"Less Slow C++" offers practical advice for improving C++ build and execution speed. It covers techniques ranging from precompiled headers and unity builds (combining source files) to link-time optimization (LTO) and profile-guided optimization (PGO). It also explores build system optimizations like using Ninja and parallelizing builds, and coding practices that minimize recompilation such as avoiding unnecessary header inclusions and using forward declarations. Finally, the guide touches upon utilizing tools like compiler caches (ccache) and build analysis utilities to pinpoint bottlenecks and further accelerate the development process. The focus is on readily applicable methods that can significantly improve C++ project turnaround times.
Unikernel Linux (UKL) presents a novel approach to building unikernels by leveraging the Linux kernel as a library. Instead of requiring specialized build systems and limited library support common to other unikernel approaches, UKL allows developers to build applications using standard Linux development tools and a wide range of existing libraries. This approach compiles applications and the necessary Linux kernel components into a single, specialized bootable image, offering the benefits of unikernels – smaller size, faster boot times, and improved security – while retaining the familiarity and flexibility of Linux development. UKL demonstrates performance comparable to or exceeding existing unikernel systems and even some containerized deployments, suggesting a practical path to broader unikernel adoption.
Several commenters on Hacker News expressed skepticism about Unikernel Linux (UKL)'s practical benefits, questioning its performance advantages over existing containerization technologies and expressing concerns about the complexity introduced by its specialized build process. Some questioned the target audience, wondering if the niche use cases justified the development effort. A few commenters pointed out the potential security benefits of UKL due to its smaller attack surface. Others appreciated the technical innovation and saw its potential for specific applications like embedded systems or highly specialized microservices, though acknowledging it's not a general-purpose solution. Overall, the sentiment leaned towards cautious interest rather than outright enthusiasm.
The author reflects positively on their experience using Lua for a 60k-line project. They praise Lua's speed, small size, and ease of embedding. While acknowledging the limited ecosystem and tooling compared to larger languages, they found the simplicity and resulting stability to be major advantages. Minor frustrations included the standard library's limitations, especially regarding string manipulation, and the lack of static typing. Overall, Lua proved remarkably effective for their needs, offering a productive and efficient development experience despite some drawbacks. They highlight LuaJIT's exceptional performance and recommend it for CPU-bound tasks.
Hacker News users generally agreed with the author's assessment of Lua, praising its speed, simplicity, and ease of integration. Several commenters highlighted their own positive experiences with Lua, particularly in game development and embedded systems. Some discussed the limitations of the standard library and the importance of choosing good third-party libraries. The lack of static typing was mentioned as a drawback, though some argued that good testing practices mitigate this issue. A few commenters also pointed out that 60k lines of code is not exceptionally large, providing context for the author's experience. The overall sentiment was positive towards Lua, with several users recommending it for specific use cases.
DeepSeek's 3FS is a distributed file system designed for large language models (LLMs) and AI training, prioritizing throughput over latency. It achieves this by utilizing a custom kernel bypass network stack and RDMA to minimize overhead. 3FS employs a metadata service for file discovery and a scale-out object storage approach with configurable redundancy. Preliminary benchmarks demonstrate significantly higher throughput compared to NFS and Ceph, particularly for large files and sequential reads, making it suitable for the demanding I/O requirements of large-scale AI workloads.
Hacker News users discuss DeepSeek's new distributed file system, focusing on its performance and design choices. Several commenters question the need for a new distributed file system given existing solutions like Ceph and GlusterFS, prompting discussion around DeepSeek's specific niche targeting AI workloads. Performance claims are met with skepticism, with users requesting more detailed benchmarks and comparisons to established systems. The decision to use Rust is praised by some for its performance and safety features, while others express concerns about the relatively small community and potential debugging challenges. Some commenters also delve into the technical details of the system, particularly its metadata management and consistency guarantees. Overall, the discussion highlights a cautious interest in DeepSeek's offering, with a desire for more data and comparisons to validate its purported advantages.
Feldera drastically reduced Rust compile times for a project with over a thousand crates from 30 minutes to 2 minutes by strategically leveraging sccache. They initially tried using a shared volume for the sccache directory but encountered performance issues. The solution involved setting up a dedicated, high-performance sccache server, accessed by developers via SSH, which dramatically improved cache hit rates and reduced compilation times. Additionally, they implemented careful dependency management, reducing unnecessary rebuilds by pinning specific crate versions in a lockfile and leveraging workspaces to manage the many inter-related crates effectively.
