G-OLA

Kai Zeng, Sameer Agarwal, Ankur Dave, Michael Armbrust, Ion Stoica
2015 Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data - SIGMOD '15  
Nearly years ago, Hellerstein, Haas and Wang proposed online aggregation (OLA), a technique that allows users to ( ) observe the progress of a query by showing iteratively re ned approximate answers, and ( ) stop the query execution once its result achieves the desired accuracy. In this demonstration, we present G-OLA, a novel mini-batch execution model that generalizes OLA to support general OLAP queries with arbitrarily nested aggregates using e cient delta maintenance techniques. We have
more » ... emented G-OLA in FluoDB, a parallel online query execution framework that is built on top of the Spark cluster computing framework that can scale to massive data sets. We will demonstrate FluoDB on a cluster of machines processing roughly TB of real-world session logs from a video-sharing website. Using an ad optimization and an A/B testing based scenario, we will enable users to perform real-time data analysis via web-based query consoles and dashboards.
doi:10.1145/2723372.2735381 dblp:conf/sigmod/ZengADAS15 fatcat:a66fy635njbhzopnu6umkodlwu