TAOBench: An End-to-End Benchmark for Social Network Workloads
Summary: TAOBench: end-to-end benchmark for social network workloads based on Meta production traffic, capturing realistic request patterns and emergent behavior. Open-source workload configurations, validated against production, evaluated on five distributed DBMS to reveal tradeoffs. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Audrey Cheng (University of California Berkeley)
- 2. Xiao Shi (Meta)
- 3. Aaron Kabcenell (Meta)
- 4. Shilpa Lawande (Meta)
- 5. Hamza Qadeer (University of California Berkeley)
- 6. Jason Chan (University of California Berkeley)
- 7. Harrison Tin (University of California Berkeley)
- 8. Ryan Zhao (University of California Berkeley)
- 9. Peter Bailis (Sisu Data)
- 10. Mahesh Balakrishnan (Meta)
- 11. Nathan Bronson (Rockset)
- 12. Natacha Crooks (University of California Berkeley)
- 13. Ion Stoica (University of California Berkeley)
BibTeX Citation
@article{cheng_vldb22,
title = {{TAOBench: An End-to-End Benchmark for Social Network Workloads}},
author = {Cheng, Audrey and Shi, Xiao and Kabcenell, Aaron and Lawande, Shilpa and Qadeer, Hamza and Chan, Jason and Tin, Harrison and Zhao, Ryan and Bailis, Peter and Balakrishnan, Mahesh and Bronson, Nathan and Crooks, Natacha and Stoica, Ion},
journal = {PVLDB},
series = {{VLDB} '22},
volume = {15},
number = {9},
pages = {1965--1977},
doi = {10.14778/3538598.3538616},
url = {https://doi.org/10.14778/3538598.3538616},
year = {2022}
}
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