HN commenters generally praise the author's work in reducing Rust compile times, while also acknowledging that long compile times remain a significant issue for the language. Several point out that the demonstrated improvement is largely due to addressing a specific, unusual dependency issue (duplicated crates) rather than a fundamental compiler speedup. Some express hope that the author's insights, particularly around dependency management, will contribute to future Rust development. Others suggest additional strategies for improving compile times, such as using sccache and focusing on reducing dependencies in the first place. A few commenters mention the trade-off between compile time and runtime performance, suggesting that Rust's speed often justifies the longer compilation.
Streak, a CRM built inside Gmail, is hiring Staff UI Engineers to build performant and scalable front-end features. They're seeking experienced engineers proficient in JavaScript/TypeScript, React, and state management solutions like Redux or MobX. The ideal candidate will architect and implement complex UI components, improve performance, mentor junior engineers, and contribute to the evolution of Streak's front-end architecture. This role emphasizes building a "local-first" user experience, ensuring responsiveness and reliability even with limited internet connectivity.
HN commenters discuss Streak's unusual tech stack (using Gmail as the frontend) and the potential challenges and benefits that come with it. Some express interest in the unique engineering problems, while others raise concerns about performance, scalability, and the reliance on a third-party platform. The "local-first" approach is questioned, with several commenters pointing out that data still resides primarily on Google's servers. There's also discussion about the compensation package, with some suggesting it's below market rate for senior engineers, particularly in high-cost areas. Finally, a few commenters share personal experiences with Streak, both positive and negative, regarding its functionality and usability.
This blog post reflects on four years of using Jai, a programming language designed for game development. The author, satisfied with their choice, highlights Jai's strengths: speed, ease of use for complex tasks, and a powerful compile-time execution feature called comptime. They acknowledge some drawbacks, such as the language's relative immaturity, limited documentation, and single-person development team. Despite these challenges, the author emphasizes the productivity gains and enjoyment experienced while using Jai, concluding it's the right tool for their specific needs and expressing excitement for its future.
Commenters on Hacker News largely praised Jai's progress and Jonathan Blow's commitment to the project. Several expressed excitement about the language's potential, particularly its speed and focus on data-oriented design. Some questioned the long-term viability given the lack of a 1.0 release and the small community, while others pointed out that Blow's independent funding allows him to develop at his own pace. The discussion also touched on Jai's compile times (which are reportedly quite fast), its custom tooling, and comparisons to other languages like C++ and Zig. A few users shared their own experiences experimenting with Jai, highlighting both its strengths and areas needing improvement, such as documentation. There was also some debate around the language's syntax and overall readability.
In 2004, a blogger explored creating a striped RAID array using four USB floppy drives under OS X. Driven by curiosity and a desire for slightly faster floppy access, they used the then-available Disk Utility to create a RAID 0 set. While the resulting "RAID" technically worked and offered a minor performance boost over a single floppy, the setup was complex, prone to errors due to the floppies' unreliability, and ultimately impractical. The author concluded the experiment was more of a fun exploration of system capabilities than a genuinely useful storage solution.
Hacker News users reacted with a mix of nostalgia and amusement to the 2004 article about creating a striped RAID array from USB floppy drives. Several commenters reminisced about the era's slow transfer speeds and the impracticality of the setup, highlighting the significant advancements in storage technology since then. Some appreciated the ingenuity and "mad science" aspect of the project, while others questioned its real-world usefulness. A few pointed out the potential data integrity issues with floppy disks, making the RAID setup even less reliable. The dominant sentiment was one of lighthearted appreciation for a bygone era of computing.
"JSX over the Wire" explores the idea of sending JSX directly from the server to the client, letting the browser parse and render it. This eliminates the need for separate HTML templates and API calls to fetch data, theoretically simplifying development and potentially improving performance by reducing data transfer and client-side processing. The author acknowledges this approach is unconventional and explores its potential benefits and drawbacks, including security considerations (XSS vulnerabilities) and the need for client-side hydration. Ultimately, the article concludes that while JSX over the wire is a fascinating concept with some appealing aspects, the existing ecosystem around established practices like server-side rendering and traditional APIs remains robust and generally preferred. Further research and experimentation are needed before declaring JSX over the wire a viable alternative for most applications.
Hacker News users discussed the potential benefits and drawbacks of sending JSX over the wire, as proposed in the linked article. Some commenters saw it as a potentially elegant solution for certain use cases, particularly for internal tools or situations where tight coupling between client and server is acceptable. They appreciated the simplified workflow and reduced boilerplate. However, others expressed concerns about security vulnerabilities (especially XSS), performance implications due to larger payload sizes, and the tight coupling making it harder to scale or adapt to different client technologies in the future. The idea of using a templating engine on the server was suggested as a more traditional and potentially safer approach. Several questioned the practicality and overall benefits compared to existing solutions, viewing it as a niche approach not suitable for most production environments.
