GPU-accelerated database systems have been studied for more than 10 years, ranging from prototyping development to industry products serving in multiple domains of data applications. Existing GPU database research solutions are often focused on specific aspects in parallel algorithms and system implementations for specific features, while industry product development generally concentrates on delivering a whole system by considering its holistic performance and cost. Aiming to fill this gap between academic research and industry development, we present a comprehensive industry product study on a complete CPU/GPU HTAP system, called RateupDB. We firmly believe "the art of balance" addresses major issues in the development of RateupDB. Specifically, we consider balancing multiple factors in the software development cycle, such as the trade-off between OLAP and OLTP, the trade-off between system performance and development productivity, and balanced choices of algorithms in the product. We also present RateupDB's complete TPC-H test performance to demonstrate its significant advantages over other existing GPU DBMS products.
Abstract. The evolution from relational DBMS to data integration system brings new challenges to the design and implementation of query execution engine that must be extended to support queries over multiple distributed, heterogeneous, and autonomous data sources. In this paper, we introduce our work on extending PostgreSQL to support distributed query processing. Although PostgreSQL has no built-in distributed query processor, its function mechanism provides possibilities for us to integrate data of various data sources and execute distributed queries. We point out several limitations in PostgreSQL's query engine and present corresponding query execution techniques to improve performance of distributed query processing. Our experimental results show that the techniques can significantly reduce response times when running a workload consisting of TPC-H queries.
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