Towards a Multi-engine Query Optimizer for Complex SQL Queries on Big Data

In an era where big data analytics has become a first-class requirement for both the industrial and the academic conummity, multiple engines are built to execute distributed domain-specific analytics. SQL-based big data analytics is a very popular but also challenging domain due to its complexity that requires multiple runtime query optimizations. Popular frameworks, such as Presto and SparkSQL, commonly retrieve data from multiple sources and process them locally using domain-specific optimizers. However, recent work indicates that no single engine offers the optimal all-in-one solution for all types of SQL queries. Taking this into account, we envision building an optimizer to facilitate faster distributed SQL analytics over multiple engines, which will perform operator-level optimization using Machine Learning techniques and will exploit the sophisticated data-driven local engine opthnizations.