HomeRun: A Cardinality Estimation Advisor for Graph Databases

Database systems depend on cardinality estimates for generation of optimal query execution plans. Selecting an appropriate cardinality estimation technique involves navigating trade-offs, including the accuracy of estimates, time required for estimation, and necessary statistics. These trade-offs can lead to different choices based on the dataset and query workload. Unfortunately there is limited support for advising graph database users in exploring these trade-offs and making the right choices for their scenarios. To address this critical gap, we introduce an advisor tool, HomeRun, which analyzes the performance of various cardinality estimation techniques in given usage scenarios. We explain HomeRun's capabilities using the industry-standard LSQB benchmark and synthetic scenarios. HomeRun reveals how minor changes in the dataset can significantly impact the conclusions about the performance of cardinality estimation techniques.