Lili Wu, Roshan Sumbaly, Chris Riccomini, Gordon Koo, Hyung Jin Kim, Jay Kreps, and Sam Shah

In the 38th International Conference on Very Large Databases (VLDB 2012)


 

Abstract

Multidimensional data generated by members on websites has seen massive growth in recent years. OLAP is a well-suited solution for mining and analyzing this data. Providing insights derived from this analysis has become crucial for these websites to give members greater value. For example, LinkedIn, the largest professional social network, provides its professional members rich analytics features like “Who’s Viewed My Profile?” and “Who’s Viewed This Job?” The data behind these features form cubes that must be efficiently served at scale, and can be neatly sharded to do so. To serve our growing 160 million member base, we built a scalable and fast OLAP serving system called Avatara to solve this many, small cubes problem. At LinkedIn, Avatara has been powering several analytics features on the site for the past two years.

BiBTeX

article{avatara, author = {Lili Wu and Roshan Sumbaly and Chris Riccomini and Gordon Koo and Hyung Jin Kim and Jay Kreps and Sam Shah}, title = {Avatara: OLAP for Web-scale Analytics Products}, journal = {PVLDB}, volume = {5}, number = {12}, year = {2012}, pages = {1874-1877}, url = {http://vldb.org/pvldb/vol5/p1874_liliwu_vldb2012.pdf}, }