C++

C C++ I think programing with c/c++ give me more control about the system. It also drive me to learn more about the system knowledge.

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 ?

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:

https://eng.uber.com/distributed-tracing/

(GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)

Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark

Migrating from Python to Python is many times faster than migrating to another language, and it's well-known how to speed up Python to the speed levels of C/C++.

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.

I use Qt because you can do cross platform application development (window/macos/linux/android/ios and more).

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. C++

Core linear algebra and optimization functionality for deep analytics and machine learning C++