Stories with Tag PEG

• How Janet's PEG module works

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  Posted: 2025-04-11 02:04:52

  Verbose tl;dr

  Janet's PEG module uses a packrat parsing approach, combining memoization and backtracking to efficiently parse grammars defined in Parsing Expression Grammar (PEG) format. The module translates PEG rules into Janet functions that recursively call each other based on the grammar's structure. Memoization, storing the results of these function calls for specific input positions, prevents redundant computations and significantly speeds up parsing, especially for recursive grammars. When a rule fails to match, backtracking occurs, reverting the input position and trying alternative rules. This process continues until a complete parse is achieved or all possibilities are exhausted. The result is a parse tree representing the matched input according to the provided grammar.

  This blog post provides a comprehensive explanation of the inner workings of Janet's Parsing Expression Grammar (PEG) module. It begins by highlighting the efficiency and simplicity of PEG parsers, particularly their linear parsing time and lack of separate lexing/scanning phases. The post then delves into the specific implementation within the Janet programming language.

  The core of Janet's PEG module revolves around a compiled bytecode representation of the grammar rules. This bytecode is executed by a virtual machine, allowing for rapid parsing. The post meticulously details the various bytecode instructions used in this process, including char, set, any, range, choice, sequence, repeat, not, behind, ahead, and grammar. Each instruction's functionality is thoroughly described, along with how it manipulates the input string and internal parser state.

  The char instruction, for example, checks for a specific character at the current input position. set checks for membership within a set of characters. any consumes any single character. range matches a character within a specified Unicode range. Control flow instructions like choice implement ordered choice, attempting each alternative rule sequentially until a match is found. sequence ensures that all sub-rules match in order. repeat allows for matching a rule multiple times, with variations for specifying minimum and maximum repetitions. Lookahead assertions are implemented via ahead (positive lookahead) and behind (positive lookbehind) which check for matches without consuming input. Negative lookahead is achieved with the not instruction. Finally, the grammar instruction enables recursive grammar definitions, allowing for complex nested structures.

  The post emphasizes the use of a backtracking mechanism to handle alternative rules and optional elements. This backtracking ensures that all possible parsing paths are explored until a successful match is found or all options are exhausted. The parser maintains an internal state that includes the current input position and a capture stack to store matched portions of the input. Upon successful parsing of a rule, the captured input fragments are assembled into a parse tree, representing the hierarchical structure of the matched input.

  The post concludes by highlighting the performance benefits of Janet's compiled PEG approach compared to interpreted PEG parsers. The bytecode execution provides a significant speed advantage. This combined with the flexibility and expressiveness of PEGs makes Janet's PEG module a powerful tool for parsing various data formats and creating domain-specific languages. The compact and understandable bytecode format further enhances the maintainability and debuggability of the parser.

  • Janet
  • PEG
  • Parsing Expression Grammar
  • parser
  • programming language
  • compiler
  • parsing
  • module
  • How-to
  • Tutorial

  Summary of Comments ( 2 )
  https://news.ycombinator.com/item?id=43649781

  Verbose tl;dr

  Hacker News users discuss the elegance and efficiency of Janet's PEG implementation, particularly praising its use of packrat parsing for memoization to avoid exponential time complexity. Some compare it favorably to other parsing techniques and libraries like recursive descent parsers and the popular Python library parsimonious, noting Janet's approach offers a good balance of performance and understandability. Several commenters express interest in exploring Janet further, intrigued by its features and the clear explanation provided in the linked article. A brief discussion also touches on error reporting in PEG parsers and the potential for improvements in Janet's implementation.

  The Hacker News post "How Janet's PEG module works" sparked a discussion thread with several insightful comments focusing primarily on parsing techniques, the Janet programming language, and comparisons to other parsing tools.

  One commenter highlighted the elegance of parsing expression grammars (PEGs) and their ability to express complex grammars concisely, contrasting them favorably with regular expressions for certain parsing tasks. They emphasized the power and flexibility of PEGs, particularly when dealing with structured data. They also expressed appreciation for the author's clear explanation of Janet's PEG implementation.

  Another commenter discussed the unique aspects of Janet as a programming language, particularly its embedded nature. They pointed out how this feature makes it well-suited for tasks where integrating a scripting language is beneficial. They also mentioned Janet's use of immutable data structures as a significant advantage.

  A subsequent comment delved into the implementation details of Janet's PEG module, touching upon memory management and performance considerations. This comment sparked a brief exchange about the trade-offs between different parsing approaches and their suitability for various applications.

  Further down the thread, a commenter compared Janet's PEG implementation to other parsing tools and libraries, mentioning tools like Parsec and LPEG (Lua Parsing Expression Grammars). They discussed the strengths and weaknesses of each, offering insights into their suitability for different parsing scenarios. This comparison provided a broader context for understanding Janet's approach.

  Several other comments expressed general appreciation for the article and the clarity of its explanation. Some users mentioned their interest in exploring Janet further based on the information presented.

  The overall sentiment in the comments was positive, with many users praising the article's educational value and the insights it provided into Janet's PEG implementation. The discussion offered a valuable perspective on parsing techniques, language design, and the trade-offs involved in different parsing approaches.

