TAOBench: An End-to-End Benchmark for Social Networking Workloads - Cheng, Audrey; Shi, Xiao; Kabcenell, Aaron; Lawande, Shilpa; Qadeer, Hamza; Chan, Jason; Tin, Harrison; Zhao, Ryan; Bailis, Peter; Balakrishnan, Mahesh; Bronson, Nathan; Crooks, Natacha; et al. (VLDB 2022, 2022)
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.