Language-Agnostic Integrated Queries in a Managed Polyglot Runtime
Language-Agnostic Integrated Queries in a Managed Polyglot Runtime
复制标题
托管多语言运行时中与语言无关的集成查询
DOI：
10.14778/3457390.3457405
复制
发表时间：
2021
期刊：
影响因子：
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通讯作者：
Walter Binder
中科院分区：
文献类型：
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作者：
F. Schiavio;Daniele Bonetta;Walter Binder
数据库集成查询（LINQ）框架为处理内存中的数据集合提供了方便的编程抽象，允许开发人员使用通用编程语言简洁地表达声明性查询。现有的LINQ框架依赖于静态类型语言（如C
#
或Java来执行查询编译和执行。由于这种设计，它们不支持动态语言，如Python、R或JavaScript。然而，这些语言在数据科学家中非常流行，他们肯定会从数据分析应用程序中的LINQ框架中受益。
在这项工作中，我们弥合了动态语言和LINQ框架之间的差距。我们介绍DynQ，一个新的查询引擎设计的动态语言。DynQ是语言无关的，因为它能够在多语言运行时中执行SQL查询。此外，DynQ还可以执行来自多个源的数据组合查询，即内存中的对象集合以及文件数据和外部数据库系统。我们对DynQ的评估显示，其性能与同等的手工优化代码相当，并且与常见的数据处理库和嵌入式数据库一致，使DynQ成为独立分析应用程序和数据密集型服务器端工作负载的有吸引力的查询引擎。
Language-integrated query (LINQ) frameworks offer a convenient programming abstraction for processing in-memory collections of data, allowing developers to concisely express declarative queries using general-purpose programming languages. Existing LINQ frameworks rely on the well-defined type system of statically-typed languages such as C
#
or Java to perform query compilation and execution. As a consequence of this design, they do not support dynamic languages such as Python, R, or JavaScript. Such languages are however very popular among data scientists, who would certainly benefit from LINQ frameworks in data analytics applications.
In this work we bridge the gap between dynamic languages and LINQ frameworks. We introduce DynQ, a novel query engine designed for dynamic languages. DynQ is language-agnostic, since it is able to execute SQL queries in a polyglot language runtime. Moreover, DynQ can execute queries combining data from multiple sources, namely in-memory object collections as well as on-file data and external database systems. Our evaluation of DynQ shows performance comparable with equivalent hand-optimized code, and in line with common data-processing libraries and embedded databases, making DynQ an appealing query engine for standalone analytics applications and for data-intensive server-side workloads.
编译查询的自适应执行
DOI：
10.1109/icde.2018.00027
发表时间：
2018
期刊：
2018 IEEE 34th International Conference on Data Engineering (ICDE)
影响因子：
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作者：
J. André Kohn;Viktor Leis;Thomas Neumann
通讯作者：
Thomas Neumann
为现代硬件高效编译高效的查询计划
DOI：
10.14778/2002938.2002940
发表时间：
2011
期刊：
Proc. VLDB Endow.
影响因子：
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作者：
T. Neumann
通讯作者：
T. Neumann