• Ohm: A user-friendly parsing toolkit for JavaScript and TypeScript

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  Posted: 2025-02-08 13:15:26

  Verbose tl;dr

  Ohm is a parsing toolkit designed for creating parsers in JavaScript and TypeScript that are both powerful and easy to use. It features a grammar definition syntax closely resembling EBNF, enabling developers to express complex syntax rules clearly and concisely. Ohm's built-in support for semantic actions allows users to directly embed JavaScript or TypeScript code within their grammar rules, simplifying the process of building abstract syntax trees (ASTs) and performing other actions during parsing. The toolkit provides excellent error reporting capabilities, helping developers quickly identify and fix syntax errors. Its flexible architecture makes it suitable for various applications, from validating user input to building full-fledged compilers and interpreters.

  Ohm is presented as a parsing toolkit designed for ease of use within JavaScript and TypeScript environments. It aims to simplify the often complex task of creating parsers, tools which analyze and interpret the structure of text according to specific grammatical rules. Ohm achieves this through a grammar definition language that is intended to be more readable and intuitive than traditional regular expressions or other parsing mechanisms. This grammar language allows developers to define the syntax of their target language in a clear and concise manner, closely mirroring the way the language is naturally structured.

  A key feature of Ohm is its focus on producing Abstract Syntax Trees (ASTs), structured representations of the parsed input. These ASTs facilitate further processing and manipulation of the parsed data, making it easier to extract meaning and perform operations on it. Ohm’s ASTs are designed to be easily traversable and manipulated using JavaScript, streamlining the integration of parsing into broader application logic.

  The toolkit provides built-in support for error handling and reporting. When a parsing error occurs, Ohm pinpoints the location of the error within the input and provides helpful diagnostic information. This assists developers in debugging their grammars and identifying issues in the input text quickly. Furthermore, Ohm offers the capability to customize error messages, allowing developers to tailor the feedback to their specific application needs.

  Ohm emphasizes a modular design, enabling the creation of reusable grammar components. This modularity promotes maintainability and reduces code duplication when working with complex grammars. It also simplifies the process of extending existing grammars to support new language features or variations.

  The website highlights Ohm’s use in diverse applications, including building domain-specific languages, creating interactive editors and code formatters, and implementing static analysis tools. This breadth of application showcases its versatility and suitability for various parsing tasks. Furthermore, the site provides extensive documentation, examples, and an interactive editor to facilitate learning and experimentation with the toolkit, contributing to its user-friendly nature. The interactive editor allows users to experiment with grammars and observe the resulting parse trees in real-time, providing a hands-on learning experience. This focus on practical application and accessible resources underscores Ohm’s commitment to simplifying the parsing process for developers.

  • javascript
  • TypeScript
  • parsing
  • parser
  • Toolkit
  • Ohm
  • grammar
  • language design
  • Programming Languages
  • compiler construction
  • DSL
  • domain specific language
  • PEG
  • Parsing Expression Grammar

  Summary of Comments ( 4 )
  https://news.ycombinator.com/item?id=42982755

  Verbose tl;dr

  HN users generally expressed interest in Ohm, praising its user-friendliness, clear documentation, and the power offered by its grammar-based approach to parsing. Several compared it favorably to traditional parser generators like PEG.js and nearley, highlighting Ohm's superior error messages and easier learning curve. Some users discussed potential applications, including building linters, formatters, and domain-specific languages. A few questioned the performance implications of its JavaScript implementation, while others suggested potential improvements like adding support for left-recursive grammars. The overall sentiment leaned positive, with many eager to try Ohm in their own projects.

  The Hacker News thread for "Ohm: A user-friendly parsing toolkit for JavaScript and TypeScript" contains several interesting comments discussing the library's merits, comparisons to other parsing tools, and potential use cases.

  Several commenters praise Ohm's ease of use and intuitive syntax. One user highlights its user-friendliness, contrasting it with the perceived complexity of traditional parser generators like PEG.js and nearley. They specifically appreciate the clear error messages, which are often a pain point when working with parsers. Another commenter echoes this sentiment, emphasizing how Ohm allows them to "think about the grammar" rather than getting bogged down in implementation details. This resonates with another user who describes Ohm as feeling more declarative than other parser generators.

  The discussion also delves into practical applications of Ohm. One commenter mentions using it for parsing custom configuration files, praising its ability to handle complex syntax with relative ease. Another suggests its potential for creating domain-specific languages (DSLs), a task often simplified by tools like Ohm. One user even shares a personal anecdote of using Ohm for a "toy language," highlighting its accessibility for experimentation and learning.

  Comparisons to other parsing tools are inevitable. One commenter draws a parallel to ANTLR, a powerful but more complex parsing tool, suggesting Ohm might be a better choice for smaller projects or those requiring a gentler learning curve. The discussion also touches on the performance aspects of Ohm, with one commenter inquiring about its speed relative to other JavaScript parsers. Another commenter brings up the topic of left recursion, a common parsing challenge, and inquires about Ohm's ability to handle it.

  Some commenters express interest in the educational aspects of Ohm. One user mentions its potential for teaching parsing concepts, appreciating its clear syntax and focus on grammar rules. Another suggests its suitability for beginners, contrasting it with the steeper learning curve associated with other parsing technologies.

  Finally, a few comments touch upon the project's maturity and community. One user expresses curiosity about the size of the Ohm community, while another inquires about the long-term maintenance and support of the project.