Mako vs. NumPy vs. Python vs. restify Comparison Chart

Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today. NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries. The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code. NumPy’s high level syntax makes it accessible and productive for programmers from any background or experience level. NumPy brings the computational power of languages like C and Fortran to Python, a language much easier to learn and use. With this power comes simplicity: a solution in NumPy is often clear and elegant.

A Node.js web service framework optimized for building semantically correct RESTful web services ready for production use at scale. restify optimizes for introspection and performance and is used in some of the largest Node.js deployments on Earth. Running at scale requires tracing problems back to their origin by separating noise from the signal. restify is built from the ground up with post-mortem debugging in mind. Staying true to the spec is one of the foremost goals of the project. You will see references to RFCs littered throughout GitHub issues and the codebase. restify is used by some of the industry's most respected companies to power some of the largest deployments of Node.js on planet Earth—the future of Node.js REST development. Setting up a server is quick and easy. Like many other Node. js-based REST frameworks, restify leverages a Sinatra-style syntax for defining routes and the function handlers that service those routes.