C++ vs Rust: What are the differences?
Developers describe C++ as "Has imperative, object-oriented and generic programming features, while also providing the facilities for low level memory manipulation". C++ compiles directly to a machine's native code, allowing it to be one of the fastest languages in the world, if optimized. On the other hand, Rust is detailed as "A safe, concurrent, practical language". Rust is a systems programming language that combines strong compile-time correctness guarantees with fast performance. It improves upon the ideas of other systems languages like C++ by providing guaranteed memory safety (no crashes, no data races) and complete control over the lifecycle of memory.
C++ and Rust can be categorized as "Languages" tools.
"Performance" is the top reason why over 146 developers like C++, while over 81 developers mention "Guaranteed memory safety" as the leading cause for choosing Rust.
Rust is an open source tool with 37.3K GitHub stars and 5.85K GitHub forks. Here's a link to Rust's open source repository on GitHub.
Lyft, OkCupid, and Twitch are some of the popular companies that use C++, whereas Rust is used by Dropbox, Sentry, and Roundscope Ukraine Labs. C++ has a broader approval, being mentioned in 199 company stacks & 371 developers stacks; compared to Rust, which is listed in 39 company stacks and 105 developer stacks.
What is C++?
What is Rust?
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To handle its growing observability needs, Postmates created and open sourced Cernan, a telemetry and logging aggregation server. Ceran is built on Rust and Lua, and can ingest data from many sources and then push or exposes what it’s collected to many destinations, or “sinks.” It can also create or manipulate in-flight data with programmable Lua filters.
Sentry's event processing pipeline, which is responsible for handling all of the ingested event data that makes it through to our offline task processing, is written primarily in Python.
For particularly intense code paths, like our source map processing pipeline, we have begun re-writing those bits in Rust. Rust’s lack of garbage collection makes it a particularly convenient language for embedding in Python. It allows us to easily build a Python extension where all memory is managed from the Python side (if the Python wrapper gets collected by the Python GC we clean up the Rust object as well).
One important decision for delivering a platform independent solution with low memory footprint and minimal dependencies was the choice of the programming language. We considered a few from Python (there was already a reasonably large Python code base at Thumbtack), to Go (we were taking our first steps with it), and even Rust (too immature at the time).
We ended up writing it in C. It was easy to meet all requirements with only one external dependency for implementing the web server, clearly no challenges running it on any of the Linux distributions we were maintaining, and arguably the implementation with the smallest memory footprint given the choices above.
Ruby NLP C++ Grammar #BNF
At FriendlyData we had a Ruby-based pipeline for natural language processing. Our technology is centered around grammar-based natural language parsing, as well as various product features, and, as the core stack of the company historically is Ruby, the initial version of the pipeline was implemented in Ruby as well.
As we were entering the exponential growth phase, both technology- and product-wise, we looked into how could we speed up and extend the performance and flexibility of our [meta-]BNF-based parsing engine. Gradually, we built the pieces of the engine in C++.
Ultimately, the natural language parsing stack spans three universes and three software engineering paradigms: the declarative one, the functional one, and the imperative one. The imperative one was and remains implemented in Ruby, the functional one is implemented in a functional language (this part is under the NDA, while everything I am talking about here is part of the public talks we gave throughout 2017 and 2018), and the declarative part, which can loosely be thought of as being BNF-based, is now served by the C++ engine.
The C++ engine for the BNF part removed the immediate blockers, gave us 500x+ performance speedup, and enabled us to launch new product features, most notably query completions, suggestions, and spelling corrections.
How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:
Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.
Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:
Maybe not in everybody focus but I do like programming for @z/OS, @z/Linux and @z/VM with C++ , Java and Assembler . Who else love to dig into control blocks and get a deep dive into system resources to run things in a high valuable way ? And also go all the way up to the application to enlight all the infrastructure features to it ?
Initially, I wrote my text adventure game in C++, but I later rewrote my project in Rust. It was an incredibly easier process to use Rust to create a faster, more robust, and bug-free project.
One difficulty with the C++ language is the lack of safety, helpful error messages, and useful abstractions when compared to languages like Rust. Rust would display a helpful error message at compile time, while C++ would often display "Segmentation fault (core dumped)" or wall of STL errors in the middle. While I would frequently push buggy code to my C++ repository, Rust enabled me to only even submit fully functional code.
Along with the actual language, Rust also included useful tools such as rustup and cargo to aid in building projects, IDE tooling, managing dependencies, and cross-compiling. This was a refreshing alternative to the difficult CMake and tools of the same nature.
At FlowStack we write most of our backend in Go. Go is a well thought out language, with all the right compromises for speedy development of speedy and robust software. It's tooling is part of what makes Go such a great language. Testing and benchmarking is built into the language, in a way that makes it easy to ensure correctness and high performance. In most cases you can get more performance out of Rust and C or C++, but getting everything right is more cumbersome.
C++ is used in Shiro (https://github.com/Marc3842h/shiro).
C++ is a high performance, low level programming language. Game servers need to run with fast performance to be able to reliably serve players across the globe.
Rust is used in Shirogane (https://github.com/Marc3842h/shirogane).
Shirogane is a osu! beatmap mirror built for shiro. We use Rust because of memory safe but still low level and high performance.
The most latency sensitive parts are written in C++. Due to our interconnected services architecture, we use either Python or C++ for each service, with the performance critical parts being C++14.
Used to write PHP extensions - AZTEC Decoder - License Driver scan - Axis2/C to PHP wrapper and Job-scheduler - Barbershop
Performance, zero-overhead abstractions and memory safety of the modern C++ language make this the perfect language for the project.