Whiz: Data-Driven Analytics Execution


Robert Grandl, Google; Arjun Singhvi, University of Wisconsin–Madison; Raajay Viswanathan, Uber Technologies Inc.; Aditya Akella, University of Wisconsin–Madison


Today's data analytics frameworks are compute-centric, with analytics execution almost entirely dependent on the predetermined 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 execution framework that cleanly separates computation from intermediate data. This enables runtime visibility into intermediate data via programmable monitoring, and data-driven computation where data properties drive when/what computation runs. Experiments with a Whiz prototype on a 50-node cluster using batch, streaming, and graph analytics workloads show that it improves analytics completion times 1.3-2x and cluster efficiency 1.4x compared to state-of-the-art.

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@inproceedings {262040,
title = {Whiz: Data-Driven Analytics Execution},
booktitle = {18th {USENIX} Symposium on Networked Systems Design and Implementation ({NSDI} 21)},
year = {2021},
url = {https://www.usenix.org/conference/nsdi21/presentation/grandl},
publisher = {{USENIX} Association},
month = apr,
Grandl Paper (Prepublication) PDF