Manousis, Antonis; Cheng, Zhuo; Ben Basat, Ran; Liu, Zaoxing; Sekar, Vyas; (2022) Enabling Efficient and General Subpopulation Analytics in Multidimensional Data Streams. In: Özcan, Fatma and Freire, Juliana and Lin, Xuemin, (eds.) Proceedings of the VLDB Endowment. (pp. pp. 3249-3262). Association for Computing Machinery (ACM): New York, NY, USA.

Preview

Text 3551793.3551867.pdf - Published Version Download (603kB) | Preview

Abstract

Today’s large-scale services (e.g., video streaming platforms, data centers, sensor grids) need diverse real-time summary statistics across multiple subpopulations of multidimensional datasets. However, state-of-the-art frameworks do not offer general and accurate analytics in real time at reasonable costs. The root cause is the combinatorial explosion of data subpopulations and the diversity of summary statistics we need to monitor simultaneously. We present Hydra, an efficient framework for multidimensional analytics that presents a novel combination of using a “sketch of sketches” to avoid the overhead of monitoring exponentially-many subpopulations and universal sketching to ensure accurate estimates for multiple statistics. We build Hydra as an Apache Spark plugin and address practical system challenges to minimize overheads at scale. Across multiple real-world and synthetic multidimensional datasets, we show that Hydra can achieve robust error bounds and is an order of magnitude more efficient in terms of operational cost and memory footprint than existing frameworks (e.g., Spark, Druid) while ensuring interactive estimation times.

Download activity - last month

Export as XMLCSVJSON

Download activity - last 12 months

Export as CSVJSONXML

Downloads by country - last 12 months

Export as XMLJSONCSV

Archive Staff Only

View Item