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Domain-specific language

From Wikipedia, the free encyclopedia

A domain-specific language (DSL) is a computer language specialized to a particular application domain. This is in contrast to a general-purpose language (GPL), which is broadly applicable across domains. There are a wide variety of DSLs, ranging from widely used languages for common domains, such as HTML for web pages, down to languages used by only one or a few pieces of software, such as MUSH soft code. DSLs can be further subdivided by the kind of language, and include domain-specific markup languages, domain-specific modeling languages (more generally, specification languages), and domain-specific programming languages. Special-purpose computer languages have always existed in the computer age, but the term "domain-specific language" has become more popular due to the rise of domain-specific modeling. Simpler DSLs, particularly ones used by a single application, are sometimes informally called mini-languages.

The line between general-purpose languages and domain-specific languages is not always sharp, as a language may have specialized features for a particular domain but be applicable more broadly, or conversely may in principle be capable of broad application but in practice used primarily for a specific domain. For example, Perl was originally developed as a text-processing and glue language, for the same domain as AWK and shell scripts, but was mostly used as a general-purpose programming language later on. By contrast, PostScript is a Turing-complete language, and in principle can be used for any task, but in practice is narrowly used as a page description language.

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  • 18. Domain Specific Languages and Autotuning
  • Writing Domain Specific Languages – Rahul Malik

Transcription

Why do you believe the future of programming language design lies in domain specific languages when we can just do operator overloading and APIs in a general language like C? Well, that’s a tough question and actually there is a bit of philosophy or opinion here. In class, we keep brushing against Turing completeness, and it turns out that languages like C++ or C# or Java or MATLAB are all going to allow us to express the same computations ultimately--that is, if it’s possible to do it in one language, it’s also possible to do it in the other, but that doesn’t say anything about how easy it is, how much help the language will give you about making mistakes, and how quick the final result will be. In my opinion, three important features that the domain specific language gives you over just coding up special types or operations in a general purpose language like C++ or C# are the conciseness of the representation, the ability to do type checking or run-time checking or otherwise have safety built-in, and the ability of the compiler or run-time system to do optimization. Let's take something like MATLAB, which is very good at doing mathematical operations or matrices or matrix multiply in a concise manner, and take it as sort of a running example. You could code up the same sorts of operations, matrix multiply, and language like C or C++ and C#, and you might start out by having them the functions that take a number of arguments in making your own data types. And to that, it’s relatively clear that languages like MATLAB, which just like to use the star to multiply two matrices, is going to be more concise. However, once you start adding operator overloading or feature of languages like C++ or C# that allows you to change the meaning of symbols like plus or stars that they, in essence, call functions you define. Then, the conciseness argument is more of a wash sort of a tie for both ends. However, the other two aspects, type safety or type checking and optimization, are still really critical. A language specifically designed to handle Mathematics or matrices something like MATLAB is going to be able to notice potentially more easily if you make mistakes related to that particular domain. For example, in C or C++, often a two-dimensional matrix in array is really just a single array carefully embedded. There's some sort of stride or approach where we reuse the elements of the indices in a very long one-dimensional array as if it were a two-dimensional array. And it is really easy to make mistakes to pass in arrays or matrices that have the wrong dimensions to matrix-matrix multiply, to confuse row major order and column major order, and language like C or C++ depending on which matrix implementation you're using won’t give you any support with that. It will silently let you shoot yourself in the foot. Compute the wrong answer. You might not even crash. You might just get something you’re not expecting. And that’s really problematic because these days the constraint is often programmer time rather than CPU time. I’d rather use a language where these things are built-in as first-class citizens, and there’s the possibility that it will alert me to error. Many of you may have a favorite C++ or C# or Java matrix library that would catch those errors. Again, ultimately, since these languages have equivalent expressive power, you can add that sort of error checking to any language or library. But often domain-specific languages do a better job at it. And the third example is then optimization. The higher-level instructions you give to a compiler or interpreter, the more scope it has for creativity, the more chances it has to reorder your statements or implement them in other way, the closer you can get to just being declarative. I want to multiply these matrices, and I don’t care how you do it. The more the compiler under the hood is able to take advantage of things like memory hierarchies, caches, special multimedia instructions you might have in order to get that sort of thing done well under the hood. If you actually write out your matrix-matrix multiply as three nested for loops, you’re forcing the compiler to generate code for that particular implementation. Often domain specific languages allow you to express things like matrix transposition or multiplication at a very high level. And thus, they actually end up generating better code for new target architectures than you might do if you were to code it up yourself. So you end up spending less time writing. It’s concise. You get guarantees, and it’s faster. And in fact, you can view the push to domain specific languages as just an extension of the push from assembly languages to high level languages. One of the first arguments made in favor of higher level languages were early studies in computer science that found that the number of lines of code that programmers could write per day essentially over the lifetime of a project was constant regardless of what language you are using. You could either pay your programmers and get ten lines of assembly or you could pay your programmers and get ten lines of C or ten lines of Python or some such. And if you have experience with multiple languages, typically you can get significantly more done in ten lines of Python than you can in ten lines of assembly because of built-in support for dictionaries or lambda, higher order data types, object-oriented programming, that sort of thing. Ultimately, you can get everything done with assembly, but it would take longer. The same argument here applies to domain specific languages. If I have a new more exotic domain-specific language, let’s pick MacroLab. That's a favorite one of mine for programming wireless sensor networks for moving data around and performing distributed computations. Let’s say for example that you want to keep track of people passing through your storefront advertising window to see if it’s actually working in attracting people. You might make up some sort of wireless sensor network to keeps track. You want to write programs for that. You could do them and see your assembly language or you could use this sort of domain specific language since it has notions of distributed computation where data lives, sending information back and forth, built-in as primitives. It’s able to check mistakes for you, generate good code for you, and in general, improve your productivity. Could you get all of that done with a well-crafted library? Yes. And in some sense, ultimately at the end of the day, maybe there is no difference between the well-crafted library and a domain specific language. But I think we'll see a lot of the initial effort or improvement common to domain specific language side and then the libraries will follow and catch up.

