MICRO: A Lightweight Middleware for Optimizing Cross-store Cross-model Graph-Relation Joins [Technical Report]

TL;DR

This paper introduces MICRO, a lightweight middleware that efficiently executes cross-store, cross-model graph-relation joins by using a learning-to-rank optimizer, enabling native heterogeneous data querying with significant performance improvements.

Contribution

The paper formalizes cross-model join queries with a unified algebra, proposes a new middleware MICRO with a learning-to-rank optimizer, and evaluates it using real-world and synthetic benchmarks.

Findings

MICRO outperforms XDB by up to 2.1x in total runtime.

14 out of 93 queries achieve over 100x speedup.

CMLero optimizer outperforms rule-based and regression-based methods.

Abstract

Modern data applications increasingly involve heterogeneous data managed in different models and stored across disparate database engines, often deployed as separate installs. Limited research has addressed cross-model query processing in federated environments. This paper takes a step toward bridging this gap by: (1) formally defining a class of cross-model join queries between a graph store and a relational store by proposing a unified algebra; (2) introducing one real-world benchmark and four semi-synthetic benchmarks to evaluate such queries; and (3) proposing a lightweight middleware, MICRO, for efficient query execution. At the core of MICRO is CMLero, a learning-to-rank-based query optimizer that selects efficient execution plans without requiring exact cost estimation. By avoiding the need to materialize or convert all data into a single model, which is often infeasible due to third-party data control or cost, MICRO enables native querying across heterogeneous systems. Experimental results on the benchmark workloads demonstrate that MICRO outperforms the state-of-the-art federated relational system XDB by up to 2.1x in total runtime across the full test set. On the 93 test queries of real-world benchmark, 14 queries achieve over 100 speedup, including 4 queries with more than 100x speedup; however, 4 queries experienced slowdowns of over 5 seconds, highlighting opportunities for future improvement of MICRO. Further comparisons show that CMLero consistently outperforms rule-based and regression-based optimizers, highlighting the advantage of learning-to-rank in complex cross-model optimization.