QPOPSS: Query and Parallelism Optimized Space-Saving for Finding Frequent Stream Elements

TL;DR

QPOPSS is a parallel algorithm for efficiently identifying frequent stream elements, offering strong concurrency guarantees, high accuracy, and minimal memory use, suitable for demanding real-time data analytics.

Contribution

It introduces QPOPSS, a novel parallel space-saving algorithm with concurrency guarantees, optimized for high throughput and accuracy in streaming data environments.

Findings

Linear scalability in multi-threaded throughput

Highest accuracy among compared methods

Significantly reduced memory footprint

Abstract

The frequent elements problem, a key component in demanding stream-data analytics, involves selecting elements whose occurrence exceeds a user-specified threshold. Fast, memory-efficient $\epsilon$-approximate synopsis algorithms select all frequent elements but may overestimate them depending on $\epsilon$ (user-defined parameter). Evolving applications demand performance only achievable by parallelization. However, algorithmic guarantees concerning concurrent updates and queries have been overlooked. We propose Query and Parallelism Optimized Space-Saving (QPOPSS), providing concurrency guarantees. The design includes an implementation of the \emph{Space-Saving} algorithm supporting fast queries, implying minimal overlap with concurrent updates. QPOPSS integrates this with the distribution of work and fine-grained synchronization among threads, swiftly balancing high throughput, high accuracy, and low memory consumption. Our analysis, under various concurrency and data distribution conditions, shows space and approximation bounds. Our empirical evaluation relative to representative state-of-the-art methods reveals that QPOPSS's multi-threaded throughput scales linearly while maintaining the highest accuracy, with orders of magnitude smaller memory footprint.