Adaptive Code Generation for Data-Intensive Analytics

Modern database management systems employ sophisticated query optimization techniques that enable the generation of efficient plans for queries over very large data sets. A variety of other applications also process large data sets, but cannot leverage database-style query optimization for their code. We therefore identify an opportunity to enhance an open-source programming language compiler with database-style query optimization. Our system dynamically generates execution plans at query time, and runs those plans on chunks of data at a time. Based on feedback from earlier chunks, alternative plans might be used for later chunks. The compiler extension could be used for a variety of data-intensive applications, allowing all of them to benefit from this class of performance optimizations.