Sampling-based Estimation of the Number of Distinct Values in Distributed Environment

TL;DR

This paper introduces a novel sketch-based distributed method for estimating the number of distinct values in large-scale data, significantly reducing communication costs while maintaining accuracy.

Contribution

A new distributed sampling-based NDV estimation method using sketches that minimizes communication costs and is compatible with existing estimators.

Findings

Reduces communication costs by orders of magnitude.

Achieves accurate NDV estimation with sub-linear communication.

Validated through extensive experiments.

Abstract

In data mining, estimating the number of distinct values (NDV) is a fundamental problem with various applications. Existing methods for estimating NDV can be broadly classified into two categories: i) scanning-based methods, which scan the entire data and maintain a sketch to approximate NDV; and ii) sampling-based methods, which estimate NDV using sampling data rather than accessing the entire data warehouse. Scanning-based methods achieve a lower approximation error at the cost of higher I/O and more time. Sampling-based estimation is preferable in applications with a large data volume and a permissible error restriction due to its higher scalability. However, while the sampling-based method is more effective on a single machine, it is less practical in a distributed environment with massive data volumes. For obtaining the final NDV estimators, the entire sample must be transferred throughout the distributed system, incurring a prohibitive communication cost when the sample rate is significant. This paper proposes a novel sketch-based distributed method that achieves sub-linear communication costs for distributed sampling-based NDV estimation under mild assumptions. Our method leverages a sketch-based algorithm to estimate the sample's {\em frequency of frequency} in the {\em distributed streaming model}, which is compatible with most classical sampling-based NDV estimators. Additionally, we provide theoretical evidence for our method's ability to minimize communication costs in the worst-case scenario. Extensive experiments show that our method saves orders of magnitude in communication costs compared to existing sampling- and sketch-based methods.