The blog post "AES and ChaCha" compares two popular symmetric encryption algorithms, highlighting ChaCha's simplicity and speed advantages, particularly in software implementations and resource-constrained environments. While AES, the Advanced Encryption Standard, is widely adopted and hardware-accelerated, its complex structure makes it more challenging to implement securely in software. ChaCha, designed with software in mind, offers easier implementation, potentially leading to fewer vulnerabilities. The post concludes that while both algorithms are considered secure, ChaCha's streamlined design and performance benefits make it a compelling alternative to AES, especially in situations where hardware acceleration isn't available or software implementation is paramount.
HN commenters generally praised the article for its clear and concise explanation of ChaCha and AES, particularly appreciating the accessible language and lack of jargon. Some discussed the practical implications of choosing one cipher over the other, highlighting ChaCha's performance advantages on devices lacking AES hardware acceleration and its resistance to timing attacks. Others pointed out that while simplicity is desirable, security and correctness are paramount in cryptography, emphasizing the rigorous scrutiny both ciphers have undergone. A few commenters delved into more technical aspects, such as the internal workings of the algorithms and the role of different cipher modes. One commenter offered a cautionary note, reminding readers that even well-regarded ciphers can be vulnerable if implemented incorrectly.
Haskell offers a powerful and efficient approach to concurrency, leveraging lightweight threads and clear communication primitives. Its unique runtime system manages these threads, enabling high performance without the complexities of manual thread management. Instead of relying on shared mutable state and locks, which are prone to errors, Haskell uses software transactional memory (STM) for safe concurrent data access. This allows developers to write concurrent code that is more composable, easier to reason about, and less susceptible to deadlocks and race conditions. Combined with asynchronous exceptions and other features, Haskell provides a robust and elegant framework for building highly concurrent and parallel applications.
Hacker News users generally praised the article for its clarity and conciseness in explaining Haskell's concurrency model. Several commenters highlighted the elegance of software transactional memory (STM) and its ability to simplify concurrent programming compared to traditional locking mechanisms. Some discussed the practical performance characteristics of STM, acknowledging its overhead but also noting its scalability and suitability for certain workloads. A few users compared Haskell's approach to concurrency with other languages like Clojure and Rust, sparking a brief debate about the trade-offs between different concurrency models. One commenter mentioned the learning curve associated with Haskell but emphasized the long-term benefits of its powerful type system and concurrency features. Overall, the comments reflect a positive reception of the article and a general appreciation for Haskell's approach to concurrency.
Fibonacci hashing offers a faster alternative to the typical modulo operator (%) for distributing items into hash tables, especially when the table size is a power of two. It leverages the golden ratio's properties by multiplying the hash key by a large constant derived from the golden ratio and then bit-shifting the result, effectively achieving a modulo operation without the expensive division. This method produces a more even distribution compared to modulo with prime table sizes, particularly when dealing with keys exhibiting sequential patterns, thus reducing collisions and improving performance. While theoretically superior, its benefits may be negligible in modern systems due to compiler optimizations and branch prediction for modulo with powers of two.
HN commenters generally praise the article for clearly explaining Fibonacci hashing and its benefits over modulo. Some point out that the technique is not forgotten, being used in game development and hash table implementations within popular languages like Java. A few commenters discuss the nuances of the golden ratio's properties and its suitability for hashing, with one noting the importance of good hash functions over minor speed differences in the hashing algorithm itself. Others shared alternative hashing methods like "Multiply-with-carry" and "SplitMix64", along with links to resources on hash table performance testing. A recurring theme is that Fibonacci hashing shines with power-of-two table sizes, losing its advantages (and potentially becoming worse) with prime table sizes.
ArkType is a new TypeScript validation library boasting significantly faster performance than Zod, often cited as 100x faster. It leverages TypeScript's type system to generate highly optimized validators at compile time, resulting in minimal runtime overhead. ArkType aims for full compatibility with Zod's schema syntax, allowing for easy migration. It focuses on ergonomics and developer experience, offering features like autocompletion, type inference, and helpful error messages. While still in early development, ArkType presents a compelling alternative for TypeScript projects needing high-performance validation.
Hacker News users discuss ArkType's claimed 100x speed improvement over Zod, with many expressing skepticism and requesting benchmarks. Some acknowledge the potential value of a faster validator, especially for complex schemas, but question the practicality of the claimed performance difference. Several users point to the importance of schema complexity and input size in benchmarking, suggesting that simple schemas might not showcase ArkType's advantages. Others highlight Zod's strengths, such as its developer experience and comprehensive feature set, and wonder if ArkType can compete in those areas. The lack of clear, comparable benchmark data is a recurring theme, with users calling for more evidence to support the 100x claim. There's also interest in how ArkType handles asynchronous validation and its overall developer experience.
