Clojure vs C++: What are the differences?
Clojure: A dynamic programming language that targets the Java Virtual Machine. Clojure is designed to be a general-purpose language, combining the approachability and interactive development of a scripting language with an efficient and robust infrastructure for multithreaded programming. Clojure is a compiled language - it compiles directly to JVM bytecode, yet remains completely dynamic. Clojure is a dialect of Lisp, and shares with Lisp the code-as-data philosophy and a powerful macro system; 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.
Clojure and C++ can be primarily classified as "Languages" tools.
"It is a lisp", "Concise syntax" and "Persistent data structures" are the key factors why developers consider Clojure; whereas "Performance", "Control over memory allocation" and "Cross-platform" are the primary reasons why C++ is favored.
Clojure is an open source tool with 7.85K GitHub stars and 1.25K GitHub forks. Here's a link to Clojure's open source repository on GitHub.
Lyft, OkCupid, and Twitch are some of the popular companies that use C++, whereas Clojure is used by CircleCI, Groupon, and Soundcloud. C++ has a broader approval, being mentioned in 199 company stacks & 371 developers stacks; compared to Clojure, which is listed in 95 company stacks and 80 developer stacks.
What is Clojure?
What is C++?
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Stitch is run entirely on AWS. All of our transactional databases are run with Amazon RDS, and we rely on Amazon S3 for data persistence in various stages of our pipeline. Our product integrates with Amazon Redshift as a data destination, and we also use Redshift as an internal data warehouse (powered by Stitch, of course).
The majority of our services run on stateless Amazon EC2 instances that are managed by AWS OpsWorks. We recently introduced Kubernetes into our infrastructure to run the scheduled jobs that execute Singer code to extract data from various sources. Although we tend to be wary of shiny new toys, Kubernetes has proven to be a good fit for this problem, and its stability, strong community and helpful tooling have made it easy for us to incorporate into our operations.
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.
I adopted Clojure and ClojureScript because:
- it's 1 language, multiple platforms.
- Simple syntax.
- Designed to avoid unwanted side effects and bugs.
- Immutable data-structures.
- Compact code, very expressive.
- Source code is data.
- It has super-flexible macro.
- Has metadata.
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.
To complement Java. The REPL lets me interactively exercise Java code. I can write performant and safe libraries in Java, and then use them in Clojure. I also find the data-centric aspect of Clojure (excellent build-in structures, literal syntax for easily creating those structures, functions that act well on abstractions of those structures) good for data processing.
This fits a sweet spot between Ruby and Java.
We use Clojure mostly for its "Minority Report"-like interactive development in situations that require 'semi-automatic programming' (data inspection, admin tasks, API exploration, scrapers, etc.). We have also used Clojure successfully to build some components of our stack very quickly and reliably, in the backend and the frontend.
just started learning clojure, maybe around two weeks or so. i'm addicted. this is what i want to be working with and learning for the foreseeable future. the elegance of the language is refreshing. the community is really amazing. i've finally found a language that fits my passion for programming.
Clojure simplifies and reduces the coding efforts involved in creating CloudRepo. The fact that it runs in the JVM gives us access to all the libraries that we could ever need. Our code base is much smaller and easier to reason about than it would have been had we gone with pure Java.
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.
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.