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ISTC-CC Abstract
G-OLA: Generalized On-Line Aggregation for Interactive Analysis on Big Data
Proceedings of the 34th ACM SIGMOD International Conference on Management of Data (SIGMOD’15), May-June 2015. Demo paper.
Kai Zeng†, Sameer Agarwal‡, Ankur Dave†, Michael Armbrust‡, Ion Stoica†
†University of California, Berkeley
‡Databricks Inc.
Nearly 15 years ago, Hellerstein, Haas and Wang proposed online aggregation (OLA), a technique that allows users to (1) observe the progress of a query by showing iteratively refined approximate answers, and (2) 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 efficient delta maintenance techniques. We have implemented 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 100 machines processing roughly 10TB 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.
FULL PAPER: pdf
