPU usage on {{ $labels.instance }}"

      - alert: HighMemoryUsage
        expr: (node_memory_MemTotal_bytes - node_memory_MemAvailable_bytes) / node_memory_MemTotal_bytes * 100 > 85
        for: 5m
        labels:
          severity: critical
        annotations:
          summary: "High memory usage on {{ $labels.instance }}"

      - alert: DiskSpaceLow
        expr: (node_filesystem_avail_bytes / node_filesystem_size_bytes) * 100 < 10
        for: 5m
        labels:
          severity: critical
        annotations:
          summary: "Low disk space on {{ $labels.instance }}"
```

### Container Monitoring

**Kubernetes Monitoring Stack:**
```yaml
# kube-prometheus-stack values
prometheus:
  prometheusSpec:
    retention: 30d
    storageSpec:
      volumeClaimTemplate:
        spec:
          storageClassName: gp3
          accessModes: ["ReadWriteOnce"]
          resources:
            requests:
              storage: 100Gi

grafana:
  adminPassword: ${GRAFANA_ADMIN_PASSWORD}
  persistence:
    enabled: true
    size: 10Gi
  
  dashboardProviders:
    dashboardproviders.yaml:
      apiVersion: 1
      providers:
      - name: 'default'
        orgId: 1
        folder: ''
        type: file
        disableDeletion: false
        editable: true
        options:
          path: /var/lib/grafana/dashboards/default

alertmanager:
  config:
    global:
      slack_api_url: '${SLACK_WEBHOOK_URL}'
    
    route:
      group_by: ['alertname']
      group_wait: 10s
      group_interval: 10s
      repeat_interval: 1h
      receiver: 'web.hook'
    
    receivers:
    - name: 'web.hook'
      slack_configs:
      - channel: '#alerts'
        title: 'Alert: {{ .GroupLabels.alertname }}'
        text: '{{ range .Alerts }}{{ .Annotations.summary }}{{ end }}'
```

## Application Monitoring

### Application Performance Monitoring (APM)

**Metrics Collection Standards:**
```python
# Python application metrics example
from prometheus_client import Counter, Histogram, Gauge, start_http_server
import time

# Define metrics
REQUEST_COUNT = Counter('http_requests_total', 'Total HTTP requests', ['method', 'endpoint', 'status'])
REQUEST_LATENCY = Histogram('http_request_duration_seconds', 'HTTP request latency')
ACTIVE_CONNECTIONS = Gauge('active_connections', 'Active database connections')

# Middleware for automatic metrics collection
def metrics_middleware(request, response):
    REQUEST_COUNT.labels(
        method=request.method,
        endpoint=request.path,
        status=response.status_code
    ).inc()
    
    with REQUEST_LATENCY.time():
        # Process request
        pass

# Custom business metrics
ORDERS_PROCESSED = Counter('orders_processed_total', 'Total orders processed')
REVENUE_GENERATED = Gauge('revenue_generated_dollars', 'Revenue generated in dollars')
```

**Health Check Implementation:**
```python
# Health check endpoint
from flask import Flask, jsonify
import psycopg2
import redis

app = Flask(__name__)

@app.route('/health')
def health_check():
    checks = {
        'database': check_database(),
        'redis': check_redis(),
        'external_api': check_external_api()
    }
    
    overall_status = 'healthy' if all(checks.values()) else 'unhealthy'
    status_code = 200 if overall_status == 'healthy' else 503
    
    return jsonify({
        'status': overall_status,
        'timestamp': time.time(),
        'checks': checks
    }), status_code

def check_database():
    try:
        conn = psycopg2.connect(DATABASE_URL)
        conn.close()
        return True
    except:
        return False
```

### Error Tracking and Alerting

**Error Monitoring Configuration:**
```yaml
# Sentry configuration
sentry:
  dsn: ${SENTRY_DSN}
  environment: ${ENVIRONMENT}
  release: ${BUILD_VERSION}
  
  # Error sampling
  sample_rate: 1.0
  traces_sample_rate: 0.1
  
  # Performance monitoring
  profiles_sample_rate: 0.1
  
  # Custom tags
  tags:
    component: backend
    team: platform
```

## Logging Standards

### Structured Logging Format

**JSON Logging Standard:**
```json
{
  "timestamp": "2024-01-15T10:30:00.000Z",
  "level": "INFO",
  "logger": "com.example.UserService",
  "message": "User created successfully",
  "request_id": "req_123456789",
  "user_id": "user_456",
  "session_id": "sess_789",
  "ip_address": "192.168.1.1",
  "user_agent": "Mozilla/5.0...",
  "duration_ms": 45,
  "status_code": 201,
  "method": "POST",
  "path": "/api/users",
  "environment": "production",
  "service": "user-service",
  "version": "1.2.3"
}
```

**Log Aggregation Stack:**
```yaml
# ELK Stack configuration
elasticsearch:
  replicas: 3
  resources:
    requests:
      memory: "2Gi"
      cpu: "1000m"
    limits:
      memory: "4Gi"
      cpu: "2000m"

logstash:
  config:
    input:
      beats:
        port: 5044
    
    filter:
      if [fields][service] {
        mutate {
          add_field => { "service" => "%{[fields][service]}" }
        }
      }
      
      date {
        match => [ "timestamp", "ISO8601" ]
      }
    
    output:
      elasticsearch:
        hosts: ["elasticsearch:9200"]
        index: "logs-%{+YYYY.MM.dd}"

kibana:
  resources:
    requests:
      memory: "1Gi"
      cpu: "500m"
```

## Alerting and Notification

### Alert Management

**Alert Severity Levels:**
- **Critical**: Immediate action required (P0)
- **Warning**: Action required within 1 hour (P1)
- **Info**: Awareness, action within 24 hours (P2)

**Alerting Rules:**
```yaml
# Critical alerts
- alert: ServiceDown
  expr: up == 0
  for: 1m
  labels:
    severity: critical
  annotations:
    summary: "Service {{ $labels.job }} is down"
    runbook_url: "https://runbooks.example.com/service-down"

