Application Monitoring

Application monitoring provides real-time visibility into the Posters.science platform's performance, health, and operational metrics. This ensures optimal user experience and system reliability.

Monitoring Stack

Primary Tools

• Prometheus: Metrics collection and storage
• Grafana: Visualization and dashboards
• Custom Metrics: Application-specific monitoring
• Health Checks: Automated system health verification

Key Metrics

Performance Metrics

• API Response Time: Endpoint latency measurement
• Throughput: Requests per second
• Error Rate: Failed request percentage
• Resource Utilization: CPU, memory, and disk usage

Search Performance

• Search Indexing Speed: Meilisearch performance
• Query Response Time: Search result delivery speed
• Index Size: Search index growth tracking
• Search Accuracy: Result relevance metrics

LLM Service Metrics

• Query Processing Time: AI response generation speed
• GPU Utilization: Compute resource usage
• Model Performance: Response quality metrics
• Queue Length: Request backlog monitoring

Data Processing

• Scraping Job Completion: Web scraping success rate
• Metadata Processing Time: Content analysis speed
• Database Query Performance: PostgreSQL optimization
• Cache Hit Rate: Redis caching effectiveness

Monitoring Implementation

Metrics Collection

// Example: API response time monitoring
app.use((req, res, next) => {
  const start = Date.now();

  res.on("finish", () => {
    const duration = Date.now() - start;
    prometheus
      .histogram("http_request_duration_seconds", {
        method: req.method,
        route: req.route?.path,
        status_code: res.statusCode,
      })
      .observe(duration / 1000);
  });

  next();
});

Health Check Endpoints

• System Health: Overall platform status
• Database Connectivity: PostgreSQL connection status
• Search Service: Meilisearch availability
• Cache Service: Redis connectivity

Custom Dashboards

• System Overview: High-level platform metrics
• Performance Trends: Historical performance data
• Error Analysis: Error pattern identification

Alerting System

Alert Configuration

• Threshold-Based: Metric threshold alerts
• Anomaly Detection: Unusual pattern alerts
• Composite Alerts: Multi-metric correlation
• Escalation Policies: Alert routing and escalation

Alert Channels

• Email Notifications: Critical issue alerts
• Slack Integration: Team communication

Alert Examples

• High Error Rate: API error rate > 5%
• Slow Response Time: Average response time > 2 seconds
• Resource Exhaustion: CPU usage > 80%
• Service Down: Health check failures
• Data Processing Delays: Job completion timeouts

Performance Optimization

Bottleneck Identification

• Database Queries: Slow query detection
• API Endpoints: Performance profiling
• Search Operations: Index optimization

Security Monitoring

Security Metrics

• Authentication Failures: Login attempt monitoring
• Rate Limiting: API abuse detection