A1 is an in-memory distributed database used by the Bing search engine to support complex queries over structured data. The key enablers for A1 are availability of cheap DRAM and high speed RDMA (Remote Direct Memory Access) networking in commodity hardware. A1 uses FaRM [11,12] as its underlying storage layer and builds the graph abstraction and query engine on top. The combination of in-memory storage and RDMA access requires rethinking how data is allocated, organized and queried in a large distributed system. A single A1 cluster can store tens of billions of vertices and edges and support a throughput of 350+ million of vertex reads per second with end to end query latency in single digit milliseconds. In this paper we describe the A1 data model, RDMA optimized data structures and query execution.

Maguro is a system for efficiently searching very large collections of text content of up to 1 trillion documents at low cost. Search engines span across content that is very dynamic and highly augmented with metadata to the tail content of the web. A long tail distribution of content calls for different trade-offs in the design space for good efficiency across the entire index range. Maguro is designed for the long tail of content with less dynamics and less metadata, but very good cost efficiency. Maguro is part of the serving stack in Bing and allows us to scale the index significantly better.