Electra: Conditional Generative Model based Predicate-Aware Query Approximation
This addresses the need for accurate interactive data exploration for analysts, but it is incremental as it builds on existing ML-based AQP methods by focusing on predicate handling.
The paper tackles the problem of high approximation error in Approximate Query Processing (AQP) when queries have many filtering predicates, proposing ELECTRA, a predicate-aware AQP system that uses a conditional generative model to generate representative samples, resulting in lower error for large numbers of predicates compared to baselines.
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.