C++ vs F#: What are the differences?
What is C++? 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.
What is F#? Strongly-typed, functional-first programming language for writing simple code to solve complex problems. F# is a mature, open source, cross-platform, functional-first programming language. It empowers users and organizations to tackle complex computing problems with simple, maintainable and robust code.
C++ and F# can be primarily classified as "Languages" tools.
"Performance" is the primary reason why developers consider C++ over the competitors, whereas "Pattern-matching" was stated as the key factor in picking F#.
F# is an open source tool with 2.09K GitHub stars and 341 GitHub forks. Here's a link to F#'s open source repository on GitHub.
Lyft, OkCupid, and Twitch are some of the popular companies that use C++, whereas F# is used by Olo, Huddle, and Property With Potential. C++ has a broader approval, being mentioned in 199 company stacks & 371 developers stacks; compared to F#, which is listed in 19 company stacks and 16 developer stacks.
What is C++?
What is F#?
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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.
Visual Studio Code worked really well for us as well, it worked well with all our polyglot services and the .Net core integration had great cross-platform developer experience (to be fair, F# was a bit trickier) - actually, each of our team members used a different OS (Ubuntu, macos, windows). Our production deployment ran for a time on Docker Swarm until we've decided to adopt Kubernetes with almost seamless migration process.
After our positive experience of running .Net core workloads in containers and developing Tweek's .Net services on non-windows machines, C# had gained back some of its popularity (originally lost to Node.js), and other teams have been using it for developing microservices, k8s sidecars (like https://github.com/Soluto/airbag), cli tools, serverless functions and other projects...
I've used .NET for many years, but only in recent years, after Microsoft introduced .NET Core, I've found a new love and excitement for the technology again. The main driver for us using .NET Core is not that it is cross platform compatible, open source or blazingly fast (which it is!), but the fact that we can use (what we consider) the best programming languages (mainly F# and C#) to carry out our jobs without sacrificing the other benefits.
Today we run most of our web infrastructure on .NET Core in Docker containers, deployed into a Kubernetes cluster which spans across multiple time zones in the Google Cloud and we couldn't be happier. Due to the portability of the .NET Core platform we are even able to develop many new services as serverless functions with F# which has become an absolute game changer.
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.