Monitoring and Observability Standards

This document outlines the standards and best practices for implementing monitoring and observability across all Bayat projects. Following these guidelines ensures consistent, effective monitoring that provides actionable insights into system health and performance.

Monitoring Principles

All monitoring and observability at Bayat should adhere to these core principles:

Actionable: Monitoring should provide actionable information
Relevant: Focus on what matters to the business and users
Timely: Detect and alert on issues promptly
Transparent: Make monitoring accessible to all stakeholders
Comprehensive: Cover all critical systems and components
Automated: Automate monitoring setup and maintenance
Contextual: Provide context to understand the significance of metrics
Proportional: Balance monitoring effort with system importance
Historical: Retain historical data for trend analysis
Correlated: Connect related information across monitoring systems

Metrics Standards

Core Metrics

Standardize on these core metrics for all systems:

Category	Metrics	Description
Service Level	Availability, Error Rate, Latency	Overall service health
Resource	CPU, Memory, Disk, Network	Resource utilization
Application	Request Count, Success Rate, Duration	Application performance
Business	Transactions, User Activity, Conversions	Business impact
Dependencies	External Service Health, Response Time	Dependency health

Metric Naming

Follow a consistent naming convention:

Use lowercase with underscores as separators
Use the format: [namespace]_[metric_name]_[unit]
Group related metrics with a common prefix
Be specific about what is being measured
Include the unit of measurement where applicable

Examples:

http_requests_total
api_request_duration_seconds
database_connections_current
memory_usage_bytes

Metric Types

Use appropriate metric types:

Counter: Ever-increasing values (e.g., request count, errors)
Gauge: Values that can go up and down (e.g., memory usage, queue depth)
Histogram: Distribution of values (e.g., request duration)
Summary: Similar to histogram but with calculated quantiles

Metric Cardinality

Manage metric cardinality:

Limit high-cardinality labels (e.g., user IDs, IP addresses)
Focus on meaningful aggregations
Consider sampling for high-volume metrics
Document cardinality limits for each system

Metric Collection Frequency

Standardize collection frequencies:

Critical Metrics: 10-30 second intervals
Standard Metrics: 1 minute intervals
Slow-changing Metrics: 5+ minute intervals

Document the rationale for any deviations from these standards.

Logging Standards

Log Levels

Use standard log levels consistently:

ERROR: System errors requiring immediate attention
WARN: Potential issues that don't stop operation
INFO: Normal operational information
DEBUG: Detailed information for debugging
TRACE: Very detailed debugging information

Log Format

Structure logs in a consistent JSON format:

{
  "timestamp": "2023-05-08T12:34:56.789Z",
  "level": "INFO",
  "service": "payment-service",
  "instance": "payment-api-67890",
  "trace_id": "abc123def456",
  "message": "Payment processed successfully",
  "context": {
    "user_id": "user-123",
    "payment_id": "pmt-456",
    "amount": 99.95,
    "currency": "USD"
  }
}

Include these standard fields in all logs:

timestamp: ISO 8601 format with timezone
level: Log level
service: Service or component name
instance: Instance identifier
trace_id: Distributed tracing ID
message: Human-readable message
context: Relevant contextual information

Sensitive Information

Handle sensitive information in logs:

Never log passwords, tokens, or credentials
Mask or truncate sensitive personal data
Comply with relevant privacy regulations
Document what should and should not be logged

Log Retention

Define standard log retention periods:

Production: Minimum 90 days
Non-production: Minimum 30 days
Security events: Minimum 1 year
Compliance-related: As required by regulations

Contextual Logging

Include relevant context in logs:

Link logs to specific requests or transactions
Include business identifiers (user ID, order ID, etc.)
Add relevant technical context (server, component, etc.)
Tag logs with environment information

Tracing Standards

Trace Context Propagation

Implement distributed tracing across all services:

Use W3C Trace Context or OpenTelemetry standards
Propagate trace context in all service-to-service calls
Ensure consistent trace ID format across all systems
Include trace IDs in logs and metrics where relevant

