The Bloom Clock

TL;DR

The bloom clock is a space-efficient, probabilistic data structure for determining event causality in distributed systems, offering high confidence with adjustable false positive rates and no false negatives.

Contribution

It introduces the bloom clock, a novel probabilistic data structure that reduces space complexity compared to vector clocks while reliably detecting causality and violations.

Findings

Reduces space complexity compared to vector clocks.

Guarantees no false negatives in causality detection.

Provides adjustable false positive rates for confidence levels.

Abstract

The bloom clock is a space-efficient, probabilistic data structure designed to determine the partial order of events in highly distributed systems. The bloom clock, like the vector clock, can autonomously detect causality violations by comparing its logical timestamps. Unlike the vector clock, the space complexity of the bloom clock does not depend on the number of nodes in a system. Instead it depends on a set of chosen parameters that determine its confidence interval, i.e. false positive rate. To reduce the space complexity from which the vector clock suffers, the bloom clock uses a 'moving window' in which the partial order of events can be inferred with high confidence. If two clocks are not comparable, the bloom clock can always deduce it, i.e. false negatives are not possible. If two clocks are comparable, the bloom clock can calculate the confidence of that statement, i.e. it can compute the false positive rate between comparable pairs of clocks. By choosing an acceptable threshold for the false positive rate, the bloom clock can properly compare the order of its timestamps, with that of other nodes in a highly accurate and space efficient way.