- alert: HighErrorRate
  expr: rate(http_requests_total{status=~"5.."}[5m]) > 0.1
  for: 5m
  labels:
    severity: critical
  annotations:
    summary: "High error rate: {{ $value }}%"

# Warning alerts
- alert: HighLatency
  expr: histogram_quantile(0.95, rate(http_request_duration_seconds_bucket[5m])) > 0.5
  for: 10m
  labels:
    severity: warning
  annotations:
    summary: "High latency: {{ $value }}s"
```

### Notification Channels

**Multi-Channel Alerting:**
```yaml
# AlertManager routing
route:
  group_by: ['alertname', 'cluster', 'service']
  group_wait: 10s
  group_interval: 10s
  repeat_interval: 12h
  receiver: 'default'
  
  routes:
  - match:
      severity: critical
    receiver: 'critical-alerts'
    group_wait: 0s
    repeat_interval: 5m
  
  - match:
      severity: warning
    receiver: 'warning-alerts'
    repeat_interval: 1h

receivers:
- name: 'critical-alerts'
  pagerduty_configs:
  - service_key: '${PAGERDUTY_SERVICE_KEY}'
    description: '{{ .GroupLabels.alertname }}: {{ .CommonAnnotations.summary }}'
  
  slack_configs:
  - api_url: '${SLACK_WEBHOOK_URL}'
    channel: '#critical-alerts'
    title: 'CRITICAL: {{ .GroupLabels.alertname }}'
    text: '{{ .CommonAnnotations.summary }}'

- name: 'warning-alerts'
  slack_configs:
  - api_url: '${SLACK_WEBHOOK_URL}'
    channel: '#alerts'
    title: 'WARNING: {{ .GroupLabels.alertname }}'
    text: '{{ .CommonAnnotations.summary }}'
```

## Performance Monitoring

### SLA/SLO Monitoring

**Service Level Objectives:**
```yaml
# SLO definitions
slos:
  api_availability:
    target: 99.9%
    measurement: uptime
    window: 30d
  
  api_latency:
    target: 95th percentile < 500ms
    measurement: response_time
    window: 7d
  
  error_rate:
    target: < 0.1%
    measurement: error_percentage
    window: 24h
```

**SLO Monitoring Queries:**
```promql
# Availability SLO
(
  sum(rate(http_requests_total{status!~"5.."}[5m])) /
  sum(rate(http_requests_total[5m]))
) * 100

# Latency SLO
histogram_quantile(0.95, 
  sum(rate(http_request_duration_seconds_bucket[5m])) by (le)
)

# Error rate SLO
(
  sum(rate(http_requests_total{status=~"5.."}[5m])) /
  sum(rate(http_requests_total[5m]))
) * 100
```

## Monitoring Automation

### Auto-Discovery and Configuration

**Service Discovery:**
```yaml
# Prometheus service discovery
scrape_configs:
  - job_name: 'kubernetes-pods'
    kubernetes_sd_configs:
    - role: pod
    
    relabel_configs:
    - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_scrape]
      action: keep
      regex: true
    
    - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_path]
      action: replace
      target_label: __metrics_path__
      regex: (.+)
```

**Dashboard Automation:**
```python
# Automated dashboard generation
import json
from grafana_api import GrafanaApi

def create_service_dashboard(service_name, metrics):
    dashboard = {
        "dashboard": {
            "title": f"{service_name} Service Dashboard",
            "panels": [
                {
                    "title": "Request Rate",
                    "type": "graph",
                    "targets": [{
                        "expr": f"rate(http_requests_total{{service='{service_name}'}}[5m])"
                    }]
                },
                {
                    "title": "Error Rate",
                    "type": "graph", 
                    "targets": [{
                        "expr": f"rate(http_requests_total{{service='{service_name}',status=~'5..'}}[5m])"
                    }]
                }
            ]
        }
    }
    
    grafana = GrafanaApi.from_url(GRAFANA_URL, auth=GRAFANA_TOKEN)
    grafana.dashboard.update_dashboard(dashboard)
```

## Compliance and Governance

### Monitoring Compliance

**Audit Logging Requirements:**
- All administrative actions logged
- Log integrity protection
- Retention policies enforced
- Access control monitoring

**Data Privacy Monitoring:**
```yaml
# Privacy-compliant logging
logging:
  pii_scrubbing:
    enabled: true
    fields: [email, phone, ssn, credit_card]
    replacement: "[REDACTED]"
  
  retention:
    application_logs: 90d
    security_logs: 2y
    audit_logs: 7y
```

### Monitoring Governance

**Monitoring Standards Enforcement:**
- Mandatory health checks for all services
- Required SLO definitions
- Standardized alert naming conventions
- Regular monitoring review cycles