Span Naming

Use consistent span naming:

Include the operation type and target
Be specific but avoid high cardinality
Use the format: [operation].[target]
Document standard span names for common operations

Examples:

http.request
db.query
cache.get
payment.process

Span Attributes

Include these standard attributes in spans:

service.name: Name of the service
service.version: Version of the service
host.name: Name of the host
http.method: HTTP method for web requests
http.url: URL for web requests
http.status_code: Status code for HTTP responses
db.system: Database system type
db.operation: Database operation type

Sampling Strategy

Implement a balanced sampling strategy:

Sample 100% of traffic in development environments
Use adaptive sampling in production
Always trace errors and critical transactions
Document sampling decisions and rationales

Alerting Standards

Alert Severity Levels

Define clear severity levels:

Level	Description	Response Time	Notification Method
P1	Critical business impact, service down	Immediate (24/7)	Phone, SMS, dedicated channel
P2	Significant impact, degraded service	< 30 minutes (business hours)	SMS, email, dedicated channel
P3	Minor impact, non-critical issues	< 4 hours (business hours)	Email, general channel
P4	Low impact, cosmetic issues	Next business day	Email, ticket

Alert Content

Include essential information in alerts:

Clear, actionable title
Severity level
Affected system and component
Issue summary
Impact assessment
Suggested remediation steps
Link to runbook
Alert context and relevant metrics

Example alert template:

[P1] Payment Service: High Error Rate (25%) exceeding threshold (5%)

Affected Service: Payment Processing API
Time Detected: 2023-05-08 12:34:56 UTC
Impact: Users unable to complete payments
Metric: error_rate = 25%

Possible Causes:
- Database connectivity issues
- External payment gateway failures
- Recent deployment issues

Suggested Actions:
1. Check payment gateway status
2. Verify database connectivity
3. Review recent deployments

Runbook: https://runbooks.bayat.io/payment-service/high-error-rate
Dashboard: https://dashboards.bayat.io/payment-service

Alert Rules

Design effective alert rules:

Alert on symptoms, not causes
Avoid alert fatigue through proper thresholds
Use dynamic thresholds where appropriate
Implement alert grouping and correlation
Include buffer periods for transient issues
Document the rationale for each alert

Alert Distribution

Define clear alert routing:

Route alerts to the responsible team
Use escalation policies for unacknowledged alerts
Maintain up-to-date on-call schedules
Provide multiple notification channels
Document notification preferences

Dashboards and Visualization

Dashboard Standards

Create consistent dashboards:

Use a standard template for each service type
Include service overview and drill-down views
Display SLIs prominently
Show correlation between metrics
Provide links to related resources and documentation
Include time range selectors
Use consistent color schemes and layouts

Standard Dashboards

Implement these standard dashboards for all services:

Service Overview: Key health and performance metrics
Resource Utilization: CPU, memory, disk, network
SLI/SLO Dashboard: Service level indicators and objectives
Dependencies: Health of dependencies and integration points
Business Metrics: User activity and business outcomes

Visualization Best Practices

Follow these visualization guidelines:

Use appropriate chart types for each metric
Provide context with thresholds and historical trends
Label axes and include units
Use consistent time windows
Include legends and explanations
Avoid visual clutter
Optimize for readability at a glance

Tool Selection

Approved Monitoring Tools

Standardize on these monitoring tools:

Category	Approved Tools	Purpose
Metrics	Prometheus, Datadog, CloudWatch	Metric collection and analysis
Logging	ELK Stack, Loki, CloudWatch Logs	Log aggregation and search
Tracing	Jaeger, Zipkin, X-Ray	Distributed tracing
Dashboards	Grafana, Datadog, CloudWatch	Visualization
Alerting	AlertManager, PagerDuty, OpsGenie	Alert management
Synthetic	Pingdom, Datadog Synthetics	Synthetic monitoring
APM	New Relic, Datadog APM, Dynatrace	Application performance monitoring
Real User	Google Analytics, Datadog RUM	Real user monitoring