Use

The design and use of appropriate DSLs is a key part of domain engineering, by using a language suitable to the domain at hand – this may consist of using an existing DSL or GPL, or developing a new DSL. Language-oriented programming considers the creation of special-purpose languages for expressing problems as standard part of the problem-solving process. Creating a domain-specific language (with software to support it), rather than reusing an existing language, can be worthwhile if the language allows a particular type of problem or solution to be expressed more clearly than an existing language would allow and the type of problem in question reappears sufficiently often. Pragmatically, a DSL may be specialized to a particular problem domain, a particular problem representation technique, a particular solution technique, or other aspects of a domain.

Overview

A domain-specific language is created specifically to solve problems in a particular domain and is not intended to be able to solve problems outside of it (although that may be technically possible). In contrast, general-purpose languages are created to solve problems in many domains. The domain can also be a business area. Some examples of business areas include:

  • life insurance policies (developed internally by a large insurance enterprise)
  • combat simulation
  • salary calculation
  • billing

A domain-specific language is somewhere between a tiny programming language and a scripting language, and is often used in a way analogous to a programming library. The boundaries between these concepts are quite blurry, much like the boundary between scripting languages and general-purpose languages.

In design and implementation

Domain-specific languages are languages (or often, declared syntaxes or grammars) with very specific goals in design and implementation. A domain-specific language can be one of a visual diagramming language, such as those created by the Generic Eclipse Modeling System, programmatic abstractions, such as the Eclipse Modeling Framework, or textual languages. For instance, the command line utility grep has a regular expression syntax which matches patterns in lines of text. The sed utility defines a syntax for matching and replacing regular expressions. Often, these tiny languages can be used together inside a shell to perform more complex programming tasks.

