Go vs. Python: What’s the Best Choice for Your Project?
The total number of programming languages worldwide is somewhere between 256 and over 8,000. Every developer has a personal favorite. But, only a few languages top the list when it comes to broad use across many different industries.
As with all languages, whether human or computer programming, new languages eventually emerge. Some are quickly adopted by a particular industry. But most are relegated to the programming language graveyard until some brave soul tries to resurrect one of them. Go may eventually be one of the ones that not only survives but universally thrives.
The Origins of Go
Go, also referred to as Golang, was developed by Google in 2007 and released to the public in 2012. As a spinoff of C/C++, it’s an open-source programming language that was built to increase efficiency, simplicity, and reliability for software development.
Given the size and scope of Google’s massive multicore processing needs and extensive distributed computational network, one could say that Go is the language of distributed computing (not the only language, but one that’s rapidly growing in popularity).
Go is lightweight and highly scalable. It’s the “Go-between” for compiled vs. interpreted languages: it’s constructed as a compiled language, but with interpreted language features.
Several tech companies, including Google (of course), have implemented the nascent language into their development infrastructure: Amazon, Facebook, Dropbox, IBM, Twitter, Uber, and hundreds more throughout the world. Additionally, Go’s usage has perpetually — and according to the Go team, significantly — increased over the past three years.
However, we’re not yet at a point where Go is a close competitor of Python. Blogs and articles may wax poetic about the superiority of one vs. the other, or whether Go will overtake Python as the “top programming language.” Even C++ still currently ranks higher than Go. Albeit, Go is the new kid on the block and is likely to evolve over time.
But, choosing Go vs. Python, or vice versa, ultimately comes down to a. what you’re building and b. developer preference (we’re a Python-focused shop, but we recognize the benefits of using other programming languages for specific purposes).
What are you building?
Arriving on the programming language scene in 1990, Python has a 30-year head start when compared to Go. Technically, Python is considered a scripting language, so that’s one of the initial differences between the two.
Since Python is a general-purpose and object-oriented language, with numerous libraries released over the years, it’s the language used in everything from software and web development to artificially intelligent systems. It has a proven track record.
But, considering it was created for a particular use within Google, e.g., boost developer/software engineering productivity in a multicore, distributed computation environment, we’ve yet to see the same utility expansion as Python. Go leans towards being object-oriented, but has fewer features for its objects.
With the dramatic rise in data science, machine learning, and AI, Python will continue to be the main go-to language for a greater number of operations. So, Python isn’t “Go-ing” to be kicked out of the programming (or scripting) language toolkit anytime soon.
Despite the utility differentials, Go is definitely faster than Python. This makes sense since it was built specifically for high-performance concurrency and parallelism in constructing and speedily deploying server-side scripts.
This returns us to the “what are you building” (and deploying) aspect. If you’re performing systems programming, then Go may be the better choice. Alternatively, Python is used for just about everything else (with some exceptions of course) in the world of software development and data science.
Over the next 20 years, as more developers and enterprises implement Go, and possibly expand its utility, then it may be a stronger competitor to Python. As of now, no. Python still reigns supreme — even though Go is faster.
If we view scalability from the perspective of concurrency and parallelism capabilities, Go was specifically designed for Google-sized scalability. Built-in to the language are concurrency primitives including Goroutines, Channels, and Select. These help with multiple task management and speedy scaling as task demands increase. It’s built to scale. As such, Go is innately superior for network servers with high scalability.
Python wasn’t originally designed for the multi-processing functions of a cloud environment. In terms of comparing its scalability within the same Go environment (i.e., cloud computing and a highly scalable network of servers), Go is going to be the winner. Python does have multi-threading capabilities, and a concurrency library, but these are workarounds for the global interpreter lock (GIL).
This comparison circles back around to the question “What do you want to do?” What Python currently lacks in scalability for cloud architecture, it compensates for by being a general-purpose and dynamic object-oriented language with broader usage capabilities.
Python has the advantage of a 30-year developmental history. Throughout this time, numerous developers have created a plethora of modules, packages, and libraries, and new projects are released on a regular basis. This speaks to its wider utility which, in turn, boosts its popularity amongst developers, data scientists, and programming newcomers.
Due to Go’s newness, along with its architectural “DNA” (similarity to C and its intended use in a multi-core network of servers, i.e. a cloud environment), its selection of libraries and packages is currently limited.
Which to Choose?
By now it should be clear that Go and Python have specific “talents” depending on your end goal. Python’s long been used for web development, IoT projects, automating various tasks, and is now the primary language for machine learning and AI. It’s a relatively simple language to learn, highly versatile, and there is extensive community support.
Go’s speed, scalability, simple code, and origination as a language for the cloud environment makes it a prime language for an extensive array of functionalities (as can be seen by the various well-known tech companies that use it, e.g., Uber uses it for their Geobase services).
There’s still much more that remains to be seen with Go’s wider applicability and potential for the same levels of Python-like industry adoption. As of now, Python continues to be one of the main languages used within all industries, and this has a solid likelihood of holding true for the near future.