Design a System for Large-Scale Graph Processing

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.