Observability & Monitoring Architecture

Introduction to Observability & Monitoring

Observability and monitoring in microservices provide insights into system health, performance, and issues through logs, metrics, and traces. Tools like ELK or Loki for log aggregation, Prometheus and Grafana for metrics, Jaeger and OpenTelemetry for distributed tracing, and alerting pipelines ensure operational awareness. This architecture enables rapid troubleshooting and proactive issue resolution in distributed systems.

Observability & Monitoring Diagram

The diagram below illustrates the observability architecture with Log Aggregators (yellow), Metrics (orange-red), Distributed Tracing (blue), and Alerting Pipelines (red). Each component is color-coded for clarity, with distinct flows showing how data moves through the system.

Key Observability Components

The core components of an observability and monitoring architecture include:

• Log Aggregators (ELK Stack, Loki):
  • ELK: Elasticsearch (storage), Logstash (processing), Kibana (visualization)
  • Loki: Log aggregation optimized for Kubernetes with Grafana integration
• Metrics System (Prometheus, Grafana):
  • Prometheus: Time-series database with powerful query language (PromQL)
  • Grafana: Visualization platform with customizable dashboards
• Distributed Tracing (Jaeger, OpenTelemetry):
  • OpenTelemetry: Vendor-neutral instrumentation library
  • Jaeger: End-to-end distributed tracing system
• Alerting Pipeline (Alertmanager):
  • Deduplicates, groups, and routes alerts to proper receivers
  • Integrates with Slack, PagerDuty, email, and more

Benefits of Observability

• Proactive Issue Detection:
  • Identify anomalies through metric thresholds
  • Detect patterns in logs before they cause outages
• Comprehensive Debugging:
  • Trace requests across service boundaries
  • Correlate logs, metrics, and traces for root cause analysis
• Performance Optimization:
  • Identify bottlenecks through tracing
  • Monitor resource utilization metrics
• Business Insights:
  • Track user journeys through the system
  • Measure business metrics alongside technical metrics

Implementation Considerations

Implementing observability requires careful planning:

• Instrumentation Strategy:
  • Use OpenTelemetry for consistent instrumentation across services
  • Standardize log formats (JSON with common fields)
  • Define service-specific metrics (latency, error rates, throughput)
• Data Management:
  • Configure log retention policies (30-90 days typically)
  • Downsample metrics after certain periods
  • Sample traces appropriately (100% for critical paths, 1% for others)
• Alert Design:
  • Follow the "3-key-metrics" principle (errors, latency, saturation)
  • Implement multi-level alerts (warning/critical)
  • Route alerts to proper teams with runbook links
• Security & Access Control:
  • Secure access to observability tools with RBAC
  • Mask sensitive data in logs
  • Encrypt data in transit and at rest

Example Configuration: OpenTelemetry Collector

Configuration for collecting and exporting telemetry data:

# otel-collector-config.yaml
receivers:
  otlp:
    protocols:
      grpc:
      http:

processors:
  batch:
    timeout: 5s
    send_batch_size: 1000
  memory_limiter:
    limit_mib: 400
    spike_limit_mib: 100
    check_interval: 1s

exporters:
  logging:
    loglevel: debug
  prometheus:
    endpoint: "0.0.0.0:8889"
  jaeger:
    endpoint: "jaeger:14250"
    tls:
      insecure: true
  loki:
    endpoint: "http://loki:3100/loki/api/v1/push"
    labels:
      resource:
        "service.name": "service_name"
        "k8s.cluster.name": "cluster_name"

service:
  pipelines:
    traces:
      receivers: [otlp]
      processors: [memory_limiter, batch]
      exporters: [jaeger]
    metrics:
      receivers: [otlp]
      processors: [memory_limiter, batch]
      exporters: [prometheus]
    logs:
      receivers: [otlp]
      processors: [memory_limiter, batch]
      exporters: [loki]
                

Example Configuration: Prometheus Alert Rules

Alerting rules for Prometheus to detect common issues:

# alert_rules.yml
groups:
- name: service-health
  rules:
  - alert: HighErrorRate
    expr: rate(http_requests_total{status=~"5.."}[5m]) / rate(http_requests_total[5m]) > 0.05
    for: 10m
    labels:
      severity: critical
    annotations:
      summary: "High error rate on {{ $labels.service }}"
      description: "Error rate is {{ $value }}% for service {{ $labels.service }}"
      
  - alert: HighLatency
    expr: histogram_quantile(0.99, rate(http_request_duration_seconds_bucket[5m])) > 2
    for: 5m
    labels:
      severity: warning
    annotations:
      summary: "High latency on {{ $labels.service }}"
      description: "99th percentile latency is {{ $value }}s for service {{ $labels.service }}"
      
  - alert: ServiceDown
    expr: up == 0
    for: 1m
    labels:
      severity: critical
    annotations:
      summary: "Service {{ $labels.job }} is down"
      description: "Service {{ $labels.job }} has been down for more than 1 minute"