Optimal Random Sampling from Distributed Streams Revisited
Abstract
We give an improved algorithm for drawing a random sample from a large data stream when the input elements are distributed across multiple sites which communicate via a central coordinator. At any point in time the set of elements held by the coordinator represent a uniform random sample from the set of all the elements observed so far. When compared with prior work, our algorithms asymptotically improve the total number of messages sent in the system as well as the computation required of the coordinator. We also present a matching lower bound, showing that our protocol sends the optimal number of messages up to a constant factor with large probability. As a byproduct, we obtain an improved algorithm for finding the heavy hitters across multiple distributed sites.
Cite
@article{arxiv.1903.12065,
title = {Optimal Random Sampling from Distributed Streams Revisited},
author = {Srikanta Tirthapura and David P. Woodruff},
journal= {arXiv preprint arXiv:1903.12065},
year = {2019}
}
Comments
This writeup is a revised version of a paper with the same title and authors, which appeared in the Proceedings of the International Conference on Distributed Computing (DISC) 2011