The cg_clif project has made significant progress in compiling Rust to C, achieving a 95.9% pass rate on the Rust test suite. This compiler leverages Cranelift as a backend and utilizes a custom ABI for passing Rust data structures. Notably, it's now functional on more unusual platforms like wasm32-wasi and thumbv6m-none-eabi (for embedded ARM devices). While performance isn't a primary focus currently, basic functionality and compatibility are progressing rapidly, demonstrating the potential for compiling Rust to a portable C representation.
Hacker News users discussed the impressive 95.9% test pass rate of the Rust-to-C compiler, particularly its ability to target unusual platforms like the Sega Saturn and Sony PlayStation. Some expressed skepticism about the practical applications, questioning the performance implications and debugging challenges of such a complex transpilation process. Others highlighted the potential benefits for code reuse and portability, enabling Rust code to run on legacy or resource-constrained systems. The project's novelty and ambition were generally praised, with several commenters expressing interest in the developer's approach and future developments. Some also debated the suitability of "compiler" versus "transpiler" to describe the project. There was also discussion around specific technical aspects, like memory management and the handling of Rust's borrow checker within the C output.
The blog post "You might not need WebSockets" argues that developers often prematurely choose WebSockets for real-time features when simpler, more efficient solutions exist. It highlights server-sent events (SSE) as a robust alternative for unidirectional communication from server to client, offering benefits like automatic reconnection and built-in event handling. While acknowledging WebSockets' bi-directional capabilities, the post emphasizes that many use cases only require server-to-client updates, making SSE a lighter and potentially better-performing choice. It encourages developers to carefully analyze their needs before defaulting to WebSockets and consider the reduced complexity and improved resource utilization that SSE can provide.
HN commenters largely agree with the author's premise that WebSockets are often overused for real-time updates when simpler solutions like HTTP long-polling or Server-Sent Events (SSE) would suffice. Several pointed out the added complexity of WebSockets, both in implementation and infrastructure, with one commenter noting the difficulty in scaling WebSocket connections. The benefits of SSE, particularly its simplicity and native browser support, were highlighted. Some suggested that the choice depends heavily on the specific use case, with WebSockets being more suitable for highly interactive applications like online games, while others argued that even these could be served efficiently with alternatives. A few commenters mentioned the advantages of WebSockets in terms of lower latency and bi-directional communication, but these were generally seen as niche benefits that don't justify the added complexity for most applications. The general consensus seemed to be: consider simpler options first, and only reach for WebSockets when absolutely necessary.
PostgreSQL's full-text search functionality is often unfairly labeled as slow. This perception stems from common misconfigurations and inefficient usage. The blog post demonstrates that with proper setup, including using appropriate data types (like tsvector for indexed documents and tsquery for search terms), utilizing GIN indexes on tsvector columns, and leveraging stemming and other linguistic features, PostgreSQL's full-text search can be extremely performant, even on large datasets. Furthermore, optimizing queries by using appropriate operators and understanding how ranking works can significantly improve search speed. The post emphasizes that understanding and correctly implementing these techniques are key to unlocking PostgreSQL's full-text search potential.
Hacker News users generally agreed with the article's premise that PostgreSQL full-text search can be performant if implemented correctly. Several commenters shared their own positive experiences, highlighting the importance of proper indexing and configuration. Some pointed out that while PostgreSQL's full-text search might not outperform specialized solutions like Elasticsearch or Algolia for very large datasets or complex queries, it's more than adequate for many use cases. A few cautioned against using stemming without careful consideration, as it can lead to unexpected results. The discussion also touched upon the benefits of using pg_trgm for fuzzy matching and the trade-offs between different indexing strategies.
Rust enums can surprisingly be smaller than expected. While naively, one might assume an enum's size is determined by the largest variant plus a discriminant to track which variant is active, the compiler optimizes this. If an enum's largest variant contains data with internal padding, the discriminant can sometimes be stored within that padding, avoiding an increase in the overall size. This optimization applies even when using #[repr(C)] or #[repr(u8)], so long as the layout allows it. Essentially, the compiler cleverly utilizes existing unused space within variants to store the variant tag, minimizing the enum's memory footprint.
