Log Processing

Last Updated: May 20, 2025

This document provides an overview of log processing and aggregation in the Typus Development Framework.

Overview

The log processing system in Typus provides tools and methodologies for collecting, storing, analyzing, and managing logs from both frontend and backend sources. This centralized approach enables comprehensive monitoring, troubleshooting, and auditing across the entire application stack.

Log Collection Architecture

Log Storage

Logs are stored in two primary formats:

Database Storage

The system stores logs in a structured database format that captures:

• Log level (debug, info, warn, error)
• Timestamp information
• Source identification (frontend/backend)
• Component and module information
• Message content
• Structured metadata
• Context information (user, session, request details)
• Error details for error-level logs

File Storage

Log files are stored in the logs directory with separate files for different purposes:

• Combined logs (all levels)
• Error-specific logs
• JSON-formatted for machine processing

Log Retention and Cleanup

The system includes automated maintenance features to manage log growth:

Database Cleanup

• Configurable retention period (default: 30 days)
• Configurable maximum log count
• Scheduled cleanup jobs
• Prioritizes keeping error logs over debug/info logs

File Rotation

• Size-based rotation
• Date-based rotation
• Configurable retention periods
• Compression of older log files

Log Analysis

The system provides multiple approaches to analyze and extract insights from logs:

Analysis Capabilities

• Filter logs by various criteria (level, time range, user, component)
• Analyze performance metrics (response times, error rates)
• Track feature usage and user behavior
• Identify error patterns and root causes
• Generate reports and visualizations

Monitoring and Alerting

The log processing system supports real-time monitoring through:

1. Console output: Immediate visibility during development
2. Real-time dashboards: Visual monitoring of system health
3. Alert notifications: Configurable alerts for critical conditions

Alert types include:

• Critical error notifications
• Performance threshold alerts
• Security-related events
• Custom application-specific alerts

Integration with External Systems

The log processing system can integrate with external monitoring and analysis tools:

Compatible Systems

• ELK Stack: Elasticsearch, Logstash, Kibana
• Prometheus/Grafana: For metrics visualization
• Sentry: For error tracking
• Custom dashboards: Built on the Typus admin interface

Log Aggregation Across Services

For multi-service deployments, logs can be aggregated from multiple sources:

Aggregation Methods

1. Database consolidation: Centralized log database
2. Log shipping: Automated file transfer
3. Centralized log API: Direct logging to a central service

Performance Considerations

The log processing system is designed to minimize impact on application performance:

1. Efficient storage: Optimized for read/write operations
2. Asynchronous processing: Non-blocking log operations
3. Configurable verbosity: Control log volume through levels
4. Automatic management: Cleanup and optimization

Security and Compliance

Data Protection

1. PII handling: Automatic redaction of personally identifiable information
2. Encryption: Encrypted storage for sensitive logs
3. Access control: Role-based access to log data

Compliance Support

The log processing system supports compliance requirements:

1. Retention policies: Configurable to meet regulatory requirements
2. Audit trails: Immutable logs for security events
3. Data export: Tools for subject access requests

Best Practices

Log Processing Workflow

1. Collection: Gather logs from all sources
2. Filtering: Remove unnecessary noise
3. Enrichment: Add contextual information
4. Storage: Store in appropriate format
5. Analysis: Regular review and automated alerts
6. Archiving: Long-term storage of important logs
7. Cleanup: Regular purging of unnecessary data

Storage Optimization

1. Log levels: Use appropriate log levels to control volume
2. Sampling: Consider sampling for high-volume debug logs
3. Aggregation: Store aggregated metrics for long-term trends

Conclusion

The log processing system in Typus provides a comprehensive solution for managing application logs across the entire stack. By centralizing log collection, storage, and analysis, it enables effective monitoring, troubleshooting, and optimization of the application.