A recent blog post about using Rust for SQL parsing has sparked an engaging community discussion about the optimal programming languages and approaches for building parsers. While the original article praised Rust's capabilities, the ensuing debate reveals deeper insights into the trade-offs between different languages and methodologies.
The Case for Different Languages
Haskell and ML Family
The community strongly advocates for Haskell and ML-family languages when it comes to parser development. These languages offer natural support for algebraic data types (ADTs) and parser combinators, making them particularly well-suited for parsing tasks. As highlighted in one comment:
Haskell takes the cake here in terms of simplicity and legibility; it reads almost as clearly as BNF, and there is very little technical ceremony involved, letting you focus on the actual grammar of whatever it is you are trying to parse. Source
OCaml as a Middle Ground
Several developers suggest OCaml as an excellent compromise, offering the functional programming benefits without Rust's memory management complexity. OCaml's syntax is described as a friendly version of Haskell or Rust without lifetimes, and its historical use in compiler development (including an early version of the Rust compiler) adds credibility to its parser-building capabilities.
Practical Considerations
Tools and Libraries
The discussion highlights several valuable tools for parser development:
- Pest.rs - A PEG-based parser-generator library for Rust
- Logos - A lexer generator praised for its efficiency
- MegaParsec - A popular parsing library for Haskell
- Nom - A parser combinator library for Rust
Performance vs. Development Experience
While Rust offers excellent performance and zero-cost abstractions, the community notes that the borrow checker can become a significant hurdle, especially for complex parsing tasks. This has led many developers to adopt a two-phase approach:
- Prototype in a higher-level language (like OCaml or Haskell)
- Rewrite in Rust if performance becomes critical
Industry Perspectives
Real-world examples from companies like Prisma and Grafbase demonstrate that parser development isn't limited to programming language implementation. Many businesses require custom parsers for domain-specific languages, schema definitions, and query languages. These cases often benefit from Rust's performance characteristics, especially in high-traffic scenarios.
Debugging and Maintenance
A significant concern raised is the debugging experience, particularly with macro-heavy Rust code. Tools like cargo expand and rust-analyzer help developers understand macro expansions, but the community generally advises minimizing macro usage for better maintainability.
The debate reveals that while Rust is capable of building efficient parsers, the choice of language should depend on specific project requirements, including performance needs, team expertise, and maintenance considerations.
Sources: Original Discussion Article