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arXiv 2107.07296cs.PL
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Meta流协议的艺术:流的洪流

The Art of the Meta Stream Protocol: Torrents of Streams

Christophe De Troyer, Jens Nicolay, Wolfgang De Meuter

摘要:

Akka Stream、Reactive Extensions和LINQ等流库的兴起普及了数据处理的声明式函数风格。流范例提供了简洁的语法来写下处理管道,以消耗当今可用的大量实时数据。这些库为程序员提供了嵌入在宿主语言中的域特定语言(DSL)来描述数据流。然而,这些库都存在可扩展性问题。流的语义被硬编码到DSL语言中,并且不能被库的用户改变。我们介绍了一种方法来修改流媒体库的语义通过元编程在运行时和编译时,并展示其通用性。我们表明,元设施的表现力是强大的,足以使推和拉语义,错误处理,并行,和运营商融合。我们评估我们的工作,通过实施一个新的流元架构方面确定的缺点,并表明其设计和架构坚持元级架构的设计原则。最先进的技术为程序员提供了大量关于反应式流处理库的选择。用通用语言表达反应式系统是很困难的。在这些库中,扩展性和微调应该是可能的,以确保在这个单一的DSL中可以表达各种各样的应用程序。

英文摘要:

The rise of streaming libraries such as Akka Stream, Reactive Extensions, and LINQ popularized the declarative functional style of data processing. The stream paradigm offers concise syntax to write down processing pipelines to consume the vast amounts of real-time data available today. These libraries offer the programmer a domain specific language (DSL) embedded in the host language to describe data streams. These libraries however, all suffer from extensibility issues. The semantics of a stream is hard-coded into the DSL language and cannot be changed by the user of the library. We introduce an approach to modify the semantics of a streaming library by means of meta-programming at both run-time and compile-time, and showcase its generality. We show that the expressiveness of the meta-facilities is strong enough to enable push and pull semantics, error handling, parallelism, and operator fusion. We evaluate our work by implementing the identified shortcomings in terms of a novel stream meta-architecture and show that its design and architecture adhere to the design principles of a meta-level architecture. The state of the art offers plenty of choice to programmers regarding reactive stream processing libraries. Expressing reactive systems is otherwise difficult to do in general purpose languages. Extensibility and fine-tuning should be possible in these libraries to ensure a broad variety of applications can be expressed within this single DSL.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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