论文：MWP: Multi-Window Parallel Evaluation of Regular Path Queries on Streaming Graphs
会议：SIGMOD 2024
作者：Siyuan Zhang, Zhenying He, Yinan Jing, Kai Zhang, and X. Sean Wang
简介：A persistent Regular Path Query (RPQ) on a streaming graph is to continuously find every pair of vertices that are connected by a path in the graph within a sliding window, such that the edge label sequence of this path matches a given regular expression. The existing RPQ evaluation algorithm in the literature incrementally maintains a set of spanning-tree-like data structures to quickly form query results and to avoid reprocessing edges that are shared by multiple sliding windows. This approach allows parallel processing of the graph edges within a sliding window but requires a blocking expiration phase between sliding windows to remove the old edges. This blocking phase can significantly degrade the query performance, especially when the edges arrive quickly and the sliding windows overlap significantly.
This paper presents a new RPQ evaluation strategy called Multi-Window Parallel (MWP) method leveraging a new data structure called Timestamped Rooted Digraph (TRD). The novel idea is to incrementally maintain TRDs for the quick formulation of query results, like the aforementioned spanning trees, but simultaneously contain needed information for multiple sliding windows. MWP eliminates the forced blocking expiration phase. Only when memory runs low, a quick “dirty garbage collection” (DGC) process is done to remove some unneeded edges and nodes on TRDs, without incurring large costs. Extensive experiments on real graph datasets show that MWP significantly outperforms the existing algorithm in terms of throughput, tail latency, and scalability, and that DGC provides an effective solution for releasing memory with minimum impact.
