Skip to main content

Design

System Architecture Overview​

HySDS (Hybrid-Cloud Science Data Processing System) uses a distributed architecture designed to support scalable science data processing across hybrid cloud environments. Below is the high-level architecture:

Core Components​

GRQ (Geo Region Query)​

The geospatial data management system that handles data discovery and cataloging.

Mozart​

Job management and orchestration system.

Verdi Worker Architecture​

Data Flow Architecture​

Auto-Scaling Architecture​

Deployment Options​

Basic Deployment​

High-Availability Deployment​

Security Architecture​

Key Design Considerations​

  1. Scalability

    • Horizontal scaling through auto-scaling groups
    • Distributed processing across multiple environments
    • Queue-based job distribution

  2. Reliability

    • Fault-tolerant job execution
    • Automatic job recovery
    • Redundant service deployment options

  3. Flexibility

    • Support for multiple cloud providers
    • Hybrid deployment capabilities
    • Pluggable architecture for different processing needs

  4. Security

    • Integration with various authentication systems
    • Network isolation through VPCs
    • Role-based access control

  5. Monitoring

    • Real-time metrics collection
    • Performance monitoring
    • Cost tracking and optimization

Deployment Best Practices​

  1. Resource Sizing

    • Right-size ElasticSearch clusters based on workload
    • Configure appropriate auto-scaling thresholds
    • Monitor and adjust queue depths

  2. Network Configuration

    • Ensure proper VPC setup
    • Configure security groups appropriately
    • Set up required VPN connections for hybrid deployments

  3. Storage Management

    • Implement data lifecycle policies
    • Configure appropriate storage classes
    • Monitor storage usage and costs

  4. Security Configuration

    • Follow principle of least privilege
    • Regular security updates
    • Audit logging and monitoring

  5. Performance Optimization

    • Cache frequently accessed data
    • Optimize job scheduling
    • Monitor and tune auto-scaling parameters