Parallelizing Windowed Stream Joins in a Shared-Nothing Cluster

TL;DR

This paper presents a framework for parallelizing sliding window stream joins in shared-nothing clusters, addressing scalability and overhead issues, with experimental validation demonstrating its effectiveness.

Contribution

It introduces a novel framework for parallelizing sliding window stream joins in shared-nothing clusters, considering communication patterns and overheads.

Findings

Effective load distribution over cluster nodes

Scalability with increasing nodes

Improved join processing performance

Abstract

The availability of large number of processing nodes in a parallel and distributed computing environment enables sophisticated real time processing over high speed data streams, as required by many emerging applications. Sliding window stream joins are among the most important operators in a stream processing system. In this paper, we consider the issue of parallelizing a sliding window stream join operator over a shared nothing cluster. We propose a framework, based on fixed or predefined communication pattern, to distribute the join processing loads over the shared-nothing cluster. We consider various overheads while scaling over a large number of nodes, and propose solution methodologies to cope with the issues. We implement the algorithm over a cluster using a message passing system, and present the experimental results showing the effectiveness of the join processing algorithm.