The line between domain-specific languages and scripting languages is somewhat blurred, but domain-specific languages often lack low-level functions for filesystem access, interprocess control, and other functions that characterize full-featured programming languages, scripting or otherwise. Many domain-specific languages do not compile to byte-code or executable code, but to various kinds of media objects: GraphViz exports to PostScript, GIF, JPEG, etc., where Csound compiles to audio files, and a ray-tracing domain-specific language like POV compiles to graphics files.

Data definition languages

A data definition language like SQL presents an interesting case: it can be deemed a domain-specific language because it is specific to a specific domain (in SQL's case, accessing and managing relational databases), and is often called from another application, but SQL has more keywords and functions than many scripting languages, and is often thought of as a language in its own right, perhaps because of the prevalence of database manipulation in programming and the amount of mastery required to be an expert in the language.

Further blurring this line, many domain-specific languages have exposed APIs, and can be accessed from other programming languages without breaking the flow of execution or calling a separate process, and can thus operate as programming libraries.

Programming tools

Some domain-specific languages expand over time to include full-featured programming tools, which further complicates the question of whether a language is domain-specific or not. A good example is the functional language XSLT, specifically designed for transforming one XML graph into another, which has been extended since its inception to allow (particularly in its 2.0 version) for various forms of filesystem interaction, string and date manipulation, and data typing.

In model-driven engineering, many examples of domain-specific languages may be found like OCL, a language for decorating models with assertions or QVT, a domain-specific transformation language. However, languages like UML are typically general-purpose modeling languages.

To summarize, an analogy might be useful: a Very Little Language is like a knife, which can be used in thousands of different ways, from cutting food to cutting down trees.[clarification needed] A domain-specific language is like an electric drill: it is a powerful tool with a wide variety of uses, but a specific context, namely, putting holes in things. A General Purpose Language is a complete workbench, with a variety of tools intended for performing a variety of tasks. Domain-specific languages should be used by programmers who, looking at their current workbench, realize they need a better drill and find that a particular domain-specific language provides exactly that.[citation needed]

Domain-specific language topics

External and Embedded Domain Specific Languages

DSLs implemented via an independent interpreter or compiler are known as External Domain Specific Languages. Well known examples include LaTeX or AWK. A separate category known as Embedded (or Internal) Domain Specific Languages are typically implemented within a host language as a library and tend to be limited to the syntax of the host language, though this depends on host language capabilities.[1]

Usage patterns

There are several usage patterns for domain-specific languages:[2][3]

  • Processing with standalone tools, invoked via direct user operation, often on the command line or from a Makefile (e.g., grep for regular expression matching, sed, lex, yacc, the GraphViz toolset, etc.)
  • Domain-specific languages which are implemented using programming language macro systems, and which are converted or expanded into a host general purpose language at compile-time or realtime
  • embedded domain-specific language (eDSL),[4] implemented as libraries which exploit the syntax of their host general purpose language or a subset thereof while adding domain-specific language elements (data types, routines, methods, macros etc.). (e.g. jQuery, React, Embedded SQL, LINQ)
  • Domain-specific languages which are called (at runtime) from programs written in general purpose languages like C or Perl, to perform a specific function, often returning the results of operation to the "host" programming language for further processing; generally, an interpreter or virtual machine for the domain-specific language is embedded into the host application (e.g. format strings, a regular expression engine)
  • Domain-specific languages which are embedded into user applications (e.g., macro languages within spreadsheets) and which are (1) used to execute code that is written by users of the application, (2) dynamically generated by the application, or (3) both.

Many domain-specific languages can be used in more than one way.[citation needed] DSL code embedded in a host language may have special syntax support, such as regexes in sed, AWK, Perl or JavaScript, or may be passed as strings.

Design goals

Adopting a domain-specific language approach to software engineering involves both risks and opportunities. The well-designed domain-specific language manages to find the proper balance between these.

Domain-specific languages have important design goals that contrast with those of general-purpose languages:

  • Domain-specific languages are less comprehensive.
  • Domain-specific languages are much more expressive in their domain.
  • Domain-specific languages should exhibit minimal redundancy.

