Publication

Unicorn: A System for Searching the Social Graph

International Conference on Very Large Data Bases (VLDB)


Abstract

Unicorn is an online, in-memory social graph-aware indexing system designed to search trillions of edges between tens of billions of users and entities on thousands of commodity servers. Unicorn is based on standard concepts in information retrieval, but it includes features to promote results with good social proximity. It also supports queries that require multiple round-trips to leaves in order to retrieve objects that are more than one edge away from source nodes.

Unicorn is designed to answer billions of queries per day at latencies in the hundreds of milliseconds, and it serves as an infrastructural building block for Facebook’s Graph Search product. In this paper, we describe the data model and query language supported by Unicorn. We also describe its evolution as it became the primary backend for Facebook’s search offerings.

Related Publications

All Publications

IMC - October 21, 2019

Internet Performance from Facebook’s Edge

Brandon Schlinker, Italo Cunha, Yi-Ching Chiu, Srikanth Sundaresan, Ethan Katz-Bassett

CC - March 3, 2021

Lightning BOLT: Powerful, Fast, and Scalable Binary Optimization

Maksim Panchenko, Rafael Auler, Laith Sakka, Guilherme Ottoni

USENIX FAST - February 23, 2021

Facebook’s Tectonic Filesystem: Efficiency from Exascale

Satadru Pan, Theano Stavrinos, Yunqiao Zhang, Atul Sikaria, Pavel Zakharov, Abhinav Sharma, Shiva Shankar, Mike Shuey, Richard Wareing, Monika Gangapuram, Guanglei Cao, Christian Preseau, Pratap Singh, Kestutis Patiejunas, JR Tipton, Ethan Katz-Bassett, Wyatt Lloyd

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy