This blog post demonstrates how to achieve tail call optimization (TCO) in Java, despite the JVM's lack of native support. The author uses the ASM bytecode manipulation library to transform compiled Java bytecode, replacing recursive tail calls with goto instructions that jump back to the beginning of the method. This avoids stack frame growth and prevents StackOverflowErrors, effectively emulating TCO. The post provides a detailed example, transforming a simple factorial function, and discusses the limitations and potential pitfalls of this approach, including the handling of local variables and debugging challenges. Ultimately, it offers a working, albeit complex, solution for achieving TCO in Java for specific use cases.
A vulnerability (CVE-2024-8176) was discovered in libexpat, a popular XML parsing library, stemming from excessive recursion during the processing of deeply nested XML documents. This could lead to denial-of-service attacks by crashing the parser due to stack exhaustion. The issue was exacerbated by internal optimizations meant to improve performance, inadvertently increasing the recursion depth. The vulnerability affected all versions of expat prior to 2.7.0, and users are strongly encouraged to update. The fix involves limiting the recursion depth and implementing a simpler, less recursion-heavy approach to parsing these nested structures, prioritizing stability over the potentially marginal performance gains of the previous optimization.
Several Hacker News commenters discussed the implications of the expat vulnerability (CVE-2024-8176). Some expressed surprise that such a deeply embedded library like expat could still have these types of vulnerabilities, highlighting the difficulty of achieving perfect security even in mature codebases. Others pointed out that while the vulnerability allows for denial-of-service, achieving remote code execution would likely be very difficult due to the nature of the bug and its typical usage. A few commenters discussed the trade-offs between security and performance, with some suggesting that the potential for stack exhaustion might be an acceptable risk in certain applications. The potential impact of this vulnerability on various software that utilizes expat was also a topic of discussion, particularly in the context of XML parsing in web browsers and other critical systems. Finally, some commenters praised the detailed write-up by the author, appreciating the clear explanation of the vulnerability and its underlying cause.
Python 3.14 introduces an experimental, limited form of tail-call optimization. While not true tail-call elimination as seen in functional languages, it optimizes specific tail calls within the same frame, significantly reducing stack frame allocation overhead and improving performance in certain scenarios like deeply recursive functions using accumulators. The optimization specifically targets calls where the last operation is a call to the same function and local variables aren't modified after the call. While promising for specific use cases, this optimization does not support mutual recursion or calls in nested functions, and it is currently hidden behind a flag. Performance benchmarks reveal substantial speed improvements, sometimes exceeding 2x, and memory usage benefits, particularly for tail-recursive functions previously prone to exceeding recursion depth limits.
HN commenters largely discuss the practical limitations of Python's new tail-call optimization. While acknowledging it's a positive step, many point out that the restriction to self-recursive calls severely limits its usefulness. Some suggest this limitation stems from Python's frame introspection features, while others question the overall performance impact given the existing bytecode overhead. A few commenters express hope for broader tail-call optimization in the future, but skepticism prevails about its wide adoption due to the language's design. The discussion also touches on alternative approaches like trampolining and the cultural preference for iterative code in Python. Some users highlight specific use cases where tail-call optimization could be beneficial, such as recursive descent parsing and certain algorithm implementations, though the consensus remains that the current implementation's impact is minimal.
The blog post details methods for eliminating left and mutual recursion in context-free grammars, crucial for parser construction. Left recursion, where a non-terminal derives itself as the leftmost symbol, is problematic for top-down parsers. The post demonstrates how to remove direct left recursion using factorization and substitution. It then explains how to handle indirect left recursion by ordering non-terminals and systematically applying the direct recursion removal technique. Finally, it addresses mutual recursion, where two or more non-terminals derive each other, converting it into direct left recursion, which can then be eliminated using the previously described methods. The post uses concrete examples to illustrate these transformations, making it easier to understand the process of converting a grammar into a parser-friendly form.
Hacker News users discussed the potential inefficiency of the presented left-recursion elimination algorithm, particularly its reliance on repeated string concatenation. They suggested alternative approaches using stacks or accumulating results in a list for better performance. Some commenters questioned the necessity of fully eliminating left recursion in all cases, pointing out that modern parsing techniques, like packrat parsing, can handle left-recursive grammars directly. The lack of formal proofs or performance comparisons with established methods was also noted. A few users discussed the benefits and drawbacks of different parsing libraries and techniques, including ANTLR and various parser combinator libraries.
