DatabaseSupported Program Execution

我们继续研究数据库支持的编程环境，在这种环境中，开发人员不再需要掌握双语(编程和数据库语言)来构建高效的数据丰富的应用程序。相反，开发人员继续使用该编程语言的众所周知的结构和习惯用法来执行数据库操作。新的编译技术识别可由接近数据的底层数据库系统(与编程语言运行时系统相对)评估的程序片段。在这个项目的续篇中，我们扩展了数据库支持的程序执行，以包含表达数据密集型算法所必需的惯用语言构造：迭代构造的新泛化(广义一元理解)、高阶函数或闭包。此外，新的数据库操作嵌入技术旨在消除宿主语言和数据库语言之间令人恼火的接缝。当对数据项的操作相互独立时，我们现在更努力地从中受益。新的编译策略发出向量指令，现代数据库内核、CPU和其他平台可以利用这些指令来执行数据并行计算。同时，我们对编译器内部进行了模块化和重新组织，以纳入新的强大的优化，并在更多的使用场景中开发该项目。

We continue our research into database-supported programming environments in which developers are no longer required to be bilingual (programming and database language) to construct efficient data-rich applications. Instead, developers continue to use the well-known constructs and idioms of the programming language to perform database operations. New compilation techniques identify program fragments that can be evaluated by an underlying database system (as opposed to the pro- gramming language runtime system), close to the data. In effect, the database system acts as a capable and scalable co-processor to the language runtime system.In this continuation of the project, we extend database-supported program execution to embrace idiomatic language constructs that are essential in expressing data-intensive algorithms: new generalizations of iterative constructs (generalized monad comprehensions), higher-order functions, or closures. Further, new embedding techniques for database operations aim to remove irritating seams between the host language and the database language.Whenever operations on data items are mutually independent, we now try harder to benefit. New compilation strategies emit vector instructions which can be exploited by modern database kernels, CPUs, and other platforms to perform data-parallel evaluation. At the same time, we modularize and re-organize compiler internals to incorporate new powerful optimizations and to exploit the project in more usage scenarios.