The Dino programming language, developed by Vladimir Makarenko in 2013, has caught the attention of developers for its ambitious feature set combining object-oriented programming, functional programming, and scripting capabilities. However, recent discussions in the programming community have focused less on its impressive features and more on concerning performance benchmark results that suggest potential implementation problems.
Dino Language Key Features:
- Multi-paradigm support (OOP, imperative, functional programming)
- Static typing with type inference
- C++ TAPI (Transparent As Possible Interface)
- Built-in SQLite3 and wxWidgets support
- Advanced pattern matching and fibers
- Exception handling and threading support
- LLVM-based implementation
Performance Concerns Dominate Community Discussion
The most significant issue raised by community members centers around Dino's benchmark performance, particularly when compared to well-established languages like OCaml, Python, and Ruby. The benchmark results have left developers puzzled and questioning the quality of the language's implementation. One community member expressed confusion about the numbers, noting that OCaml is known for its speed, making the comparative results difficult to interpret.
The performance concerns become more troubling when considering that if Dino is indeed slower than Python or Ruby in certain scenarios, this could indicate serious underlying implementation issues. Some developers have speculated that the benchmarks might be using OCaml's interactive bytecode compiler and interpreter version, which is significantly slower than its optimized counterpart, potentially skewing the comparative results.
Performance Benchmark Concerns:
- Unclear comparative results against OCaml, Python, and Ruby
- Potential implementation quality issues suggested by slow performance
- Missing benchmark source code for community verification
- Speculation about OCaml bytecode interpreter affecting comparison accuracy
Feature-Rich Design Attracts Interest Despite Performance Issues
Beyond the performance concerns, Dino has garnered praise for its comprehensive feature set that addresses common gaps found in popular scripting languages. The language offers an appealing combination of static typing with type inference, extensive pattern matching capabilities, and modern features like fibers that are often missing from mainstream languages.
It seems to have a pretty high ratio of 'I use X because it's the only one that has Y' type features, all in one place. Very appealing to Python users, since it fills a few well known language gaps.
The language's design philosophy appears to target developers who frequently switch between languages to access specific features. For instance, developers who like Python but need Ruby's fibers, or those who prefer Ruby but want advanced pattern matching, might find Dino's unified approach attractive.
Implementation and Tooling Questions
Community discussions have also touched on the language's implementation details and tooling ecosystem. Dino is built with LLVM support and offers extensive integration with C++, SQLite3, and wxWidgets, suggesting a focus on practical application development rather than just academic interest.
However, the lack of readily available benchmark code and detailed performance analysis has made it difficult for the community to properly evaluate the language's real-world viability. This transparency gap has contributed to skepticism about the language's production readiness.
Conclusion
While Dino presents an interesting approach to combining multiple programming paradigms in a single language, the performance questions raised by the community highlight the importance of robust implementation and transparent benchmarking in language development. Until these concerns are addressed with clear performance data and accessible benchmark code, developers may remain hesitant to adopt Dino for serious projects, despite its appealing feature set.