PETGraphDB: A Property Evolution Temporal Graph Data Management System

TL;DR

PETGraph is a specialized data management system designed for Property Evolution Temporal Graphs, offering efficient storage, high transaction throughput, and fast query processing tailored to IoT and dynamic property data.

Contribution

The paper introduces PETGraph, a novel system optimized for Property Evolution Temporal Graphs, with a new data model, space-efficient storage, and enhanced concurrency control.

Findings

Uses only 33% of storage space compared to existing solutions.

Achieves 58.8x higher transaction throughput in HTAP workloads.

Outperforms current solutions by 267x in query latency.

Abstract

Temporal graphs are graphs whose nodes and edges, together with their associated properties, continuously change over time. With the development of Internet of Things (IoT) systems, a subclass of the temporal graph, i.e., Property Evolution Temporal Graph, in which the value of properties on nodes or edges changes frequently while the graph's topology barely changes, is growing rapidly. However, existing temporal graph management solutions are not oriented to the Property Evolution Temporal Graph data, which leads to highly complex data modeling and low-performance query processing of temporal graph queries. To solve these problems, we developed PETGraph, a data management system for Property Evolution Temporal Graph data. PETGraph adopts a valid-time temporal property graph data model to facilitate data modeling, supporting ACID features with transactions. To improve temporal graph query performance, we designed a space-efficient temporal property storage and a fine-granularity multi-level locking mechanism. Experimental results show that PETGraph requires, on average, only 33% of the storage space needed by the current best data management solution. Additionally, it achieves an average of 58.8 times higher transaction throughput in HTAP workloads compared to the best current solutions and outperforms them by an average of 267 times in query latency.