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.

Transactions can simplify distributed applications by hiding data distribution, concurrency, and failures from the application developer. Ideally the developer would see the abstraction of a single large machine that runs transactions sequentially and never fails. This requires the transactional subsystem to provide opacity (strict serializability for both committed and aborted transactions), as well as transparent fault tolerance with high availability. As even the best abstractions are unlikely to be used if they perform poorly, the system must also provide high performance. Existing distributed transactional designs either weaken this abstraction or are not designed for the best performance within a data center. This paper extends the design of FaRM --- which provides strict serializability only for committed transactions --- to provide opacity while maintaining FaRM's high throughput, low latency, and high availability within a modern data center. It uses timestamp ordering based on real time with clocks synchronized to within tens of microseconds across a cluster, and a failover protocol to ensure correctness across clock master failures. FaRM with opacity can commit 5.4 million neworder transactions per second when running the TPC-C transaction mix on 90 machines with 3-way replication.

Transactions with strong consistency and high availability simplify building and reasoning about distributed systems. However, previous implementations performed poorly. This forced system designers to avoid transactions completely, to weaken consistency guarantees, or to provide single-machine transactions that require programmers to partition their data. In this paper, we show that there is no need to compromise in modern data centers. We show that a main memory distributed computing platform called FaRM can provide distributed transactions with strict serializability, high performance, durability, and high availability. FaRM achieves a peak throughput of 140 million TATP transactions per second on 90 machines with a 4.9 TB database, and it recovers from a failure in less than 50 ms. Key to achieving these results was the design of new transaction, replication, and recovery protocols from first principles to leverage commodity networks with RDMA and a new, inexpensive approach to providing non-volatile DRAM.

We address the practical problem of automating the process of translating figures from mathematics, science, and engineering textbooks to a tactile form suitable for blind students. The Tactile Graphics Assistant (TGA) and accompanying workflow is described. Components of the TGA that identify text and replace it with Braille use machine learning, computational geometry, and optimization algorithms. We followed through with the ideas in our 2005 paper by creating a more detailed workflow, translating actual images, and analyzing the translation time. Our experience in translating more than 2,300 figures from 4 textbooks demonstrates that figures can be translated in ten minutes or less of human time on average. We describe our experience with training tactile graphics specialists to use the new TGA technology.

Access to graphical images (bar charts, diagrams, line graphs, etc.) that are in a tactile form (representation through which content can be accessed by touch) is inadequate for students who are blind and take mathematics, science, and engineering courses. We describe our analysis of the current work practices of tactile graphics specialists who create tactile forms of graphical images. We propose automated means by which to improve the efficiency of current work practices.We describe the implementation of various components of this new automated process, which includes image classification, segmentation, simplification, and layout. We summarize our development of the tactile graphics assistant, which will enable tactile graphics specialists to be more efficient in creating tactile graphics both in batches and individually. We describe our unique team of researchers, practitioners, and student consultants who are blind, all of whom are needed to successfully develop this new way of translating tactile graphics.