Idioms

In programming, idioms are methods imposed by programmers to handle common development tasks, e.g.:

  • Ensure data is saved before the window is closed.
  • Edit code whenever command-line parameters change because they affect program behavior.

General purpose programming languages rarely support such idioms, but domain-specific languages can describe them, e.g.:

  • A script can automatically save data.
  • A domain-specific language can parameterize command line input.

Examples

Examples of domain-specific programming languages include HTML, Logo for pencil-like drawing, Verilog and VHDL hardware description languages, MATLAB and GNU Octave for matrix programming, Mathematica, Maple and Maxima for symbolic mathematics, Specification and Description Language for reactive and distributed systems, spreadsheet formulas and macros, SQL for relational database queries, YACC grammars for creating parsers, regular expressions for specifying lexers, the Generic Eclipse Modeling System for creating diagramming languages, Csound for sound and music synthesis, and the input languages of GraphViz and GrGen, software packages used for graph layout and graph rewriting, Hashicorp Configuration Language used for Terraform and other Hashicorp tools, Puppet also has its own configuration language.

GameMaker Language

The GML scripting language used by GameMaker Studio is a domain-specific language targeted at novice programmers to easily be able to learn programming. While the language serves as a blend of multiple languages including Delphi, C++, and BASIC, there is a lack of structures, data types, and other features of a full-fledged programming language. Many of the built-in functions are sandboxed for the purpose of easy portability. The language primarily serves to make it easy for anyone to pick up the language and develop a game.

ColdFusion Markup Language

ColdFusion's associated scripting language is another example of a domain-specific language for data-driven websites. This scripting language is used to weave together languages and services such as Java, .NET, C++, SMS, email, email servers, http, ftp, exchange, directory services, and file systems for use in websites.

The ColdFusion Markup Language (CFML) includes a set of tags that can be used in ColdFusion pages to interact with data sources, manipulate data, and display output. CFML tag syntax is similar to HTML element syntax.

Erlang OTP

The Erlang Open Telecom Platform was originally designed for use inside Ericsson as a domain-specific language. The language itself offers a platform of libraries to create finite state machines, generic servers and event managers that quickly allow an engineer to deploy applications, or support libraries, that have been shown in industry benchmarks to outperform other languages intended for a mixed set of domains, such as C and C++. The language is now officially open source and can be downloaded from their website.

FilterMeister

FilterMeister is a programming environment, with a programming language that is based on C, for the specific purpose of creating Photoshop-compatible image processing filter plug-ins; FilterMeister runs as a Photoshop plug-in itself and it can load and execute scripts or compile and export them as independent plug-ins. Although the FilterMeister language reproduces a significant portion of the C language and function library, it contains only those features which can be used within the context of Photoshop plug-ins and adds a number of specific features only useful in this specific domain.

MediaWiki templates

The Template feature of MediaWiki is an embedded domain-specific language whose fundamental purpose is to support the creation of page templates and the transclusion (inclusion by reference) of MediaWiki pages into other MediaWiki pages.

Software engineering uses

There has been much interest in domain-specific languages to improve the productivity and quality of software engineering. Domain-specific language could possibly provide a robust set of tools for efficient software engineering. Such tools are beginning to make their way into the development of critical software systems.

The Software Cost Reduction Toolkit[5] is an example of this. The toolkit is a suite of utilities including a specification editor to create a requirements specification, a dependency graph browser to display variable dependencies, a consistency checker to catch missing cases in well-formed formulas in the specification, a model checker and a theorem prover to check program properties against the specification, and an invariant generator that automatically constructs invariants based on the requirements.

A newer development is language-oriented programming, an integrated software engineering methodology based mainly on creating, optimizing, and using domain-specific languages.

Metacompilers

Further information: Metacompiler

Complementing language-oriented programming, as well as all other forms of domain-specific languages, are the class of compiler writing tools called metacompilers. A metacompiler is not only useful for generating parsers and code generators for domain-specific languages, but a metacompiler itself compiles a domain-specific metalanguage specifically designed for the domain of metaprogramming.

