Continuous Monitoring & Observability¶

Monitoring and observability are critical for maintaining reliable, performant systems. They provide insights into system behavior, help detect issues early, and enable data-driven decision making.

Monitoring vs Observability¶

Understanding the distinction between monitoring and observability is crucial for building effective system visibility.

Key Resources¶

• Observability vs. monitoring in DevOps | GitLab

Clear explanation of the differences and complementary nature of monitoring and observability.

• DevOps measurement: Monitoring and observability | Google Cloud

Google's comprehensive guide to implementing monitoring and observability in DevOps practices.

Monitoring vs Observability Comparison¶

Continuous Monitoring in DevOps¶

Continuous monitoring integrates monitoring practices throughout the software development lifecycle.

Continuous Monitoring Benefits¶

• What is Continuous Monitoring in DevOps? | BrowserStack

Comprehensive guide covering continuous monitoring implementation and benefits.

• Monitoring Cheatsheet - Introduction to DevOps | Codecademy

Quick reference for monitoring concepts and tools.

The Three Pillars of Observability¶

flowchart TB
    A[Observability] --> B[Metrics]
    A --> C[Logs]
    A --> D[Traces]

    B --> E[System Performance]
    B --> F[Business KPIs]

    C --> G[Application Events]
    C --> H[System Events]

    D --> I[Request Flow]
    D --> J[Performance Bottlenecks]

1. Metrics¶

Quantitative measurements of system behavior over time.

Types of Metrics: - System Metrics: CPU, memory, disk, network usage - Application Metrics: Response times, error rates, throughput - Business Metrics: User engagement, conversion rates, revenue - SLI/SLO Metrics: Service level indicators and objectives

Common Metric Patterns:

# Counter - monotonically increasing value
http_requests_total{method="GET", status="200"} 1234

# Gauge - current value that can go up or down
cpu_usage_percent 75.2

# Histogram - distribution of values
http_request_duration_seconds_bucket{le="0.1"} 100
http_request_duration_seconds_bucket{le="0.5"} 250

# Summary - quantiles and totals
api_response_time{quantile="0.95"} 450

2. Logs¶

Structured or unstructured records of events that occurred in the system.

Log Levels: - ERROR: Error events that might still allow the application to continue - WARN: Potentially harmful situations - INFO: Informational messages highlighting application progress - DEBUG: Fine-grained informational events for debugging - TRACE: Very detailed information, typically only enabled in development

Structured Logging Example:

{
  "timestamp": "2024-01-15T10:30:00Z",
  "level": "INFO",
  "service": "user-api",
  "trace_id": "abc123",
  "span_id": "def456",
  "message": "User login successful",
  "user_id": "user123",
  "ip_address": "192.168.1.100",
  "response_time_ms": 250
}

3. Distributed Tracing¶

Tracks requests as they flow through distributed systems.

Trace Components: - Trace: Complete journey of a request - Span: Individual operation within a trace - Tags: Key-value pairs for additional context - Baggage: Cross-process context propagation

Monitoring Implementation¶

Monitoring Stack Architecture¶

flowchart TB
    subgraph "Data Collection"
        A[Application] --> B[Metrics Exporter]
        A --> C[Log Agent]
        A --> D[Trace Agent]
    end

    subgraph "Data Storage"
        B --> E[Time Series DB]
        C --> F[Log Storage]
        D --> G[Trace Storage]
    end

    subgraph "Analysis & Alerting"
        E --> H[Monitoring Platform]
        F --> I[Log Analysis]
        G --> J[Trace Analysis]
    end

    subgraph "Visualization"
        H --> K[Dashboards]
        I --> L[Log Search]
        J --> M[Trace Viewer]
    end

Popular Monitoring Tools¶

Prometheus + Grafana Setup Example¶

# docker-compose.yml for monitoring stack
version: '3.8'

services:
  prometheus:
    image: prom/prometheus:latest
    ports:
      - "9090:9090"
    volumes:
      - ./prometheus.yml:/etc/prometheus/prometheus.yml
    command:
      - '--config.file=/etc/prometheus/prometheus.yml'
      - '--storage.tsdb.path=/prometheus'
      - '--web.console.libraries=/etc/prometheus/console_libraries'
      - '--web.console.templates=/etc/prometheus/consoles'
      - '--web.enable-lifecycle'

