Today's data analytics frameworks are compute-centric, with analytics execution almost entirely dependent on the pre-determined physical structure of the high-level computation. Relegating intermediate data to a second class entity in this manner hurts flexibility, performance, and efficiency. We present Whiz, a new analytics framework that cleanly separates computation from intermediate data. It enables runtime visibility into data via programmable monitoring, and data-driven computation (where intermediate data values drive when/what computation runs) via an event abstraction. Experiments with a Whiz prototype on a large cluster using batch, streaming, and graph analytics workloads show that its performance is 1.3-2x better than state-of-the-art.
@article{arxiv.1703.10272,
title = {Whiz: A Fast and Flexible Data Analytics System},
author = {Robert Grandl and Arjun Singhvi and Raajay Viswanathan and Aditya Akella},
journal= {arXiv preprint arXiv:1703.10272},
year = {2019}
}