In Agriya, We have been exercising python-based web app development practices all these years, and we’ve deployed a diverse range of web and mobile products as well. Throughout these years, we felt that Python is a great language and is ideal for one time scripts or if you need to parse random, not structured data. Moreover, there is no other better high-level programming language than Python when it comes to general purpose tasks such as data mining, and big data facilitation. However, we had disapprovals with using Python in terms of its performance. Especially, it lacks speed, GIL, dynamic typing and non-built-in concurrency support. This inspired us to adopt Golang, which is concurrent, much faster and efficient.

Here, in this document, we solely render the potential reasons why you should shift from Python to Go.

What Makes Golang Better than Python? – How things will get better if you adopt Golang?

It has been decades since the inception of Python, whilst Go is comparatively new and incorporates many useful factors that can facilitate the developer’s demands in a much efficient manner. Below we list out the major factors that set Golang development apart from conventional Python-oriented practices.

Static Typing

One of the major drawbacks we found in Python is that it is both interpreted and dynamically typed. Hence and so the type will be checked only during the stage of execution.  In contrary, Go is statically typed, and because the types will be checked before run time. Also, it is known that statistically typed languages have better performance at run-time due to not needing to check types dynamically while executing.

JSON Processing

JSON or Javascript Object Notation is a standard method of sending and receiving information. And For any data science project, JSON parsing is crucial. However, Go doesn’t have classes and thus make use of structs, interfaces and methods. Structs are really helpful in a statically typed language similar to Go; for it gives clarity on what types we can expect to work with. Also, one should know all the data types of fields you are parsing in JSON.  And if the data types are all mixed up, Python code will stay same, whereas Go will undergo some deliberate changes by making use of interface{}, type assertion etc. Above all, the error handling in Python is less efficient and can easily be missed. With regards to exception handling in Go, err is created if there is any problem with Unmarshal function in JSON.

Interfaces and Type Assertions

Interfaces were the pain point with using Python as our default developing medium. And as we switched to Go, the conception of pointers created some confusion. However, in Go, pointers are merely used to give users more control of the variable.

When it comes to type assertions, it enables you to transform the interface into a much concrete type. And if you are already aware of the underlying type, you can easily assert it, else you can try diverse type assertions until you find the ideal one. Moreover, since the Golang web framework is a strongly written coding language, interfaces help Go to interpret its data types in many possible ways. Interfaces can be both types as well as a set of methods.

Concurrency and Channels

Concurrency is nothing rather than blending independently executing computations, it is the most efficient way to write clean code that interacts well with the real world. Goroutines are the Go’s lightweight approach to threading, whereas channels imply the way to communicate with Goroutines. Now, how do this Goroutines and channels create an impact in terms of effective concurrency?

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