The continued emergence of large social network applications has introduced a scale of data and query volume that challenges the limits of existing data stores. However, few benchmarks accurately simulate these request patterns, leaving researchers in short supply of tools to evaluate and improve upon these systems. In this paper, we present a new benchmark, TAOBench, that captures the social graph workload at Meta. We open source workload configurations along with a benchmark that leverages these request features to both accurately model production workloads and generate emergent application behavior. We ensure the integrity of TAOBench's workloads by validating them against their production counterparts. We also describe several benchmark use cases at Meta and report results for five popular distributed database systems to demonstrate the benefits of using TAOBench to evaluate system tradeoffs as well as identify and address performance issues. Our benchmark fills a gap in the available tools and data that researchers and developers have to inform system design decisions.

Facebook's graph store TAO, like many other distributed data stores, traditionally prioritizes availability, efficiency, and scalability over strong consistency or isolation guarantees to serve its large, read-dominant workloads. As product developers build diverse applications on top of this system, they increasingly seek transactional semantics. However, providing advanced features for select applications while preserving the system's overall reliability and performance is a continual challenge. In this paper, we first characterize developer desires for transactions that have emerged over the years and describe the current failure-atomic (i.e., write) transactions offered by TAO. We then explore how to introduce an intuitive read transaction API. We highlight the need for atomic visibility guarantees in this API with a measurement study on potential anomalies that occur without stronger isolation for reads. Our analysis shows that 1 in 1,500 batched reads reflects partial transactional updates, which complicate the developer experience and lead to unexpected results. In response to our findings, we present the RAMP-TAO protocol, a variation based on the Read Atomic Multi-Partition (RAMP) protocols that can be feasibly deployed in production with minimal overhead while ensuring atomic visibility for a read-optimized workload at scale.

DEVise is a data exploration system that allows users to easily develop, browse, and share visual presentation of large tabular datasets (possibly containing or referencing multimedia objects) from several sources. The DEVise framework is being implemented in a tool that has been already successfully applied to a variety of real applications by a number of user groups.

DEVise is a data exploration system that allows users to easily develop, browse, and share visual presentations of large tabular datasets (possibly containing or referencing multimedia objects) from several sources. The DEVise framework, implemented in a tool that has been already successfully applied to a variety of real applications by a number of user groups, makes several contributions. In particular, it combines support for extended relational queries with powerful data visualization features. Datasets much larger than available main memory can be handled—DEVise is currently being used to visualize datasets well in excess of 100MB—and data can be interactively examined at several levels of detail: all the way from meta-data summarizing the entire dataset, to large subsets of the actual data, to individual data records. Combining querying (in general, data processing) with visualizations gives us a very versatile tool, and presents several novel challenges.