Electra: Conditional Generative Model based Predicate-Aware Query Approximation

TL;DR

ELECTRA is a predicate-aware approximate query processing system that uses a conditional generative model to generate representative samples, significantly reducing approximation errors for queries with many predicates.

Contribution

This paper introduces ELECTRA, a novel conditional generative model-based AQP system that maintains low approximation errors even with numerous query predicates.

Findings

ELECTRA achieves lower approximation errors than baselines on real datasets.

ELECTRA effectively handles queries with many predicates.

ELECTRA generates small, representative samples for fast query approximation.

Abstract

The goal of Approximate Query Processing (AQP) is to provide very fast but "accurate enough" results for costly aggregate queries thereby improving user experience in interactive exploration of large datasets. Recently proposed Machine-Learning based AQP techniques can provide very low latency as query execution only involves model inference as compared to traditional query processing on database clusters. However, with increase in the number of filtering predicates(WHERE clauses), the approximation error significantly increases for these methods. Analysts often use queries with a large number of predicates for insights discovery. Thus, maintaining low approximation error is important to prevent analysts from drawing misleading conclusions. In this paper, we propose ELECTRA, a predicate-aware AQP system that can answer analytics-style queries with a large number of predicates with much smaller approximation errors. ELECTRA uses a conditional generative model that learns the conditional distribution of the data and at runtime generates a small (~1000 rows) but representative sample, on which the query is executed to compute the approximate result. Our evaluations with four different baselines on three real-world datasets show that ELECTRA provides lower AQP error for large number of predicates compared to baselines.