Hacker News users discussed the surprising optimization where Rust can reduce the size of an enum if its variants all have the same representation. Some commenters expressed admiration for this detail of the Rust compiler and its potential performance benefits. A few questioned the long-term stability of relying on this optimization, wondering if changes to the enum's variants could inadvertently increase its size in the future. Others delved into the specifics of how this optimization interacts with features like repr(C) and niche filling optimizations. One user linked to a relevant section of the Rust Reference, further illuminating the compiler's behavior. The discussion also touched upon the potential downsides, such as making the generated assembly more complex, and how using #[repr(u8)] might offer a more predictable and explicit way to control enum size.
This blog post details the author's experience building a fast, in-browser analytics tool using DuckDB compiled to WebAssembly (Wasm), Apache Arrow for data transfer, and web workers for parallel processing. The post highlights the performance benefits of this combination, allowing for efficient querying of large datasets directly within the browser without server-side processing. By leveraging DuckDB's analytical capabilities within the browser, the application provides a responsive and interactive user experience for data exploration. The author also discusses the challenges encountered and solutions implemented, such as handling large data transfers between the main thread and the web worker using Arrow, ultimately achieving significant performance gains compared to traditional JavaScript-based solutions.
HN commenters generally praised the approach of using DuckDB, Arrow, and web workers for in-browser analytics. Several highlighted the potential of this combination for powerful client-side data processing and visualization, particularly for large datasets. Some pointed out that this method shifts the burden of computation to the client, potentially saving server costs and improving privacy. A few commenters offered alternative solutions or discussed the limitations of the current implementation, including browser compatibility and memory management. The performance benefits and ease of use compared to JavaScript solutions were recurring themes, with one commenter specifically mentioning its usefulness for interactive dashboards.
This blog post explores optimizing vector tile serving for speed. The authors benchmark various approaches using Go, focusing on minimizing the time spent serializing vector tile data into the Protocol Buffer (protobuf) format. They demonstrate that using a custom protobuf implementation tailored for vector tiles, specifically pg_featureserv's vtprotobuf, significantly outperforms general-purpose protobuf libraries. Furthermore, they show that pre-serializing tiles and storing them in MVT format, served directly by Nginx, yields the absolute fastest response times, eliminating per-request serialization overhead altogether. This pre-serialization tactic provides a simple yet effective caching strategy for static vector tile datasets.
Hacker News users discussed various aspects of serving vector tiles quickly. Several commenters highlighted the importance of simplification strategies, like using Geobuf instead of MVT and pre-filtering data based on zoom level. Performance comparisons between different tile servers like Martin and Tegola were mentioned, with some suggesting pg_tileserv as a good alternative. The use of flatgeobuf as a potentially faster format also generated interest. Several comments focused on PostGIS performance and the benefits of simplification for improving rendering speed, particularly on mobile devices. Finally, some users shared their own experiences with implementing fast tile serving solutions.
PlanetScale's Vitess project, which uses a Go-based MySQL interpreter, historically lagged behind C++ in performance. Through focused optimization efforts targeting function call overhead, memory allocation, and string conversion, they significantly improved Vitess's speed. By leveraging Go's built-in profiling tools and making targeted changes like using custom map implementations and byte buffers, they achieved performance comparable to, and in some cases exceeding, a similar C++ interpreter. These improvements demonstrate that with careful optimization, Go can be a competitive choice for performance-sensitive applications like database interpreters.
Hacker News users discussed the benchmarks presented in the PlanetScale blog post, expressing skepticism about their real-world applicability. Several commenters pointed out that the microbenchmarks might not reflect typical database workload performance, and questioned the choice of C++ implementation used for comparison. Some suggested that the Go interpreter's performance improvements, while impressive, might not translate to significant gains in a production environment. Others highlighted the importance of considering factors beyond raw execution speed, such as memory usage and garbage collection overhead. The lack of details about the specific benchmarks and the C++ implementation used made it difficult for some to fully assess the validity of the claims. A few commenters praised the progress Go has made, but emphasized the need for more comprehensive and realistic benchmarks to accurately compare interpreter performance.
uWrap.js is a lightweight (<2KB) JavaScript utility for wrapping text, boasting both speed and accuracy improvements over native browser solutions and other libraries. It handles various edge cases effectively, including complex characters, multiple spaces, and hyphenation. Designed for performance, it employs binary search and other optimizations to quickly calculate line breaks, making it suitable for dynamic content and frequent updates. The library offers customizable options for wrapping behavior, including maximum line width, indentation, and handling of whitespace.
Hacker News users generally praised uWrap.js for its performance and small size, directly addressing the issues with existing text wrapping libraries. Several commenters pointed out the difficulty of accurate text wrapping, particularly with handling Unicode and different languages, validating the author's claims. Some discussed specific use cases, including code editors and terminal emulators, where precise and fast text wrapping is crucial. A few users questioned the benchmarks and methodology, prompting the author to clarify and provide additional context. Overall, the reception was positive, with commenters acknowledging the practical value of a lightweight, high-performance text wrapping utility.
This blog post explores hydration errors in server-side rendered (SSR) React applications, demonstrating the issue by building a simple counter application. It explains how discrepancies between the server-rendered HTML and the client-side JavaScript's initial DOM can lead to hydration mismatches. The post walks through common causes, like using random values or relying on browser-specific APIs during server rendering, and offers solutions like using placeholders or delaying client-side logic until after hydration. It highlights the importance of ensuring consistency between the server and client to avoid unexpected behavior and improve user experience. The post also touches upon the performance implications of hydration and suggests strategies for minimizing its overhead.
Hacker News users discussed various aspects of hydration errors in React SSR. Several commenters pointed out that the core issue often stems from a mismatch between the server-rendered HTML and the client-side JavaScript, particularly with dynamic content. Some suggested solutions included delaying client-side rendering until after the initial render, simplifying the initial render to avoid complex components, or using tools to serialize the initial state and pass it to the client. The complexity of managing hydration was a recurring theme, with some users advocating for simplifying the rendering process overall to minimize potential mismatches. A few commenters highlighted the performance implications of hydration and suggested strategies like partial hydration or islands architecture as potential mitigations. Others mentioned alternative frameworks like Qwik or Astro as potentially offering simpler solutions for server-side rendering.
The article "Overengineered Anchor Links" explores excessively complex methods for implementing smooth scrolling anchor links, ultimately advocating for a simple, standards-compliant approach. It dissects common overengineered solutions, highlighting their drawbacks like unnecessary JavaScript dependencies, performance issues, and accessibility concerns. The author demonstrates how a concise snippet of JavaScript leveraging native browser behavior can achieve smooth scrolling with minimal code and maximum compatibility, emphasizing the importance of prioritizing simplicity and web standards over convoluted solutions. This approach relies on Element.scrollIntoView() with the behavior: 'smooth' option, providing a performant and accessible experience without the bloat of external libraries or complex calculations.
Hacker News users generally agreed that the author of the article overengineered the anchor link solution. Many commenters suggested simpler, more standard approaches using just HTML and CSS, pointing out that JavaScript adds unnecessary complexity for such a basic feature. Some appreciated the author's exploration of the problem, but ultimately felt the final solution was impractical for real-world use. A few users debated the merits of using the <details> element for navigation, and whether it offered sufficient accessibility. Several comments also highlighted the performance implications of excessive JavaScript and the importance of considering Core Web Vitals. One commenter even linked to a much simpler CodePen example achieving a similar effect. Overall, the consensus was that while the author's technical skills were evident, a simpler, more conventional approach would have been preferable.
Ferron is a new web server built in Rust, designed for speed and memory safety. It leverages tokio and hyper, focusing on efficiency and avoiding unnecessary allocations. The project emphasizes performance and aims to be a robust and reliable foundation for web applications, though it is still in early development. Its core features include request routing, middleware support, and static file serving. Ferron aims to provide a solid alternative to existing web servers by capitalizing on Rust's performance characteristics and safety guarantees.
HN commenters generally express enthusiasm for Ferron, praising its performance and memory safety due to Rust. Several highlight the potential of integrating with existing Rust libraries and the benefits of its modular design. Some discuss the challenges of asynchronous programming in Rust and offer suggestions for improvements like connection pooling and HTTP/2 support. A few express skepticism about the project's maturity and the real-world performance benefits compared to established solutions, but overall, the sentiment is positive and curious about the project's future development. Some insightful comments compare Ferron to other Rust web frameworks like Actix and Axum, noting potential advantages in simplicity and performance.
Research suggests supervisors often favor employees who moderately bend the rules, viewing them as resourceful and proactive. These "constructive nonconformists" challenge procedures in ways that benefit the organization, while still adhering to core values and demonstrating respect for authority. However, this tolerance has limits. Employees who consistently or significantly violate rules, exhibiting "destructive nonconformity," are viewed negatively and penalized. Supervisors perceive a key difference between rule-breaking that aims to improve the organization versus self-serving or malicious violations.
HN commenters generally agree with the study's findings that moderate rule-breaking is viewed favorably by supervisors, particularly when it leads to positive outcomes. Some point out that "rule-breaking" is often conflated with independent thinking, initiative, and a willingness to challenge the status quo, traits valued in many workplaces. Several commenters note the importance of context and company culture. In some environments, rule-breaking might be implicitly encouraged, while in others, it's strictly punished. A few express skepticism about the study's methodology and generalizability, questioning whether self-reported data accurately reflects supervisors' true opinions. Others highlight the potential downsides of rule-breaking, such as creating inconsistency and unfairness, and the inherent subjectivity in determining what constitutes "acceptable" rule-breaking. The "Goldilocks zone" of rule-breaking is also discussed, with the consensus being that it's a delicate balance, dependent on the specific situation and the individual's relationship with their supervisor.
