DQBarge: Improving Data-Quality Tradeoffs in Large-Scale Internet Services

OSDI 2016


Modern Internet services often involve hundreds of distinct software components cooperating to handle a single user request. Each component must balance the competing goals of minimizing service response time and maximizing the quality of the service provided. This leads to low-level components making data-quality tradeoffs, which we define to be explicit decisions to return lower fidelity data in order to improve response time or minimize resource usage.

We first perform a comprehensive study of low-level data-quality tradeoffs at Facebook. We find that such tradeoffs are widespread. We also find that existing data-quality tradeoffs are often suboptimal because the low-level components making the tradeoffs lack global knowledge that could enable better decisions. Finally, we find that most tradeoffs are reactive, rather than proactive, and so waste resources and fail to mitigate system overload.

Next, we develop DQBarge, a system that enables better data-quality tradeoffs by propagating critical information along the causal path of request processing. This information includes data provenance, load metrics, and critical path predictions. DQBarge generates performance and quality models that help low-level components make better, more proactive, tradeoffs. Our evaluation shows that DQBarge helps Internet services mitigate load spikes, improve utilization of spare resources, and implement dynamic capacity planning.

Latest Publications

A Practical Stereo Depth System for Smart Glasses

Jialiang Wang, Daniel Scharstein, Akash Bapat, Kevin Blackburn-Matzen Matthew Yu, Jonathan Lehman, Suhib Alsisan, Yanghan Wang, Sam Tsai, Jan-Michael Frahm, Zijian He, Peter Vajda, Michael Cohen, Matt Uyttendaele

CVPR - 2023

Presto: A Decade of SQL Analytics at Meta

Yutian James Sun, Tim Meehan, Rebecca Schlussel, Wenlei Xie, Masha Basmanova, Orri Erling, Andrii Rosa, Shixuan Fan, Rongrong Zhong, Arun Thirupathi, Nikhil Collooru, Ke Wang, Sameer Agarwal, Arjun Gupta, Dionysios Logothetis, Kostas Xirogiannopoulos, Bin Fan, Amit Dutta, Varun Gajjala, Rohit Jain, Ajay Palakuzhy, Prithvi Pandian, Sergey Pershin, Abhisek Saikia, Pranjal Shankhdhar, Neerad Somanchi, Swapnil Tailor, Jialiang Tan, Sreeni Viswanadha, Zac Wen, Deepak Majeti, Aditi Pandit, Biswapesh Chattopadhyay

SIGMOD - 2023