  grafana:
    image: grafana/grafana:latest
    ports:
      - "3000:3000"
    environment:
      - GF_SECURITY_ADMIN_PASSWORD=admin
    volumes:
      - grafana-storage:/var/lib/grafana

  node-exporter:
    image: prom/node-exporter:latest
    ports:
      - "9100:9100"
    volumes:
      - /proc:/host/proc:ro
      - /sys:/host/sys:ro
      - /:/rootfs:ro
    command:
      - '--path.procfs=/host/proc'
      - '--path.rootfs=/rootfs'
      - '--path.sysfs=/host/sys'
      - '--collector.filesystem.mount-points-exclude=^/(sys|proc|dev|host|etc)($$|/)'

volumes:
  grafana-storage:

# prometheus.yml configuration
global:
  scrape_interval: 15s
  evaluation_interval: 15s

rule_files:
  - "alert_rules.yml"

alerting:
  alertmanagers:
    - static_configs:
        - targets:
          - alertmanager:9093

scrape_configs:
  - job_name: 'prometheus'
    static_configs:
      - targets: ['localhost:9090']

  - job_name: 'node-exporter'
    static_configs:
      - targets: ['node-exporter:9100']

  - job_name: 'application'
    static_configs:
      - targets: ['app:8080']
    metrics_path: '/metrics'
    scrape_interval: 30s

Alerting Strategy¶

Alert Design Principles¶

Alert Severity Levels¶

Sample Alert Rules (Prometheus)¶

# alert_rules.yml
groups:
- name: system
  rules:
  - alert: HighCPUUsage
    expr: 100 - (avg by(instance) (rate(node_cpu_seconds_total{mode="idle"}[2m])) * 100) > 80
    for: 5m
    labels:
      severity: warning
    annotations:
      summary: "High CPU usage detected"
      description: "CPU usage is above 80% for more than 5 minutes on {{ $labels.instance }}"

  - alert: HighMemoryUsage
    expr: (node_memory_MemTotal_bytes - node_memory_MemAvailable_bytes) / node_memory_MemTotal_bytes * 100 > 90
    for: 3m
    labels:
      severity: critical
    annotations:
      summary: "High memory usage detected"
      description: "Memory usage is above 90% on {{ $labels.instance }}"

- name: application
  rules:
  - alert: HighErrorRate
    expr: rate(http_requests_total{status=~"5.."}[5m]) / rate(http_requests_total[5m]) * 100 > 5
    for: 2m
    labels:
      severity: critical
    annotations:
      summary: "High error rate detected"
      description: "Error rate is above 5% for the last 5 minutes"

  - alert: SlowResponseTime
    expr: histogram_quantile(0.95, rate(http_request_duration_seconds_bucket[5m])) > 1
    for: 5m
    labels:
      severity: warning
    annotations:
      summary: "Slow response time detected"
      description: "95th percentile response time is above 1 second"

SLIs, SLOs, and SLAs¶

Service Level Management¶

Service Level Indicators (SLIs): Quantitative measures of service reliability Service Level Objectives (SLOs): Target values or ranges for SLIs Service Level Agreements (SLAs): Contractual agreements about service levels

Common SLIs¶

Error Budget Calculation¶

Error Budget = 100% - SLO
Example: 99.9% SLO = 0.1% error budget

Monthly downtime allowance (30 days):
30 days × 24 hours × 60 minutes × 0.001 = 43.2 minutes

Deployment Environments¶

Understanding different deployment environments is crucial for effective monitoring and deployment strategies.

Environment Types¶

• Production Environment vs Development, Stage, and Production | DEV Community

Clear explanation of different environment types and their purposes.

• Deployment Environment | Wikipedia

Comprehensive overview of deployment environment concepts.

Environment Comparison¶

Environment-Specific Monitoring¶

# Environment-based monitoring configuration
monitoring:
  development:
    metrics: basic
    alerts: none
    retention: 7 days

  staging:
    metrics: full
    alerts: team-only
    retention: 30 days

  production:
    metrics: comprehensive
    alerts: pager-duty
    retention: 1 year
    sla_monitoring: true

Monitoring Best Practices¶

Implementation Guidelines¶

Common Monitoring Pitfalls¶

Next Steps¶

After implementing monitoring and observability:

• Advanced Alerting and Monitoring strategies
• Performance Tuning techniques
• Incident Management processes