{"version":"1.0","type":"rich","provider_name":"Acast","provider_url":"https://acast.com","height":250,"width":700,"html":"<iframe src=\"https://embed.acast.com/$/629a6154b4e1e70012764c00/638fd14e96d1480011bd3ab6?\" frameBorder=\"0\" width=\"700\" height=\"250\"></iframe>","title":"Audrey Cheng | TAOBench: An End-to-End Benchmark for Social Network Workloads | #15","thumbnail_width":200,"thumbnail_height":200,"thumbnail_url":"https://open-images.acast.com/shows/629a6154b4e1e70012764c00/1670370062198-764b10aa73aa28c56ed7220a70593eb1.jpeg?height=200","description":"<p><strong>Summary: </strong>This episode features Audrey Cheng talking about TAOBench, a new benchmark that captures the social graph workload at Meta. Audrey tells us about the features of workload, how it compares with other benchmarks, and how it fills a gap in the existing space of benchmark. Also, we hear all about the fantastic real-world impact the benchmark has already had across a range of companies. </p><p><br></p><p><strong>Links:</strong></p><ul><li><a href=\"https://www.vldb.org/pvldb/vol15/p1965-cheng.pdf\" rel=\"noopener noreferrer\" target=\"_blank\">Paper</a></li><li><a href=\"https://audreyccheng.com/\" rel=\"noopener noreferrer\" target=\"_blank\">Personal website</a></li><li><a href=\"https://engineering.fb.com/2022/09/07/open-source/taobench/\" rel=\"noopener noreferrer\" target=\"_blank\">Meta blog post</a></li><li><a href=\"https://github.com/audreyccheng/taobench\" rel=\"noopener noreferrer\" target=\"_blank\">GitHub repo</a></li></ul>","author_name":"Jack Waudby"}