This post explores integrating Rust into a Java project for performance-critical components using JNI. It details a practical example of optimizing a data serialization task, demonstrating significant speed improvements by leveraging Rust's efficiency and memory safety. The article walks through the process of creating a Rust library, exposing functions via JNI, and integrating it into the Java application. It acknowledges the added complexity of JNI but emphasizes the substantial performance gains as justification, particularly for CPU-bound operations. Finally, the author recommends careful consideration of the trade-offs between complexity and performance when deciding whether to adopt this hybrid approach.
mem-isolate is a Rust crate designed to execute potentially unsafe code within isolated memory compartments. It leverages Linux's memfd_create system call to create anonymous memory mappings, allowing developers to run untrusted code within these confined regions, limiting the potential damage from vulnerabilities or exploits. This sandboxing approach helps mitigate security risks by restricting access to the main process's memory, effectively preventing malicious code from affecting the wider system. The crate offers a simple API for setting up and managing these isolated execution environments, providing a more secure way to interact with external or potentially compromised code.
Hacker News users discussed the practicality and security implications of the mem-isolate crate. Several commenters expressed skepticism about its ability to truly isolate unsafe code, particularly in complex scenarios involving system calls and shared resources. Concerns were raised about the performance overhead and the potential for subtle bugs in the isolation mechanism itself. The discussion also touched on the challenges of securely managing memory in Rust and the trade-offs between safety and performance. Some users suggested alternative approaches, such as using WebAssembly or language-level sandboxing. Overall, the comments reflected a cautious optimism about the project but acknowledged the difficulty of achieving complete isolation in a practical and efficient manner.
C Plus Prolog is a project that embeds a Prolog interpreter within C++ code, allowing for logic programming within a C++ application. It aims to provide a seamless integration where Prolog predicates can be called directly from C++ and vice-versa, enabling the combination of Prolog's declarative power with C++'s performance and imperative features. The project leverages a modified version of SWI-Prolog, a popular open-source Prolog implementation, and offers a bidirectional interface for data exchange between the two languages. This facilitates the development of applications that benefit from both efficient procedural code and the logical reasoning capabilities of Prolog.
Hacker News users discussed the practicality and niche appeal of C Plus Prolog. Some expressed interest in its potential for specific applications like implementing rule engines or program analysis tools, while others questioned the performance implications of embedding Prolog within C++. One commenter suggested that a cleaner approach might involve interfacing Prolog with a language like Rust. Several pointed out the project's age and apparent inactivity, raising concerns about maintainability and documentation. The potential for improved tooling using C++-based IDEs was mentioned as a possible benefit. Overall, the discussion centered around the specialized nature of the project and the trade-offs involved in its approach.
This post explores optimizing Ruby's Foreign Function Interface (FFI) performance by using tiny Just-In-Time (JIT) compilers. The author demonstrates how generating specialized machine code for specific FFI calls can drastically reduce overhead compared to the generic FFI invocation process. They present a proof-of-concept implementation using Rust and inline assembly, showcasing significant speed improvements, especially for repeated calls with the same argument types. While acknowledging limitations and areas for future development, like handling different calling conventions and more complex types, the post concludes that tiny JITs offer a promising path toward a much faster Ruby FFI.
The Hacker News comments on "Tiny JITs for a Faster FFI" express skepticism about the practicality of tiny JITs in real-world scenarios. Several commenters question the performance gains, citing the overhead of the JIT itself and the potential for optimization by the host language's runtime. They argue that a well-optimized native library, or even careful use of the host language's FFI, could often outperform a tiny JIT. One commenter notes the difficulties of debugging and maintaining such a system, and another raises security concerns related to executing untrusted code. The overall sentiment leans towards established optimization techniques rather than introducing a new layer of complexity with a tiny JIT.
This blog post explores using Go's strengths for web service development while leveraging Python's rich machine learning ecosystem. The author details a "sidecar" approach, where a Go web service communicates with a separate Python process responsible for ML tasks. This allows the Go service to handle routing, request processing, and other web-related functionalities, while the Python sidecar focuses solely on model inference. Communication between the two is achieved via gRPC, chosen for its performance and cross-language compatibility. The article walks through the process of setting up the gRPC connection, preparing a simple ML model in Python using scikit-learn, and implementing the corresponding Go service. This architectural pattern isolates the complexity of the ML component and allows for independent scaling and development of both the Go and Python parts of the application.
HN commenters discuss the practicality and performance implications of the Python sidecar approach for ML in Go. Some express skepticism about the added complexity and overhead, suggesting gRPC or REST might be overkill for simple tasks and questioning the performance benefits compared to pure Python or using GoML libraries directly. Others appreciate the author's exploration of different approaches and the detailed benchmarks provided. The discussion also touches on alternative solutions like using shared memory or embedding Python in Go, as well as the broader topic of language interoperability for ML tasks. A few comments mention specific Go ML libraries like gorgonia/tensor as potential alternatives to the sidecar approach. Overall, the consensus seems to be that while interesting, the sidecar approach may not be the most efficient solution in many cases, but could be valuable in specific circumstances where existing Go ML libraries are insufficient.
Summary of Comments ( 32 )
https://news.ycombinator.com/item?id=43991221
Hacker News users generally expressed interest in the potential of Rust for performance-critical sections of Java applications. Several commenters pointed out that JNI comes with overhead, advising caution and profiling to ensure actual performance gains. Some shared alternative approaches like JNA and GraalVM's native image for simpler integration. Others discussed the complexities of memory management and exception handling across the language boundary, emphasizing the importance of careful design. A few users also mentioned existing projects using Rust with Java, indicating growing real-world adoption of this approach. One compelling comment highlighted that while the appeal of Rust is performance, maintainability should also be a primary consideration, especially given the added complexity of cross-language integration. Another pointed out the potential for data corruption if Rust code modifies Java-managed objects without proper synchronization.
The Hacker News post "Lessons from Mixing Rust and Java: Fast, Safe, and Practical" linking to a Medium article about integrating Rust into Java projects using JNI generated a moderate discussion with several insightful comments.
Several commenters discussed the complexities and performance implications of JNI. One user pointed out the overhead associated with JNI calls, especially for smaller operations, suggesting that it's crucial to benchmark and profile to ensure performance gains outweigh the overhead. They emphasized that JNI isn't a magic bullet and its effectiveness depends heavily on the specific use case. Another commenter echoed this sentiment, cautioning against premature optimization and advising careful consideration of whether the performance benefits justify the added complexity of JNI.
Another thread focused on alternatives to JNI, such as using message queues like Kafka or RabbitMQ for inter-process communication. This approach, while potentially introducing latency, offers better isolation and fault tolerance, particularly beneficial in distributed systems. This suggestion sparked a discussion about the trade-offs between performance and complexity when choosing between JNI and inter-process communication.
Some users shared their personal experiences with Rust and Java integration. One commenter mentioned using Rust for performance-critical sections of a Java application and achieving significant improvements, while another highlighted the challenges of managing memory and ensuring thread safety when working with JNI. They discussed the importance of understanding the memory models of both languages to avoid common pitfalls.
The discussion also touched upon the tooling and ecosystem surrounding Rust and Java integration. Commenters mentioned tools like JNA and SWIG as alternatives to hand-written JNI code, highlighting their ease of use but also potential performance limitations.
Finally, a few commenters expressed their enthusiasm for the combination of Rust and Java, praising Rust's performance and safety features while acknowledging the complexities of integrating the two languages. They emphasized the importance of careful design and thorough testing to ensure a successful integration.