Besides parsing domain-specific languages, metacompilers are useful for generating a wide range of software engineering and analysis tools. The meta-compiler methodology is often found in program transformation systems.

Metacompilers that played a significant role in both computer science and the computer industry include Meta-II,[6] and its descendant TreeMeta.[7]

Unreal Engine before version 4 and other games

Unreal and Unreal Tournament unveiled a language called UnrealScript. This allowed for rapid development of modifications compared to the competitor Quake (using the Id Tech 2 engine). The Id Tech engine used standard C code meaning C had to be learned and properly applied, while UnrealScript was optimized for ease of use and efficiency. Similarly, the development of more recent games introduced their own specific languages, one more common example is Lua for scripting.[citation needed]

Rules engines for policy automation

Various business rules engines have been developed for automating policy and business rules used in both government and private industry. ILOG, Oracle Policy Automation, DTRules, Drools and others provide support for DSLs aimed to support various problem domains. DTRules goes so far as to define an interface for the use of multiple DSLs within a rule set.

The purpose of business rules engines is to define a representation of business logic in as human-readable fashion as possible. This allows both subject-matter experts and developers to work with and understand the same representation of the business logic. Most rules engines provide both an approach to simplifying the control structures for business logic (for example, using declarative rules or decision tables) coupled with alternatives to programming syntax in favor of DSLs.

Statistical modelling languages

Statistical modelers have developed domain-specific languages such as R (an implementation of the S language), Bugs, Jags, and Stan. These languages provide a syntax for describing a Bayesian model and generate a method for solving it using simulation.

Generate model and services to multiple programming Languages

Generate object handling and services based on an Interface Description Language for a domain-specific language such as JavaScript for web applications, HTML for documentation, C++ for high-performance code, etc. This is done by cross-language frameworks such as Apache Thrift or Google Protocol Buffers.

Gherkin

Gherkin is a language designed to define test cases to check the behavior of software, without specifying how that behavior is implemented. It is meant to be read and used by non-technical users using a natural language syntax and a line-oriented design. The tests defined with Gherkin must then be implemented in a general programming language. Then, the steps in a Gherkin program acts as a syntax for method invocation accessible to non-developers.

Other examples

Other prominent examples of domain-specific languages include:

Advantages and disadvantages

Some of the advantages:[2][3]

  • Domain-specific languages allow solutions to be expressed in the idiom and at the level of abstraction of the problem domain. The idea is that domain experts themselves may understand, validate, modify, and often even develop domain-specific language programs. However, this is seldom the case.[8]
  • Domain-specific languages allow validation at the domain level. As long as the language constructs are safe any sentence written with them can be considered safe.[citation needed]
  • Domain-specific languages can help to shift the development of business information systems from traditional software developers to the typically larger group of domain-experts who (despite having less technical expertise) have a deeper knowledge of the domain.[9]
  • Domain-specific languages are easier to learn, given their limited scope.

Some of the disadvantages:

  • Cost of learning a new language
  • Limited applicability
  • Cost of designing, implementing, and maintaining a domain-specific language as well as the tools required to develop with it (IDE)
  • Finding, setting, and maintaining proper scope.
  • Difficulty of balancing trade-offs between domain-specificity and general-purpose programming language constructs.
  • Potential loss of processor efficiency compared with hand-coded software.
  • Proliferation of similar non-standard domain-specific languages, for example, a DSL used within one insurance company versus a DSL used within another insurance company.[10]
  • Non-technical domain experts can find it hard to write or modify DSL programs by themselves.[8]
  • Increased difficulty of integrating the DSL with other components of the IT system (as compared to integrating with a general-purpose language).
  • Low supply of experts in a particular DSL tends to raise labor costs.
  • Harder to find code examples.

