Whiz: Data-Driven Analytics Execution

Today's data analytics frameworks are computecentric, 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 Wifiz, 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 Wmz prototype on a 50-node cluster using batch, streaming, and graph analytics workloads show that it improves analytics completion times 1.3-2 x and cluster efficiency 1.4x compared to state-of-the-art.