On-off sketch

Abstract

Approximate stream processing has attracted much attention recently. Prior art mostly focuses on characteristics like frequency, cardinality, and quantile. Persistence, as a new characteristic, is getting increasing attention. Unlike frequency, persistence highlights behaviors where an item appears recurrently in many time windows of a data stream. There are two typical problems with persistence - persistence estimation and finding persistent items. In this paper, we propose the On-Off sketch to address both problems. For persistence estimation, using the characteristic that the persistence of an item is increased periodically, we compress increments when multiple items are mapped to the same counter, which significantly reduces the error. Compared with the Count-Min sketch, 1) in theory, we prove that the error of the On-Off sketch is always smaller; 2) in experiments, the On-Off sketch achieves around 6.17 times smaller error and 2.2 times higher throughput. For finding persistent items, we propose a technique to separate persistent and non-persistent items, further improving the accuracy. We show that the space complexity of our On-Off sketch is much better than the state-of-the-art (PIE), and it reduces the error up to 4 orders of magnitude and achieves 2.84 times higher throughput than prior algorithms in experiments.