# BriskStream: Scaling Data Stream Processing on Shared-Memory Multicore Architectures

arXiv: https://arxiv.org/abs/1904.03604 · HTML: https://mlsanity.com/paper/1904.03604/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2019.

## Built on by (most cited first, 2 of 2)

- [The Metaverse Data Deluge: What Can We Do About It?](https://mlsanity.com/paper/2206.10326/) (2022)
- [Harnessing Scalable Transactional Stream Processing for Managing Large Language Models [Vision]](https://mlsanity.com/paper/2307.08225/) (2023)

## Abstract (arXiv)

We introduce BriskStream, an in-memory data stream processing system (DSPSs) specifically designed for modern shared-memory multicore architectures. BriskStream's key contribution is an execution plan optimization paradigm, namely RLAS, which takes relative-location (i.e., NUMA distance) of each pair of producer-consumer operators into consideration. We propose a branch and bound based approach with three heuristics to resolve the resulting nontrivial optimization problem. The experimental evaluations demonstrate that BriskStream yields much higher throughput and better scalability than existing DSPSs on multi-core architectures when processing different types of workloads.