High-Throughput Graph Abstraction at Netflix: Part I
Netflix's Graph Abstraction efficiently handles up to 10 million operations per second across 650 TB of graph datasets, supporting diverse business needs with low latency and high throughput, by leveraging existing data abstractions and caching strategies for optimal performance and consistency.
MAIN POINTS
- Netflix's Graph Abstraction supports OLAP and OLTP use cases, focusing on high throughput and low latency.
- It integrates with Netflix's data ecosystem, using Key-Value and TimeSeries abstractions for real-time and historical data views.
- The architecture employs Property Graph models, namespaces, and graph schemas for efficient data management.
- Caching strategies and consistency enforcement ensure high performance and data integrity across multiple regions.
TAKEAWAYS
- Graph Abstraction supports real-time distributed graphs, social graphs, and service topology for various Netflix applications.
- The system's architecture builds on existing data abstractions, enhancing performance without reinventing storage layers.
- Efficient caching and consistency strategies reduce write and read amplification, ensuring stable graph datastore performance.
- Future improvements include minimizing query fanout and enhancing developer experience with schema-aware APIs.