NudgeLang Documentation
Best Practices
Monitoring

Monitoring

This guide covers best practices for monitoring NudgeLang applications.

Metrics Collection

1. Basic Metrics 

# Basic metrics configuration
states:
  - id: metrics_state
    type: tool
    tool: collect_metrics
    input:
      service: "${input.service}"
    # Metrics configuration
    output:
      collected: "${output.collected}"

2. Advanced Metrics 

# Advanced metrics configuration
states:
  - id: metrics_state
    type: tool
    tool: collect_metrics
    input:
      service: "${input.service}"
    config:
      metrics:
        - name: "request_count"
          type: "counter"
          labels: ["method", "path", "status"]
        - name: "request_duration"
          type: "histogram"
          buckets: [0.1, 0.5, 1, 2, 5]
        - name: "error_count"
          type: "counter"
          labels: ["type", "service"]
      collection:
        interval: 15
        timeout: 5
    # Metrics configuration
    output:
      collected: "${output.collected}"
      metrics: "${output.metrics}"

Logging

1. Basic Logging 

# Basic logging configuration
states:
  - id: logging_state
    type: tool
    tool: configure_logging
    input:
      service: "${input.service}"
    # Logging configuration
    output:
      configured: "${output.configured}"

2. Advanced Logging 

# Advanced logging configuration
states:
  - id: logging_state
    type: tool
    tool: configure_logging
    input:
      service: "${input.service}"
    config:
      format: "json"
      level: "info"
      fields:
        - name: "service"
          value: "${service.name}"
        - name: "environment"
          value: "${environment}"
      handlers:
        - type: "file"
          path: "/var/log/app.log"
          max_size: 100
          max_backups: 5
        - type: "syslog"
          facility: "local0"
    # Logging configuration
    output:
      configured: "${output.configured}"
      handlers: "${output.handlers}"

Tracing

1. Basic Tracing 

# Basic tracing configuration
states:
  - id: tracing_state
    type: tool
    tool: configure_tracing
    input:
      service: "${input.service}"
    # Tracing configuration
    output:
      configured: "${output.configured}"

2. Advanced Tracing 

# Advanced tracing configuration
states:
  - id: tracing_state
    type: tool
    tool: configure_tracing
    input:
      service: "${input.service}"
    config:
      sampler:
        type: "probabilistic"
        rate: 0.1
      reporter:
        type: "jaeger"
        endpoint: "http://jaeger:14268/api/traces"
      tags:
        - name: "service"
          value: "${service.name}"
        - name: "environment"
          value: "${environment}"
    # Tracing configuration
    output:
      configured: "${output.configured}"
      sampler: "${output.sampler}"

Alerting

1. Basic Alerting 

# Basic alerting configuration
states:
  - id: alerting_state
    type: tool
    tool: configure_alerts
    input:
      service: "${input.service}"
    # Alerting configuration
    output:
      configured: "${output.configured}"

2. Advanced Alerting 

# Advanced alerting configuration
states:
  - id: alerting_state
    type: tool
    tool: configure_alerts
    input:
      service: "${input.service}"
    config:
      rules:
        - name: "high_error_rate"
          condition: "error_rate > 0.05"
          duration: "5m"
          severity: "critical"
          notifications:
            - type: "email"
              to: "[email protected]"
            - type: "slack"
              channel: "#alerts"
        - name: "high_latency"
          condition: "p95_latency > 1s"
          duration: "5m"
          severity: "warning"
          notifications:
            - type: "slack"
              channel: "#alerts"
    # Alerting configuration
    output:
      configured: "${output.configured}"
      rules: "${output.rules}"

Best Practices

  1. Metrics: Collect relevant metrics
  2. Logging: Implement proper logging
  3. Tracing: Use distributed tracing
  4. Alerting: Set up meaningful alerts
  5. Visualization: Create useful dashboards
  6. Retention: Manage data retention
  7. Testing: Test monitoring setup

Common Pitfalls

  1. Missing Metrics: Not collecting important metrics
  2. Poor Logging: Insufficient logging
  3. No Tracing: Missing distributed tracing
  4. Alert Fatigue: Too many alerts
  5. Data Overload: Too much monitoring data

Next Steps

Last updated on
Deployment