C vs Rust: Can the Flux Library Make Rust a True Contender in Low-Latency HFT?
- Bryan Downing
- 3 days ago
- 5 min read
C vs Rust: Can the Flux Library Make Rust a True Contender in Low-Latency HFT?
For decades, the world of ultra-low-latency and high-frequency trading (HFT) has been the undisputed domain of C and C++. The logic was simple: to achieve the fastest possible execution, you need to be as close to the metal as possible, with manual memory management and zero abstractions that might introduce unpredictable latency. The C vs Rust debate in this context was often short, with Rust’s safety guarantees seen as an unnecessary trade-off. However, a new generation of Rust libraries, exemplified by the experimental project Flux, is challenging this long-held belief, making a compelling case for Rust as a viable, and perhaps even superior, alternative.

Introducing Flux: A Rust Library Forged in the Spirit of Speed
Flux is a high-performance, copy-optimized message transport library written in Rust. According to its developers, it is designed specifically for ultra-low-latency applications that demand maximum throughput. It is not a ground-up reinvention but is built upon the battle-tested patterns and practices of legendary low-latency Java frameworks like LMAX Disruptor and Aeron, adapted and enhanced with modern Rust optimizations.
At its core, Flux provides the essential building blocks for any high-performance trading system:
Inter-Process Communication (IPC): For lightning-fast, same-host communication between different components of a trading strategy.
UDP Transport: For general-purpose, high-frequency network messaging.
Reliable UDP (RUDP): For mission-critical data streams where message loss is not an option, implemented with NAK-based retransmission.
The project is explicitly labeled as experimental and in a "Research Preview" stage, but its architecture and stated goals aim directly at the heart of what HFT engineers care about.
The Architecture of Low Latency
Flux achieves its performance through a combination of sophisticated design patterns and low-level hardware optimizations, the same kind of techniques that C++ developers have painstakingly implemented for years.
Lock-Free Ring Buffer: The core of the library is a lock-free ring buffer with single-writer, multiple-reader semantics. This avoids kernel-level locking, a common source of latency and jitter, allowing different processes or threads to communicate with minimal overhead. The buffer is designed to support over a million slots with microsecond latencies.
Cache-Friendly Design: Data structures are cache-line aligned and padded. This ensures that when data is pulled into the CPU's fastest caches, it isn't inadvertently evicted by unrelated, adjacent data—a subtle but critical optimization for performance.
Batch Processing: Flux is built around batching operations. By claiming, processing, and publishing messages in batches, the fixed costs of atomic operations and memory barriers are amortized over many messages, dramatically increasing throughput.
Platform-Specific Optimizations: The library goes further by leveraging platform-specific features. On Linux, it is designed with NUMA (Non-Uniform Memory Access) awareness to ensure memory is allocated on the same CPU node that will be processing it. On macOS, it uses thread priority tuning to get the most out of Apple Silicon. Planned features like io_uring on Linux promise to enable true kernel-bypass zero-copy networking, a holy grail of low-latency I/O.
Preliminary benchmarks on Apple M1 hardware show IPC throughput reaching over 30 million messages per second, demonstrating that the library is capable of operating in the performance territory required by HFT.
The Great Debate: C vs Rust for High-Frequency Trading
This brings us to the central question: can a library like Flux, and Rust by extension, truly compete with C++?
The traditional argument for C++ is its absolute control. Developers can manage memory layout precisely, dictate allocation strategies, and use a mature ecosystem of tools to wring out every last nanosecond of performance. There is no garbage collector to cause unexpected pauses, and the language places no constraints on what the programmer can do. This power, however, comes with immense responsibility. Memory leaks, data races, and buffer overruns are constant threats that can lead to subtle bugs or catastrophic system failures.
This is where the C vs Rust debate finds its modern footing. Rust offers a compelling proposition: safety without sacrificing speed.
Zero-Cost Abstractions and Fearless Concurrency: Rust's compiler is famous for its "borrow checker," which enforces strict memory safety and thread safety rules at compile time. This eliminates entire categories of bugs common in C++ without any runtime performance penalty—a concept known as a "zero-cost abstraction."
Disciplined Use of unsafe: Rust acknowledges that some ultra-low-level operations, like the memory-mapped I/O used in Flux, require breaking these safety rules. It allows this through the unsafe keyword. However, this explicitly marks and isolates the sections of code that require the highest level of scrutiny. The Flux project documents its unsafe blocks and their justifications in a SAFETY.md file, promoting a disciplined approach that is often harder to enforce in a sprawling C++ codebase.
Performance Parity: As Flux demonstrates, the same low-level optimization techniques available to C++ programmers can be implemented in Rust. From hardware-accelerated CRC32 calculations to cache-line padding and NUMA awareness, Rust provides the tools to achieve performance that is directly comparable to C++. The result is a system that can be just as fast as its C++ counterpart but is significantly safer overall.
For HFT, this means developers can focus more on strategy logic and less on chasing down elusive memory corruption bugs. The compiler becomes a partner in ensuring correctness, which is invaluable in a domain where a single bug can have enormous financial consequences.
From Theory to Trading Floor: Building Real-World Systems
Having a high-performance messaging library like Flux is a critical first step, but it's only one piece of a complex puzzle. A complete quantitative trading system requires much more: market data connectors, strategy logic, order management, risk analysis, and backtesting frameworks. The C vs Rust debate is important at the engine level, but successful trading is built on a complete, robust software stack.
For those looking to master the practical skills required to build these systems, the journey goes beyond choosing a language. It involves learning the architectural patterns and quantitative strategies that drive modern markets. This is where a focused, project-based learning path becomes essential.
The Quant Elite Programming Membership is designed for this exact purpose. It provides aspiring quants and developers with a library of over 50 hands-on projects that teach everything from foundational quant strategies to advanced market making. With a focus on the industry-standard languages of C++ for high-performance execution and Python for research and data analysis, it offers the comprehensive skill set needed to turn theoretical knowledge into a successful career. Members learn to build the very systems that would use a library like Flux as their core, bridging the gap between low-level engineering and profitable trading.
In conclusion, while C++ remains the long-reigning king of HFT, the C vs Rust narrative is rapidly changing. Projects like Flux prove that Rust is no longer just a language for safe systems programming but a serious contender for the most demanding low-latency applications. It offers a powerful combination of performance that rivals C++ and a safety model that is in a class of its own. As the Rust ecosystem continues to mature, the choice between the two may become less about capability and more about a philosophical preference for building fast, correct, and robust systems.
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