Monitoring

Overview

The Gustaffo Reservations Application employs a comprehensive monitoring strategy to ensure system health, performance, and availability. This document outlines the monitoring approach, tools, and procedures.

Monitoring Architecture

flowchart TB
    subgraph "Application"
        API[API Services]
        Workers[Background Workers]
        DB[Database]
        Cache[Cache]
        Queue[Message Queue]
    end

    subgraph "Monitoring Infrastructure"
        Prometheus[Prometheus]
        Grafana[Grafana Dashboards]
        AlertManager[Alert Manager]
        Loki[Loki Log Aggregation]
        Tempo[Tempo Tracing]
    end

    subgraph "Notification Channels"
        Email[Email]
        Slack[Slack]
        PagerDuty[PagerDuty]
        SMS[SMS]
    end

    API --> Prometheus
    Workers --> Prometheus
    DB --> Prometheus
    Cache --> Prometheus
    Queue --> Prometheus

    API --> Loki
    Workers --> Loki
    DB --> Loki

    API --> Tempo
    Workers --> Tempo

    Prometheus --> Grafana
    Loki --> Grafana
    Tempo --> Grafana

    Prometheus --> AlertManager
    AlertManager --> Email
    AlertManager --> Slack
    AlertManager --> PagerDuty
    AlertManager --> SMS

Monitoring Components

Metrics Collection

Application Metrics

• Request Metrics: Request count, latency, error rate
• Resource Usage: CPU, memory, disk I/O, network I/O
• Business Metrics: Reservations created, payments processed
• Cache Metrics: Hit rate, miss rate, eviction rate
• Queue Metrics: Queue depth, processing time, failure rate

Infrastructure Metrics

• Node Metrics: CPU, memory, disk space, network
• Kubernetes Metrics: Pod status, deployment status, resource utilization
• Database Metrics: Query performance, connection count, replication lag
• Load Balancer Metrics: Request count, error rate, backend health

Log Management

• Application Logs: Structured JSON logs from all services
• System Logs: Kernel and service logs
• Access Logs: Web server and API gateway logs
• Audit Logs: Security-relevant events and actions

Tracing

• Distributed Tracing: End-to-end request tracing across services
• Span Collection: Detailed timing for service operations
• Dependency Mapping: Service dependency visualization

Alerting

• Alert Definitions: Predefined alert thresholds and conditions
• Alert Routing: Routing rules based on alert severity and type
• Alert Aggregation: Grouping related alerts to reduce noise
• Escalation Policies: Tiered escalation for unacknowledged alerts

Monitoring Tools

Core Monitoring Stack

• Prometheus: Metrics collection and storage
• Grafana: Visualization and dashboards
• Loki: Log aggregation and querying
• Tempo: Distributed tracing
• Alert Manager: Alert routing and notification

Additional Tools

• Node Exporter: Host-level metrics collection
• Kubernetes Metrics Server: Kubernetes metrics collection
• Prometheus Operator: Kubernetes-native Prometheus management
• Blackbox Exporter: External endpoint monitoring
• Fluentd: Log collection and forwarding

Dashboards

System Dashboards

• System Overview: High-level system health and performance
• Kubernetes Cluster: Cluster status and resource utilization
• Database Performance: Query metrics and database health
• Service Health: Individual service status and performance

Business Dashboards

• Reservation Metrics: Reservation creation and cancellation rates
• Payment Processing: Payment success rate and volume
• User Activity: User login and activity metrics
• Revenue Tracking: Revenue by property, channel, and time period

Alert Configuration

Severity Levels

• Critical: Immediate action required, service outage or data loss risk
• High: Urgent action required, degraded service or performance
• Medium: Action required during business hours, non-critical issues
• Low: Informational, may require investigation

Alert Categories

• Availability Alerts: Service or endpoint availability issues
• Performance Alerts: Latency or throughput degradation
• Resource Alerts: Resource utilization thresholds exceeded
• Error Rate Alerts: Elevated error rates in services
• Business Alerts: Anomalies in business metrics

Health Checks

Endpoint Health Checks

• API Health: Periodic checks of API endpoints
• Service Health: Internal service health endpoints
• Database Health: Database connectivity and query execution
• Dependency Health: Checks for external service dependencies

Synthetic Monitoring

• User Journeys: Automated testing of critical user flows
• API Tests: Periodic execution of API test suites
• Performance Tests: Regular load testing of key components

Incident Response

Incident Detection

• Automated Detection: Alert-based incident creation
• Manual Detection: User-reported issues
• Proactive Detection: Trend analysis and anomaly detection

Incident Management

• Incident Classification: Severity and impact assessment
• Incident Assignment: Routing to appropriate teams
• Incident Communication: Status updates to stakeholders
• Incident Resolution: Troubleshooting and recovery actions

Post-Incident Analysis

• Root Cause Analysis: Identification of underlying causes
• Corrective Actions: Improvements to prevent recurrence
• Monitoring Enhancements: Updates to monitoring based on incidents

Capacity Planning

• Trend Analysis: Historical usage patterns and growth trends
• Predictive Scaling: Forecasting future resource requirements
• Seasonal Planning: Preparation for peak usage periods
• Resource Optimization: Identifying over-provisioned resources

Operational Procedures

Routine Monitoring

• Daily Health Checks: Regular review of system health
• Performance Reviews: Weekly analysis of performance metrics
• Capacity Reviews: Monthly evaluation of resource utilization

Alert Handling

• Alert Acknowledgement: Process for acknowledging alerts
• Investigation Procedures: Steps for investigating alert causes
• Escalation Paths: When and how to escalate unresolved issues

Reporting

• Daily Status Reports: Summary of system health and incidents
• Weekly Performance Reports: Detailed performance analysis
• Monthly Service Level Reports: SLA compliance and metrics

SLAs and SLOs

Service Level Indicators (SLIs)

• Availability: Percentage of successful health checks
• Latency: Request processing time
• Error Rate: Percentage of failed requests
• Throughput: Requests processed per second

Service Level Objectives (SLOs)

• API Availability: 99.9% availability
• Request Latency: 95% of requests processed within 500ms
• Error Rate: Less than 0.1% error rate
• Database Response Time: 99% of queries complete within 100ms