RedTAO: A Trillion-edge High-throughput Graph Store

With the explosive growth of daily active users, the social graph data of Xiaohongshu has scaled to trillions of edges, imposing high pressure on our storage system. Current state-of-the-art systems struggle to address the issue, primarily due to: (1) Traditional relational databases as the back-end storage require frequent scaling, incurring high cost and stability risks. (2) Most graph databases focus on complex multi-hop queries. Redundant components in these systems make them difficult to take advantage when processing our workloads dominated by one-hop queries. (3) Using cache systems like Redis or Memcache often struggles to ensure consistency between the cache and storage. In this paper, we propose RedTAO, which has a scalable and efficient graph cache layer optimized for social scenarios. Over 90.7% of queries are served directly by the cache, enabling us to focus on scaling it as traffic increases. RedTAO employs cross-cloud, multi-active deployment, synchronizing replicas through the storage layer. The cache layer directly accesses local storage, avoiding costly cross-region requests. Additionally, the data transmission service (DTS) component asynchronously corrects cache data, ensuring cache consistency. RedTAO has been successfully deployed in Xiaohongshu, achieving a 1.8× throughput improvement and at least 21.3% reduction in resource usage compared to the previously used MySQL architecture.