The blog post explores how Python code performance can be affected by CPU caching, though less predictably than in lower-level languages like C. Using a matrix transpose operation as an example, the author demonstrates that naive Python code suffers from cache misses due to its row-major memory layout conflicting with the column-wise access pattern of the transpose. While techniques like NumPy's transpose function can mitigate this by leveraging optimized C code under the hood, writing cache-efficient pure Python is difficult due to the interpreter's memory management and dynamic typing hindering fine-grained control. Ultimately, the post concludes that while awareness of caching can be beneficial for Python programmers, particularly when dealing with large datasets, focusing on algorithmic optimization and leveraging optimized libraries generally offers greater performance gains.
Commenters on Hacker News largely agreed with the article's premise that Python code, despite its interpreted nature, is affected by CPU caching. Several users provided anecdotal evidence of performance improvements after optimizing code for cache locality, particularly when dealing with large datasets. One compelling comment highlighted that NumPy, a popular Python library, heavily leverages C code under the hood, meaning that its performance is intrinsically linked to memory access patterns and thus caching. Another pointed out that Python's garbage collector and dynamic typing can introduce performance variability, making cache effects harder to predict and measure consistently, but still present. Some users emphasized the importance of profiling and benchmarking to identify cache-related bottlenecks in Python. A few commenters also discussed strategies for improving cache utilization, such as using smaller data types, restructuring data layouts, and employing libraries designed for efficient memory access. The discussion overall reinforces the idea that while Python's high-level abstractions can obscure low-level details, underlying hardware characteristics like CPU caching still play a significant role in performance.
Nue.js is a new JavaScript framework focusing on extreme performance and minimal bundle size for complex web apps. It achieves this through a reactive core inspired by SolidJS and Svelte, compiling templates to optimized vanilla JavaScript, and offering built-in features like routing, state management, and SSR. The blog post demonstrates Nue's efficiency by showcasing a full-featured to-do MVC app with a bundle size smaller than a single React button, while maintaining excellent performance metrics. This makes it particularly suitable for situations where performance and low bandwidth consumption are critical, such as mobile-first development and slow networks.
Hacker News users discussed the performance benefits of Nue.js, particularly its small bundle size compared to React. Some expressed skepticism about the benchmark methodology and questioned whether the "lighter than a React button" claim held true in real-world scenarios. Others were interested in the framework's approach and appreciated its focus on simplicity and performance. Several commenters pointed out the difficulty of comparing frameworks based on microbenchmarks and emphasized the importance of overall developer experience and ecosystem maturity. The lack of TypeScript support was also mentioned as a potential drawback. A few users discussed the tradeoffs between using a smaller, less mature framework like Nue.js versus a more established option like React, Svelte, or Preact.
The Go Optimization Guide at goperf.dev provides a practical, structured approach to optimizing Go programs. It covers the entire optimization process, from benchmarking and profiling to understanding performance characteristics and applying targeted optimizations. The guide emphasizes data-driven decisions using benchmarks and profiling tools like pprof and highlights common performance bottlenecks in areas like memory allocation, garbage collection, and inefficient algorithms. It also delves into specific techniques like using optimized data structures, minimizing allocations, and leveraging concurrency effectively. The guide isn't a simple list of tips, but rather a comprehensive resource that equips developers with the methodology and knowledge to systematically improve the performance of their Go code.
Hacker News users generally praised the Go Optimization Guide linked in the post, calling it "excellent," "well-written," and a "great resource." Several commenters highlighted the guide's practicality, appreciating the clear explanations and real-world examples demonstrating performance improvements. Some pointed out specific sections they found particularly helpful, like the advice on using sync.Pool and understanding escape analysis. A few users offered additional tips and resources related to Go performance, including links to profiling tools and blog posts. The discussion also touched on the nuances of benchmarking and the importance of considering optimization trade-offs.
This JEP proposes preparing the Java platform for a future where final truly means final, eliminating the current capability of dynamically modifying final fields via reflection or other privileged code. The goal is to improve performance, security, and maintainability by enabling further runtime optimizations based on the immutability guarantees of final. This JEP focuses on identifying and mitigating compatibility risks posed by this change, such as existing frameworks and libraries that rely on altering final fields. It outlines an incremental approach involving a new JVM command-line option to enforce final field immutability, allowing developers to test and adapt their code before the restriction becomes the default and eventually permanent. This preparatory work will pave the way for a subsequent JEP to actually finalize the behavior of final.
