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Netflix details gRPC execution API for querying its Real-Time Distributed Graph
Key points
- Netflix details the serving layer for its Real-Time Distributed Graph (RDG).
- The serving layer enables efficient querying of a billion-edge graph.
- The goal is sub-100ms response times for diverse workloads.
- The system supports various access patterns, from security lookups to personalization traces.
Introduction to the Real-Time Distributed Graph (RDG)
Netflix has released the third installment of a blog series outlining the development of its Real-Time Distributed Graph (RDG). Previous parts covered the motivation for the RDG and its data processing pipeline, as well as the design of its storage layer, which handles billions of nodes and edges with single-digit-millisecond latency.
Efficient Graph Querying
This latest entry focuses on the serving layer, which is crucial for efficiently querying the graph to provide real-time insights for internal partners. The primary challenge is to enable complex queries and deliver quick responses from a dynamic, large-scale graph. The serving layer must turn a constantly evolving, billion-edge graph into sub-100ms responses across a wide variety of workloads.
Addressing Diverse Query Needs
Netflix recognized that querying the graph is not a uniform operation, requiring the system to handle a broad spectrum of access patterns. These patterns range from high-volume security lookups to deep, exploratory personalization traces. The RDG supports a richer graph structure, including multiple profiles per account, to track member journeys across devices and content.
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Reporting from
Netflix published the third part of a series explaining how it built its Real-Time Distributed Graph (RDG), focusing on the design of a fast and flexible serving layer for efficient graph querying. This part describes how the company addresses the challenge of querying a constantly evolving, billion-edge graph to achieve sub-100ms responses across various workloads.