Tool Selection Criteria

Choose monitoring tools based on:

Integration: Compatibility with existing systems
Scalability: Ability to handle expected load
Functionality: Coverage of required capabilities
Usability: Ease of use for all stakeholders
Cost: Total cost of ownership
Support: Vendor support and community
Security: Security features and compliance

Monitoring as Code

Infrastructure Definition

Define monitoring as code:

Store monitoring definitions in version control
Use infrastructure as code tools to deploy monitoring
Apply the same review and approval process as application code
Test monitoring changes before deployment
Document monitoring code comprehensively

Example Terraform Configuration

# Define CloudWatch alarm for API Gateway
resource "aws_cloudwatch_metric_alarm" "api_5xx_error" {
  alarm_name          = "${var.service_name}-5xx-errors"
  comparison_operator = "GreaterThanThreshold"
  evaluation_periods  = 2
  metric_name         = "5XXError"
  namespace           = "AWS/ApiGateway"
  period              = 60
  statistic           = "Sum"
  threshold           = 5
  alarm_description   = "This alarm monitors for 5XX errors in the API"
  
  dimensions = {
    ApiName = var.api_name
    Stage   = var.environment
  }
  
  alarm_actions = [aws_sns_topic.alarm_topic.arn]
  ok_actions    = [aws_sns_topic.alarm_topic.arn]
  
  tags = local.common_tags
}

Example Prometheus Configuration

# prometheus.yml
scrape_configs:
  - job_name: 'api_service'
    scrape_interval: 15s
    metrics_path: '/metrics'
    static_configs:
      - targets: ['api-service:8080']
    relabel_configs:
      - source_labels: [__address__]
        target_label: instance
      - source_labels: [job]
        target_label: service

# alerts.yml
groups:
- name: api_alerts
  rules:
  - alert: HighErrorRate
    expr: rate(http_requests_total{status=~"5.."}[5m]) / rate(http_requests_total[5m]) > 0.05
    for: 2m
    labels:
      severity: critical
      team: api
    annotations:
      summary: "High HTTP error rate on {{ $labels.instance }}"
      description: "Error rate is {{ $value | humanizePercentage }} for the past 5 minutes"
      runbook: "https://runbooks.bayat.io/api/high-error-rate"

Health Checks

Health Check Types

Implement multiple health check types:

Liveness: Confirms the service is running
Readiness: Confirms the service can handle requests
Dependency: Confirms dependencies are available
Functional: Confirms business functions work correctly
Synthetic: Simulates user interactions

Health Check Implementation

Standardize health check implementation:

Expose health check endpoints for all services
Use standard HTTP status codes (200 for healthy, non-200 for unhealthy)
Include detailed health information in response body
Set appropriate timeouts and intervals
Document health check endpoints and expected responses

Example health check response:

{
  "status": "healthy",
  "version": "1.2.3",
  "timestamp": "2023-05-08T12:34:56.789Z",
  "dependencies": {
    "database": {
      "status": "healthy",
      "responseTime": 15
    },
    "paymentGateway": {
      "status": "healthy",
      "responseTime": 120
    },
    "notificationService": {
      "status": "degraded",
      "responseTime": 450,
      "message": "High latency detected"
    }
  }
}

Synthetic Monitoring

Implement synthetic monitoring for critical paths:

Define key user journeys to monitor
Run synthetic tests at regular intervals
Monitor from multiple geographic locations
Alert on synthetic test failures
Include detailed failure information

SLIs, SLOs, and SLAs

Service Level Indicators (SLIs)

Define standard SLIs for all services:

Availability: Percentage of successful requests
Latency: Request duration at various percentiles
Error Rate: Percentage of error responses
Throughput: Requests per second
Saturation: Resource utilization relative to capacity

Service Level Objectives (SLOs)

Set clear SLOs for each SLI:

Define measurement windows (e.g., 30 days)
Set appropriate targets (e.g., 99.9% availability)
Allocate error budgets
Document SLO decision rationale
Review and adjust SLOs regularly