Testtrim, a tool designed to reduce the size of test suites while maintaining coverage, ironically struggled to effectively test itself due to its reliance on ptrace for syscall tracing. This limitation prevented Testtrim from analyzing nested calls, leading to incomplete coverage data and hindering its ability to confidently trim its own test suite. A recent update introduces a novel approach using eBPF, enabling Testtrim to accurately trace nested syscalls. This breakthrough allows Testtrim to thoroughly analyze its own behavior and finally optimize its test suite, demonstrating its newfound self-testing capability and reinforcing its effectiveness as a test suite reduction tool.
The Hacker News comments discuss the complexity of testing tools like Testtrim, which aim to provide comprehensive syscall tracing. Several commenters appreciate the author's deep dive into the technical challenges and the clever solution involving a VM and intercepting the vmexit instruction. Some highlight the inherent difficulties in testing tools that operate at such a low level, where the very act of observation can alter the behavior of the system. One commenter questions the practical applications, suggesting that existing tools like strace and ptrace might be sufficient in most scenarios. Others point out that Testtrim's targeted approach, specifically focusing on nested virtualization, addresses a niche but important use case not covered by traditional tools. The discussion also touches on the value of learning obscure assembly instructions and the excitement of low-level debugging.
This blog post explores the powerful concept of functions as the fundamental building blocks of computation, drawing insights from the book Structure and Interpretation of Computer Programs (SICP) and David Beazley's work. It illustrates how even seemingly complex structures like objects and classes can be represented and implemented using functions, emphasizing the elegance and flexibility of this approach. The author demonstrates building a simple object system solely with functions, highlighting closures for managing state and higher-order functions for method dispatch. This functional perspective provides a deeper understanding of object-oriented programming and showcases the unifying power of functions in expressing diverse programming paradigms. By breaking down familiar concepts into their functional essence, the post encourages a more fundamental and adaptable approach to software design.
Hacker News users discuss the transformative experience of learning Scheme and SICP, particularly under David Beazley's tutelage. Several commenters emphasize the power of Beazley's teaching style, highlighting his ability to simplify complex concepts and make them engaging. Some found the author's surprise at the functional paradigm's elegance noteworthy, with one suggesting that other languages like Python and Javascript offer similar functional capabilities, perhaps underappreciated by the author. Others debated the benefits and drawbacks of "pure" functional programming, its practicality in real-world projects, and the learning curve associated with Scheme. A few users also shared their own positive experiences with SICP and its impact on their understanding of computer science fundamentals. The overall sentiment reflects an appreciation for the article's insights and the enduring relevance of SICP in shaping programmers' perspectives.
Summary of Comments ( 21 )
https://news.ycombinator.com/item?id=43523741
Hacker News users generally expressed skepticism about the practicality and value of the approach described in the article. Several commenters pointed out that while technically interesting, using ASM to achieve tail-call optimization in Java is likely to be more trouble than it's worth due to the complexity and potential for subtle bugs. The performance benefits were questioned, with some suggesting that iterative solutions would be simpler and potentially faster. Others noted that relying on such a technique would make code less portable and harder to maintain. A few commenters appreciated the cleverness of the solution, but overall the sentiment leaned towards considering it more of a curiosity than a genuinely useful technique.
The Hacker News post "Tail Call Recursion in Java with ASM (2023)" has generated several comments discussing the article's approach to implementing tail call optimization in Java using the ASM bytecode manipulation library.
Several commenters express skepticism about the practical benefits and maintainability of this approach. One commenter points out that while intellectually interesting, using bytecode manipulation for this purpose introduces significant complexity and potential debugging challenges. They argue that the performance gains might not be worth the added difficulty in understanding and maintaining the code. This sentiment is echoed by others who question the real-world applicability of this technique, particularly in larger projects where readability and maintainability are paramount.
Another commenter suggests that relying on the JVM to perform tail call optimization, even if it's not guaranteed, is a more sensible approach. They argue that future JVM versions might implement tail call optimization more broadly, making this bytecode manipulation technique unnecessary. Furthermore, they highlight the risk of relying on undocumented JVM behavior.
Some commenters delve into more technical aspects of the implementation. One discusses the challenges of handling exceptions and the potential complexities that arise when trying to maintain proper stack traces with this approach. Another commenter explores the potential performance implications in more detail, considering different scenarios and workloads.
The discussion also touches upon alternative approaches to achieving similar results, such as using trampolines or iterative methods. One commenter points out the trade-offs between different techniques and emphasizes the importance of choosing the right approach based on the specific needs of the project.
Several users express appreciation for the author's work in exploring and demonstrating this technique, even if they are not convinced of its practical utility. They acknowledge the educational value of the article in showcasing the capabilities of ASM and providing insights into the inner workings of the JVM.
Finally, some comments delve into the limitations of the JVM's current approach to tail call optimization and the reasons why it hasn't been fully implemented yet. One commenter mentions the complexities related to security and reflection, which make implementing proper tail call optimization in the JVM a challenging endeavor.