Color: A Framework for Applying Graph Coloring to Subgraph Cardinality Estimation

TL;DR

This paper introduces COLOR, a graph compression-based framework for subgraph cardinality estimation that significantly improves accuracy and efficiency in query optimization tasks involving complex graph workloads.

Contribution

The paper presents a novel framework applying graph compression theory to estimate subgraph cardinalities, outperforming existing methods in accuracy and efficiency.

Findings

Up to 1000x accuracy improvement over competing methods

Fast inference with small memory footprint

Effective under large and dynamic query graphs

Abstract

Graph workloads pose a particularly challenging problem for query optimizers. They typically feature large queries made up of entirely many-to-many joins with complex correlations. This puts significant stress on traditional cardinality estimation methods which generally see catastrophic errors when estimating the size of queries with only a handful of joins. To overcome this, we propose COLOR, a framework for subgraph cardinality estimation which applies insights from graph compression theory to produce a compact summary that captures the global topology of the data graph. Further, we identify several key optimizations that enable tractable estimation over this summary even for large query graphs. We then evaluate several designs within this framework and find that they improve accuracy by up to 10$^3$x over all competing methods while maintaining fast inference, a small memory footprint, efficient construction, and graceful degradation under updates.