Tools for designing domain-specific languages

  • JetBrains MPS is a tool for designing domain-specific languages. It uses projectional editing which allows overcoming the limits of language parsers and building DSL editors, such as ones with tables and diagrams. It implements language-oriented programming. MPS combines an environment for language definition, a language workbench, and an Integrated Development Environment (IDE) for such languages.[11]
  • MontiCore is a language workbench for the efficient development of domain-specific languages. It processes an extended grammar format that defines the DSL and generates Java components for processing the DSL documents.[12]
  • Xtext is an open-source software framework for developing programming languages and domain-specific languages (DSLs). Unlike standard parser generators, Xtext generates not only a parser but also a class model for the abstract syntax tree. In addition, it provides a fully featured, customizable Eclipse-based IDE.[13] The project was archived in April 2023.
  • Racket is a cross-platform language toolchain including native code, JIT and Javascript compiler, IDE (in addition to supporting Emacs, Vim, VSCode and others) and command line tools designed to accommodate creating both domain-specific and general purpose languages.[14][15]

See also

References

  1. ^ Fowler, Martin; Parsons, Rebecca. "Domain Specific Languages". Retrieved 6 July 2019.
  2. ^ a b Marjan Mernik, Jan Heering, and Anthony M. Sloane. When and how to develop domain-specific languages. ACM Computing Surveys, 37(4):316–344, 2005.doi:10.1145/1118890.1118892
  3. ^ a b Diomidis Spinellis. Notable design patterns for domain specific languages. Journal of Systems and Software, 56(1):91–99, February 2001. doi:10.1016/S0164-1212(00)00089-3
  4. ^ Felleisen, Matthias; Findler, Robert Bruce; Flatt, Matthew; Krishnamurthi, Shriram; Barzilay, Eli; McCarthy, Jay; Tobin-Hochstadt, Sam (March 2018). "A Programmable Programming Language". Communications of the ACM. 61 (3): 62–71. doi:10.1145/3127323. S2CID 3887010. Retrieved 15 May 2019.
  5. ^ Heitmeyer, C. (1999). "Using the SCR* toolset to specify software requirements" (PDF). Proceedings. 2nd IEEE Workshop on Industrial Strength Formal Specification Techniques. IEEE. pp. 12–13. doi:10.1109/WIFT.1998.766290. ISBN 0-7695-0081-1. S2CID 16079058. Archived from the original (PDF) on 2004-07-19.
  6. ^ Shorre, D. V. (1964). "META II a syntax-oriented compiler writing language". Proceedings of the 1964 19th ACM national conference. pp. 41.301–41.3011. doi:10.1145/800257.808896. S2CID 43144779.
  7. ^ Carr, C. Stephen; Luther, David A.; Erdmann, Sherian (1969). "The TREE-META Compiler-Compiler System: A Meta Compiler System for the Univac 1108 and General Electric 645". University of Utah Technical Report RADC-TR-69-83. Archived from the original on February 1, 2020.
  8. ^ a b Freudenthal, Margus (1 January 2009). "Domain Specific Languages in a Customs Information System". IEEE Software: 1. doi:10.1109/MS.2009.152.
  9. ^ Aram, Michael; Neumann, Gustaf (2015-07-01). "Multilayered analysis of co-development of business information systems" (PDF). Journal of Internet Services and Applications. 6 (1). doi:10.1186/s13174-015-0030-8. S2CID 16502371.
  10. ^ Miotto, Eric. "On the integration of domain-specific and scientific bodies of knowledge in Model Driven Engineering" (PDF). Archived from the original (PDF) on 2011-07-24. Retrieved 2010-11-22.
  11. ^ "JetBrains MPS: Domain-Specific Language Creator".
  12. ^ "MontiCore".
  13. ^ "Xtext".
  14. ^ Tobin-Hochstadt, S.; St-Amour, V.; Culpepper, R.; Flatt, M.; Felleisen, M. (2011). "Languages as Libraries" (PDF). Programming Language Design and Implementation.
  15. ^ Flatt, Matthew (2012). "Creating Languages in Racket". Communications of the ACM. Retrieved 2012-04-08.

Further reading

External links

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