Hardware-Conscious Stream Processing: A Survey

TL;DR

This survey reviews recent advances in hardware-conscious stream processing, emphasizing computation, I/O, and deployment optimizations to enhance real-time data analytics performance.

Contribution

It systematically summarizes recent research on integrating modern hardware capabilities into stream processing systems, highlighting key optimization strategies.

Findings

Identifies three main directions: computation, I/O, and query deployment.

Highlights the potential of hardware-aware optimizations for improved performance.

Suggests future research avenues in hardware-conscious stream processing.

Abstract

Data stream processing systems (DSPSs) enable users to express and run stream applications to continuously process data streams. To achieve real-time data analytics, recent researches keep focusing on optimizing the system latency and throughput. Witnessing the recent great achievements in the computer architecture community, researchers and practitioners have investigated the potential of adoption hardware-conscious stream processing by better utilizing modern hardware capacity in DSPSs. In this paper, we conduct a systematic survey of recent work in the field, particularly along with the following three directions: 1) computation optimization, 2) stream I/O optimization, and 3) query deployment. Finally, we advise on potential future research directions.