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

TL;DR

BriskStream is a new in-memory data stream processing system optimized for shared-memory multicore architectures, using a novel RLAS execution plan approach to improve throughput and scalability.

Contribution

It introduces RLAS, a relative-location aware optimization paradigm, with a branch and bound approach, enhancing data stream processing performance on multicore systems.

Findings

Significantly higher throughput than existing DSPSs

Better scalability on multi-core architectures

Effective handling of various workload types

Abstract

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.