Example SLO definition:

Service: Payment API
SLI: Availability
Definition: Percentage of requests that return a valid response (non-5xx)
Measurement: 30-day rolling window
Target: 99.95%
Error Budget: 0.05% (21.9 minutes per 30 days)

Service Level Agreements (SLAs)

Structure SLAs based on SLOs:

Set SLA targets below SLO targets (buffer)
Define clear measurement methodologies
Specify exclusions and limitations
Include remediation and reporting requirements
Document business impact of violations

On-Call and Incident Response

On-Call Rotations

Establish clear on-call practices:

Define primary and secondary on-call roles
Create fair and sustainable rotation schedules
Document escalation paths
Provide clear handoff procedures
Ensure knowledge sharing across the team

Incident Classification

Classify incidents by severity:

Severity	Definition	Examples
Critical	Complete service outage with significant business impact	Payment system down, authentication failure for all users
Major	Partial service degradation affecting many users	Slow response times, subset of features unavailable
Minor	Limited impact affecting few users or non-critical features	Cosmetic issues, isolated errors for specific users

Incident Response Process

Define a clear incident response process:

Detection: Identify the incident through monitoring or reports
Classification: Determine severity and impact
Notification: Alert appropriate stakeholders
Mitigation: Implement immediate fix or workaround
Resolution: Fully resolve the underlying issue
Post-mortem: Analyze root cause and implement preventative measures

Incident Documentation

Document incidents thoroughly:

Incident summary and timeline
Impact assessment
Root cause analysis
Resolution steps
Preventative measures
Lessons learned

Capacity Planning

Capacity Metrics

Monitor these metrics for capacity planning:

Resource utilization trends
Growth rates for users and transactions
Seasonal patterns
Peak-to-average ratios
Resource headroom

Capacity Forecasting

Implement capacity forecasting:

Analyze historical usage patterns
Project future growth
Plan for peak events and seasonality
Document capacity requirements
Schedule regular capacity reviews

Scaling Guidelines

Define guidelines for capacity changes:

Triggers for horizontal scaling
Triggers for vertical scaling
Lead time for capacity changes
Approval process for significant changes
Documentation of scaling decisions

Security Monitoring

Security Metrics

Monitor these security-specific metrics:

Authentication failures
Authorization violations
Rate limit breaches
Unusual access patterns
Security scan results
Vulnerability counts

Security Alerting

Implement security-specific alerts:

Excessive authentication failures
Privilege escalation attempts
Configuration changes
Unusual network traffic
Data exfiltration patterns
Compliance violations

Compliance Monitoring

Address compliance requirements:

Define compliance-specific monitoring
Generate required compliance reports
Maintain audit trails for compliance activities
Verify monitoring coverage of compliance controls

Cost Monitoring

Cost Metrics

Track these cost-related metrics:

Resource costs by service
Cost per transaction
Unutilized resources
Cost anomalies
Cost trends

Cost Optimization

Monitor for cost optimization opportunities:

Overprovisioned resources
Idle resources
Inefficient architecture patterns
Unexpected cost increases
Resource usage outside business hours

Cost Allocation

Implement cost allocation monitoring:

Track costs by team, project, and environment
Set up budget alerts
Compare actual costs to budgets
Identify cost outliers
Report on cost efficiency metrics

Monitoring Governance

Monitoring Standards Compliance

Ensure compliance with monitoring standards:

Conduct regular monitoring coverage reviews
Verify SLO measurement and reporting
Audit alert configurations
Check dashboard accuracy and completeness
Validate log coverage and retention

Monitoring as a Service

Provide monitoring as a service:

Create self-service monitoring tools
Document monitoring onboarding procedures
Provide monitoring templates and examples
Offer monitoring consultation for teams
Establish monitoring support channels

Continuous Improvement

Continuously improve monitoring practices:

Collect feedback on monitoring effectiveness
Analyze alert patterns and response times
Identify monitoring gaps
Stay current with monitoring technologies
Update standards based on lessons learned

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