# Optimizing Subgraph Queries by Combining Binary and Worst-Case Optimal Joins

## TL;DR

This paper presents a cost-based optimizer that enhances subgraph query performance by intelligently combining binary and worst-case optimal joins, including an adaptive reordering technique during query execution.

## Contribution

It introduces a novel optimizer that selects efficient vertex orderings for worst-case optimal plans and creates hybrid plans mixing binary and multiway joins, outperforming previous methods.

## Key findings

- The optimizer effectively improves query execution times.
- Hybrid plans outperform traditional approaches in experiments.
- Adaptive reordering enhances plan efficiency during execution.

## Abstract

We study the problem of optimizing subgraph queries using the new worst-case optimal join plans. Worst-case optimal plans evaluate queries by matching one query vertex at a time using multiway intersections. The core problem in optimizing worst-case optimal plans is to pick an ordering of the query vertices to match. We design a cost-based optimizer that (i) picks efficient query vertex orderings for worst-case optimal plans; and (ii) generates hybrid plans that mix traditional binary joins with worst-case optimal style multiway intersections. Our cost metric combines the cost of binary joins with a new cost metric called intersection-cost. The plan space of our optimizer contains plans that are not in the plan spaces based on tree decompositions from prior work. In addition to our optimizer, we describe an adaptive technique that changes the orderings of the worst-case optimal sub-plans during query execution. We demonstrate the effectiveness of the plans our optimizer picks and adaptive technique through extensive experiments. Our optimizer is integrated into the Graphflow DBMS.