The blog post explores using e-graphs, a data structure representing equivalent expressions, to create domain-specific languages (DSLs) within Python. By combining e-graphs with pattern matching and rewrite rules, users can define custom operations and optimizations tailored to their needs. The post introduces Egglog, a Python library built on this principle, demonstrating how it allows users to represent and manipulate mathematical expressions symbolically, perform automatic simplification, and even derive symbolic gradients. This approach bridges the gap between the flexibility of Python and the performance of specialized DSLs, enabling rapid prototyping and efficient execution of complex computations.
K is a concise and powerful array-oriented programming language designed for speed and expressiveness. It prioritizes right-to-left evaluation, uses a small set of built-in symbols for a wide range of operations, and features implicit iteration over arrays. This allows complex data transformations to be expressed with minimal code. K leverages a dictionary-like structure called an associative array as its core data type, facilitating easy handling of key-value pairs. The language is intended for building high-performance applications, particularly in domains like finance where efficient data manipulation is crucial. Its terse syntax and powerful primitives make it ideal for rapid prototyping and concise expression of algorithms.
HN users discuss K's terse syntax, powerful array-oriented programming, and steep learning curve. Some find its conciseness appealing, comparing it favorably to APL and J, while others find it overly cryptic. Several commenters mention its historical influence on other languages and databases like kdb+. Performance is a recurring theme, with users noting K's speed and efficiency. The lack of free, readily available learning resources is also highlighted as a barrier to entry, though some point to the "K Book" mentioned in the submission as a useful starting point. The community appears small but dedicated, with experienced K programmers offering insights and resources to those curious about the language.
Summary of Comments ( 0 )
https://news.ycombinator.com/item?id=43398908
HN commenters generally expressed interest in Egglog and its potential. Several questioned its practicality for larger, real-world Python programs due to performance concerns and the potential difficulty of defining rules for complex codebases. Some highlighted the project's novelty and the cleverness of using e-graphs for optimization, drawing comparisons to other symbolic execution and program synthesis techniques. A few commenters also inquired about specific features, such as handling side effects and integration with existing Python tooling. There was also discussion around potential applications beyond optimization, including program analysis and verification. Overall, the sentiment was cautiously optimistic, acknowledging the early stage of the project but intrigued by its innovative approach.
The Hacker News post titled "Specializing Python with E-Graphs" (linking to https://vectorfold.studio/blog/egglog) generated a modest amount of discussion, with a handful of comments focusing on the technical aspects and potential applications of the Egglog system.
One commenter expressed excitement about the project, viewing it as a powerful tool for symbolic computation and program synthesis, particularly for tasks involving constraint solving and program optimization. They highlighted the potential for combining Egglog with other tools like SMT solvers and speculated about its usefulness in domains like robotics and compiler design.
Another comment focused on the performance characteristics of Egglog, questioning the efficiency of using Python as the foundation for such a system. They suggested that a language with more predictable performance, or even a custom virtual machine, might be a better choice for performance-critical applications. This concern sparked a brief discussion about the trade-offs between ease of use and performance, with another user pointing out that Python's extensive library ecosystem makes it an attractive platform for rapid prototyping and experimentation, even if it comes at a cost in performance.
One user discussed the applicability of Egglog in formal verification, wondering if it could be used to prove properties of programs or verify the correctness of hardware designs. They pointed to the growing interest in formal methods and suggested that tools like Egglog could play a crucial role in making formal verification more accessible to developers.
Another commenter made a connection between Egglog and relational programming paradigms, such as Datalog and Prolog. They discussed the potential benefits of using a declarative approach for expressing computations and how Egglog's e-graph-based rewriting system could offer advantages in terms of expressiveness and efficiency compared to traditional relational systems.
Finally, one user expressed a desire for more detailed examples and tutorials demonstrating the practical use of Egglog. They suggested that concrete examples, especially those relevant to specific application domains, would be helpful in understanding the capabilities and limitations of the system and in attracting a wider audience.
Overall, the comments reflect a generally positive sentiment towards Egglog, with many commenters recognizing its potential for various applications. However, there were also some practical concerns raised about performance and the need for more comprehensive documentation and examples.