Property graph schema evolution enabled by reusable transformation sequences
Transformations for Evolving Property Graph Schemas
Databases
Summary
Graph databases store information with flexible structures that often change over time. The authors created GRAFT, a system that helps automatically transform graph database schemas in a reusable and reliable way. GRAFT breaks down schema changes into small steps and finds sequences of these steps to reach new schemas efficiently. Tests show it works well on multiple datasets, producing stable and correct schema updates while running quickly.
What this means in practice
- •For graph database engineers: Automate and reuse schema updates in property graph databases to manage evolving data structures efficiently.
- •For data platform architects: Design systems that support systematic, order-aware evolution of graph schemas ensuring correctness and runtime efficiency.
Authors
Gauvain Devillez, Stefania Dumbrava, Angela Bonifati
Abstract
Property graph databases are widely used to represent complex and evolving data; yet, systematic support for property graph schema evolution remains limited. In practice, schema transformations are typically defined manually, coupled to specific application contexts, and are difficult to reuse across schemas or evolution scenarios. We present GRAFT, a logic-based framework that models prop- erty graph schema evolution as reusable, order-constrained meta- transformations derived from atomic edits. Schema evolution is formulated as exploration of a finite meta-graph with schemas as nodes and grounded meta-transformations as edges. To ensure tractability, GRAFT combines similarity-guided search and pruning, guaranteeing duplication-freeness, termination and correctness. An experimental evaluation on four benchmark and real-world property graph schema evolution scenarios shows that GRAFT effi- ciently computes high-quality schema transformation sequences. Using greedy exploration, GRAFT reaches the exact target schema on most datasets, producing stable transformation sequences while keeping runtimes low. A qualitative study on both real-world and a synthetic large-scale dataset further shows the quality and robust- ness of the obtained reusable meta-transformations.