HN commenters largely discuss the implications of making final mean truly final in Java. Some express concern about the performance impact, particularly for JIT compilers and escape analysis. Others question the practicality and benefit, given the existing workarounds like sealed classes and the potential disruption to existing codebases. A few commenters welcome the change, seeing it as a positive step toward stricter immutability and potentially simplifying some aspects of the language. There's also discussion around the nuances of the proposal, such as its impact on method overriding and the interaction with reflection. Several users highlight the complexity of implementing this change in the JVM and the potential for unforeseen consequences.
The paper "File Systems Unfit as Distributed Storage Back Ends" argues that relying on traditional file systems for distributed storage systems leads to significant performance and scalability bottlenecks. It identifies fundamental limitations in file systems' metadata management, consistency models, and single points of failure, particularly in large-scale deployments. The authors propose that purpose-built storage systems designed with distributed principles from the ground up, rather than layered on top of existing file systems, are necessary for achieving optimal performance and reliability in modern cloud environments. They highlight how issues like metadata scalability, consistency guarantees, and failure handling are better addressed by specialized distributed storage architectures.
HN commenters generally agree with the paper's premise that traditional file systems are poorly suited for distributed storage backends. Several highlighted the impedance mismatch between POSIX semantics and distributed systems, citing issues with consistency, metadata management, and performance bottlenecks. Some questioned the novelty of the paper's findings, arguing these limitations are well-known. Others discussed alternative approaches like object storage and databases, emphasizing the importance of choosing the right tool for the job. A few commenters offered anecdotal experiences supporting the paper's claims, while others debated the practicality of replacing existing file system-based infrastructure. One compelling comment suggested that the paper's true contribution lies in quantifying the performance overhead, rather than merely identifying the issues. Another interesting discussion revolved around whether "cloud-native" storage solutions truly address these problems or merely abstract them away.
Summary of Comments ( 18 )
https://news.ycombinator.com/item?id=43727743
Hacker News users discussed the practicality and potential benefits of the "less_slow.cpp" guidelines. Some questioned the emphasis on micro-optimizations, arguing that focusing on algorithmic efficiency and proper data structures is generally more impactful. Others pointed out that the advice seemed tailored for very specific scenarios, like competitive programming or high-frequency trading, where every ounce of performance matters. A few commenters appreciated the compilation of optimization techniques, finding them valuable for niche situations, while some expressed concern that blindly applying these suggestions could lead to less readable and maintainable code. Several users also debated the validity of certain recommendations, like avoiding virtual functions or minimizing branching, citing potential trade-offs with code design and flexibility.
The Hacker News post titled "Less Slow C++" (https://news.ycombinator.com/item?id=43727743) sparked a discussion with a moderate number of comments, largely focusing on the practicality and nuances of the advice offered in the linked GitHub repository.
Several commenters appreciated the author's effort to collect and present performance optimization tips. One user highlighted the value in consolidating such information, especially for those newer to C++, acknowledging that while experienced developers might be familiar with many of the tips, having them readily available in one place is beneficial.
However, a recurring theme in the comments was the caution against premature optimization. Multiple users emphasized that focusing on code clarity and correctness should precede optimization efforts. They argued that optimizing without proper profiling and understanding of actual bottlenecks can be counterproductive, leading to more complex code without significant performance gains. One commenter even suggested the title should be "Faster C++," as "Less Slow" implies a focus on fixing slowness rather than writing efficient code from the start.
Some commenters delved into specific points from the GitHub document. There was discussion around the use of
std::vectorversusstd::array, pointing out thatstd::arrayis often preferable for small, fixed-size collections due to its avoidance of heap allocation. Another discussion centered on the advice to avoid exceptions, with some agreeing on their performance overhead, especially when thrown frequently, while others argued that exceptions are crucial for error handling and shouldn't be dismissed solely for performance reasons.The topic of inlining also garnered attention. While the GitHub document recommends strategic use of inlining, some commenters elaborated on the compiler's role in inlining decisions. They highlighted that modern compilers are often better at determining which functions to inline, making explicit inlining less necessary and sometimes even detrimental.
Finally, a few commenters shared their own experiences and preferred optimization techniques, adding further depth to the conversation. One mentioned the importance of considering data locality and cache efficiency for performance-critical code.
Overall, the comments section provides a balanced perspective on C++ optimization. While acknowledging the usefulness of the compiled tips, the discussion emphasizes the importance of careful profiling, prioritizing code readability, and understanding the trade-offs involved in different optimization strategies. It serves as a reminder that blindly applying performance tweaks without proper consideration can often do more harm than good.