TVA: A Version-aware Temporal Graph Storage System for Real-time Analytics

2026-07-01

AI Summary

Existing graph storage systems lack native support for temporal data, making it challenging to meet the low-latency requirements of real-time temporal graph analytics. To address this limitation, this work proposes an efficient storage system tailored for temporal graphs, featuring a multi-version storage architecture that decouples metadata from attribute data. The design incorporates compact temporal tables, an enhanced hopscotch hashing structure, and a version-skipping strategy, collectively optimizing neighborhood scans and multi-vertex queries. Experimental results demonstrate that the proposed system reduces query latency by up to 9.9× and decreases storage overhead by 2.2× compared to the state-of-the-art temporal graph storage solutions.

Abstract

Analyzing temporal graphs can reveal valuable insights that are typically hidden in static graphs. Unfortunately, existing graph storage systems either lack native temporal support or suffer from high latency when querying temporal graphs. This paper presents TVA, a new temporal graph storage system designed for efficient temporal query processing. First, TVA introduces a specialized multi-version storage architecture that separates version metadata from actual data, i.e., the property values associated with different versions of vertices and edges. This architecture enables efficient version retrieval for a vertex or edge by quickly locating valid version metadata and directly dereferencing it to access the corresponding property values. Second, we design tailored data structures, namely the temporal table and enhanced hopscotch-based hashing, to compactly organize the version metadata of adjacent vertices and edges, thus reducing random I/O for metadata lookups during the neighborhood scan initiated from a vertex. Finally, to further accelerate neighborhood scans over multiple vertices, we propose a version-kipping strategy that reuses temporal information obtained from prior scans, thereby avoiding redundant metadata lookups across scans. Empirical evaluations demonstrate that TVA achieves up to 9.9x lower temporal query latency and 2.2x lower storage overhead compared to state-of-the-art temporal graph storage systems.

Problem

• temporal graph
• graph storage system
• query latency
• real-time analytics
• version management

Innovation

• temporal graph
• multi-version storage
• version-skipping
• hopscotch hashing
• real-time analytics