Abstract:Abstract. Macros improve expressiveness, concision, abstraction, and language interoperability without changing the programming language itself. They are indispensable for building increasingly prevalent multilingual applications. Unfortunately, existing macro systems are wellencapsulated but unsafe (e.g., the C preprocessor) or are safe but tightlyintegrated with the language implementation (e.g., Scheme macros). This paper introduces Marco, the first macro system that seeks both encapsulation and safety. Mar… Show more
“…Jeannie takes this to the extreme by embedding Java into C and vice versa [Hirzel and Grimm 2007]. Finally, Marco is a macro system that supports multiple target languages, including C++, while still offering early error detection [Lee et al 2012]. In contrast to these approaches, extensibility in SPL focuses on operators.…”
Big data is revolutionizing how all sectors of our economy do business, including telecommunication, transportation, medical, and finance. Big data comes in two flavors: data at rest and data in motion. Processing data in motion is stream processing. Stream processing for big data analytics often requires scale that can only be delivered by a distributed system, exploiting parallelism on many hosts and many cores. One such distributed stream processing system is IBM Streams. Early customer experience with IBM Streams uncovered that another core requirement is extensibility, since customers want to build high-performance domain-specific operators for use in their streaming applications. Based on these two core requirements of distribution and extensibility, we designed and implemented the Streams Processing Language (SPL). This article describes SPL with an emphasis on the language design, distributed runtime, and extensibility mechanism. SPL is now the gateway for the IBM Streams platform, used by our customers for stream processing in a broad range of application domains. CCS Concepts: r Software and its engineering → Data flow languages;
“…Jeannie takes this to the extreme by embedding Java into C and vice versa [Hirzel and Grimm 2007]. Finally, Marco is a macro system that supports multiple target languages, including C++, while still offering early error detection [Lee et al 2012]. In contrast to these approaches, extensibility in SPL focuses on operators.…”
Big data is revolutionizing how all sectors of our economy do business, including telecommunication, transportation, medical, and finance. Big data comes in two flavors: data at rest and data in motion. Processing data in motion is stream processing. Stream processing for big data analytics often requires scale that can only be delivered by a distributed system, exploiting parallelism on many hosts and many cores. One such distributed stream processing system is IBM Streams. Early customer experience with IBM Streams uncovered that another core requirement is extensibility, since customers want to build high-performance domain-specific operators for use in their streaming applications. Based on these two core requirements of distribution and extensibility, we designed and implemented the Streams Processing Language (SPL). This article describes SPL with an emphasis on the language design, distributed runtime, and extensibility mechanism. SPL is now the gateway for the IBM Streams platform, used by our customers for stream processing in a broad range of application domains. CCS Concepts: r Software and its engineering → Data flow languages;
“…Macro systems do not usually differentiate between lexical elements of the hosting language such as expressions, identifiers, constants, etc. One preprocessor that differs in this aspect is the Marco preprocessor [56], which has a way to reduce the coupling between the host language to the macro system.…”
“…They provide support for generating new programming languages and tooling in a modular manner. The Marco language [16] similarly provides macro definition at a level of abstraction that is largely independent of the target language. In these approaches, each TSL is external relative to the host language; in contrast, Wyvern focuses on internal extensibility, improving interoperability and composability.…”
Abstract. Programming languages often include specialized syntax for common datatypes (e.g. lists) and some also build in support for specific specialized datatypes (e.g. regular expressions), but user-defined types must use generalpurpose syntax. Frustration with this causes developers to use strings, rather than structured data, with alarming frequency, leading to correctness, performance, security, and usability issues. Allowing library providers to modularly extend a language with new syntax could help address these issues. Unfortunately, prior mechanisms either limit expressiveness or are not safely composable: individually unambiguous extensions can still cause ambiguities when used together. We introduce type-specific languages (TSLs): logic associated with a type that determines how the bodies of generic literals, able to contain arbitrary syntax, are parsed and elaborated, hygienically. The TSL for a type is invoked only when a literal appears where a term of that type is expected, guaranteeing noninterference. We give evidence supporting the applicability of this approach and formally specify it with a bidirectionally typed elaboration semantics for the Wyvern programming language.
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