Python usability, performance of C and ownership model of Rust? Python superset Mojo has some serious mojo!
In the intricate landscape of data science and engineering, the choice of programming language plays a huge role in building efficient, scalable, and robust solutions. Code matters. At BigHub, we pride ourselves in being early adopters, and our relentless pursuit of innovation recently led us to be a part of a beta testing group of Mojo, Python superset.
Data science and software engineering combined
Mojo is a novel programming language that promises to significantly ease the interplay between Python and low-level languages such as Rust, C, C++, or JVM-based languages. At BigHub, we see its huge potential in its ability to do the heavy lifting when putting real-time models into production. It is a great tool for someone combining data science and software engineering, which is in short what BigHub does.
Mojo is engineered to tackle some of the bottlenecks we as data engineers often grapple with. Traditionally, optimizing code performance meant writing code in low-level language and then binding it to Python, and then packaging the library to run on different machine instruction sets. Mojo simplifies this ordeal with its intriguing features: backward compatibility, the ability to run both interpreted and compiled code side by side and a packaging system that mitigates cross-platform compilation challenges.
Rust as inspiration
One of the standout features of Mojo is its borrowing of the ownership model from Rust, alongside the utilization of zero-cost abstractions and well-structured compiler error messages. This amalgam of features empowers developers with a rare blend of performance, ease of debugging, and code safety.
Moreover, Mojo introduces an exciting concept of code optimization based on running small benchmarks to ascertain the optimal parameters before the compilation of the code. We at BigHub love the approach of driving performance while maintaining an agile development cycle.
With Mojo, we can write optimized code akin to C's performance, alongside normal Python code. This streamlined workflow alone enhanced our code optimization process.
What do we think?
Our experience with Mojo's beta version has been a joy ride so far, demonstrating a potential pathway to address real-time model operation needs, supercharging Python, which was heavily criticized for falling short in performance. Mojo potentially resolves this, making it an extremely valuable asset for our real-time models. We at BigHub are excited about the prospects that Mojo brings to the table and will continue to explore its potential. Stay tuned for our further discoveries and hands-on experience.