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System Design Question

Design a System for Large-Scale Graph Processing

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Functional Requirements

  1. Graph Storage and Retrieval

    • Ability to store large-scale graphs efficiently.
    • Support for retrieval of nodes and edges based on various criteria.
    • Capability to handle dynamic updates to the graph, including adding and removing nodes and edges.
  2. Graph Querying

    • Support for complex graph queries, such as shortest path, connectivity, and subgraph matching.
    • Ability to execute queries in parallel to improve performance.
  3. Graph Analytics

    • Provide built-in analytics functions like PageRank, community detection, and centrality measures.
    • Support for custom analytics algorithms.
  4. Scalability

    • System should scale horizontally to accommodate growing data sizes and increased query loads.
  5. Data Ingestion

    • Efficient data ingestion pipelines to handle streaming and batch data inputs.
  6. Access Control

    • Implement role-based access control to ensure data security and privacy.
  7. Integration

    • Provide APIs for integration with other systems and data sources.
  8. Monitoring and Logging

    • Real-time monitoring of system performance and logging of operations for auditing and debugging.

Non-Functional Requirements

  1. Performance

    • Low latency for query execution and data retrieval.
    • High throughput for data ingestion and processing.
  2. Reliability

    • Ensure high availability with minimal downtime.
    • Implement fault-tolerant mechanisms to handle node failures.
  3. Consistency

    • Ensure data consistency across distributed nodes.
    • Support eventual consistency for certain operations to improve performance.
  4. Scalability

    • System should support scaling to petabytes of data and millions of queries per second.
  5. Security

    • Implement encryption for data at rest and in transit.
    • Regular security audits and vulnerability assessments.
  6. Flexibility

    • Support for various graph models (e.g., directed, undirected, weighted).
    • Ability to adapt to different use cases and industries.
  7. Cost Efficiency

    • Optimize resource usage to minimize operational costs.
    • Support for cloud-based deployment to leverage cost-effective infrastructure.
  8. Interoperability

    • Ensure compatibility with existing data formats and protocols.
    • Support for data import/export in standard formats like CSV, JSON, and XML.

System Design Diagrams

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