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High Volume Data Management

This documentation covers best practices and procedures for managing high volumes of attendance data generated by biometric/time attendance devices.

System Requirements​

Hardware Requirements:​

  • Server: Minimum 8-core CPU, 32GB RAM, RAID 10 storage configuration
  • Storage: SSD storage recommended (minimum 500GB for 1,000+ employees)
  • Network: Gigabit Ethernet with dedicated VLAN for attendance devices

Software Requirements:​

  • Database: MySQL 8.0+ or Microsoft SQL Server 2019+
  • Recommended: Columnar database for analytics (e.g., ClickHouse, Amazon Redshift)
  • Middleware: Message queue system (RabbitMQ, Apache Kafka) for data ingestion

Data Architecture​

Optimal Database Schema:​

CREATE TABLE attendance_records ( record_id BIGINT PRIMARY KEY AUTO_INCREMENT, device_id VARCHAR(50) NOT NULL, employee_id VARCHAR(50) NOT NULL, timestamp DATETIME(6) NOT NULL, verification_mode TINYINT, -- 1=Fingerprint, 2=Face, 3=Card, etc. status TINYINT, -- 0=Invalid, 1=Valid, 2=Duplicate raw_data BLOB, -- Optional biometric template data INDEX idx_employee (employee_id), INDEX idx_timestamp (timestamp), INDEX idx_device (device_id) ) ENGINE=InnoDB PARTITION BY RANGE (YEAR(timestamp)*100 + MONTH(timestamp)) ( PARTITION p202301 VALUES LESS THAN (202302), PARTITION p202302 VALUES LESS THAN (202303), -- ... additional partitions );

Data Processing Pipeline​

  1. Ingestion Layer:

    • Device Database
    • Batch processing for offline device synchronization
    • Real-time stream processing for live devices

  2. Processing Workflow:

Processing Workflow

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Performance Optimization​

Database Optimization​

  • Implement monthly partitioning for attendance tables
  • Use columnar storage for analytics queries
  • Configure appropriate indexes (avoid over-indexing)
  • Implement database sharding for multi-location deployments

Application Optimization​

  • Implement data compression for network transmission
  • Use connection pooling for database access
  • Cache frequently accessed data (employee lists, device configs)
  • Implement read replicas for reporting queries

Backup Strategy​

  1. Full Backups: Weekly compressed backups to offsite location
  2. Incremental Backups: Daily differential backups
  3. Disaster Recovery: Hot standby in alternate location with 15-minute RPO

Monitoring and Maintenance​

Key Metrics to Monitor​

  • Records processed per minute
  • Database query latency
  • Storage growth rate
  • Device synchronization success rate

Maintenance Tasks​

  • Monthly index reorganization
  • Quarterly partition maintenance
  • Annual storage capacity planning
  • Regular performance tuning

Scaling Considerations​

Vertical Scaling​

  • Upgrade server resources (CPU, RAM, storage)
  • Implement faster storage (NVMe SSDs)

Horizontal Scaling​

  • Database read replicas
  • Microservices architecture for processing
  • Distributed file storage for biometric data