Netflix's Graph Abstraction is tailored for high-throughput applications, focusing mainly on OLTP use cases that require rapid processing of millions of operations per second. While OLAP scenarios emphasize deep analysis of large datasets, OLTP demands quick responses, often involving trade-offs like eventual consistency. Currently, Netflix's Graph Abstraction handles nearly 10 million operations per second across 650 TB of data, highlighting its efficiency. The architecture builds on existing Netflix data layers rather than creating new ones. It utilizes the Key-Value (KV) Abstraction for real-time indexing of nodes and edges, with options for TimeSeries (TS) data for historical analysis. Low-latency performance is achieved through EVCache, with ongoing experiments for further enhancements. The system’s namespaces allow for isolated data management, optimized based on user-defined needs like throughput and latency. The schema supporting this abstraction plays a critical role, ensuring data quality by rejecting non-conforming inputs and streamlining query planning. This setup helps avoid redundant processing and eliminates impossible traversal paths during queries. The architecture is not static; it updates in real-time to adapt to user changes and aims to improve developer experience by incorporating type-safe access layers and schema-aware APIs. Overall, the Graph Abstraction is a sophisticated solution that balances performance with operational demands in Netflix’s complex environment.