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.
@article{arxiv.1904.03604,
title = {BriskStream: Scaling Data Stream Processing on Shared-Memory Multicore Architectures},
author = {Shuhao Zhang and Jiong He and Amelie Chi Zhou and Bingsheng He},
journal= {arXiv preprint arXiv:1904.03604},
year = {2019}
}