In 2009, the computer science world was blessed with two powerful tools: Golang and Node.js.
Golang is a procedural, multiparadigm, open-source programming language, created by Google developers that were unhappy with the existing languages. C, C++, and Java all failed to manage Google’s large network servers, so they created Go, a language derived from the power and syntax of C and based on safety, simplicity, and speed.
Now, it may seem silly to compare such seemingly unrelated tools, but they’re both known and created for web development, as well as other similar applications. To find out which is best for you, I’m going to answer which has better performance, salary, community, and application, so you can see how they meet your needs!
Golang vs Node.js: Which Is Better for Experienced Programmers? 🔗
Both tools have great benefits for experienced programmers.
Node.js’ main limitations for programmers are that it’s dynamically typed, which leads to poor error handling, and it runs in a single-threaded asynchronous environment, which can be confusing if you’re not comfortable with the style.
Golang’s strength lies in its streamlined concurrency and optimized memory management. Its clean code, garbage collector, and static type make error handling and writing manageable code a breeze.
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Node.js vs Golang Performance 🔗
This is where Node.js and Go’s differences really come into play.
One of the top marks for Golang is that it performs at a high level when it comes to speed and memory management, second only to low-level languages like C and Rust. With memory management tools like its garbage collector and native goroutines, Go programs thrive on a powerful yet minimalist structure.
In 2016, Uber even migrated from Node.js to Go to enhance their geofence look-up microservice, specifically because Go’s static typed and raw CPU performance suited Uber’s algorithms better, as well as the multithreading providing smoother resource usage.
The final results for Uber’s switch were 99.99% uptime and a peak load of 170,000 queries per second.
Node.js uses the V8 engine for fast processing and boasts a robust tech stack too.
A report was released from PayPal where they switched their back-end operations from Java to Node.js, which led to a 35% decrease in average response time, and pages were served 200ms faster.
In the end, Golang has better raw performance and stability, but in a workplace application, they both offer strong benefits.
Golang vs Node.js: Which Has a Higher Salary? 🔗
According to PayScale, Node.js developers earn a median salary of $91,198 a year in the US, and Go developers earn $117,394 a year.
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Go vs Node: Which Is Best for Web Development? 🔗
This is the field where both Node and Golang stake their claim.
NPM, the Node.js package manager, is the pièce de résistance of this whole operation, with 800,000 tools or “building blocks” dedicated to web development. However, the trick comes with navigating these blocks, as anyone can easily publish an NPM package, so it’s often difficult to find a trusted tool.
Oppositely, Golang was developed for big network servers and heavy computations that Node.js simply can’t handle as well. It’s concurrent, so it doesn’t have to cut corners to run multiple processes at a time, making it efficient and fast for enterprise applications.
You can build entire web servers inside Go without the use of any extra frameworks or 3rd party services, and while it doesn’t have 800,000 blocks, it does have amazing features like
go fmt, Godoc,
go mod, and
The takeaway, Golang is better for high-scale backend web development as it offers more flexibility and stability when it comes to building web servers, however, if you’re looking to run small projects, or to build front-end code bundles, then Node.js could be a more useful tool.
Go vs Node.js: Which Is Best for Machine Learning? 🔗
Node.js and Golang may seem like unusual choices for machine learning, given their extensive role in web development, but they both offer great resources.
TensorFlow.js is an open-source software library, and it allows you to create and use machine learning and deep learning models directly in Node.js. With it, you can create, train, and reuse models. You can also write the model in Python (a notable ML language) and import it to Node.js since Python has been supporting for training and research.
Node.js also calls for local running, which is an attractive feature if you’re worried about security risks and has GPU support for faster processing.
Golang is much more lucid. It’s designed to handle large data sets, so no worries about needing other languages for support. It also has a lot of machine learning tools already built into its library, and native support tools help programmers develop models faster and easier. As far as TensorFlow goes, it has support for Golang when it comes to using models in production, but training and testing has yet to receive proper tooling.
As a concurrent language, Go also allows for multiple machine learning algorithms and programs to run simultaneously with superb speed and possesses an intuitive syntax. However, it does often lack more advanced features like GPU support.
Node.js vs Golang: Which Has a Better Community? 🔗
This support is particularly useful when troubleshooting or learning new frameworks. Especially when it comes to more niche applications like machine learning, where it’s still quite new. That support ends up becoming integral to your programming process.
On the other hand, Golang’s community is still small and new.
It’s definitely growing in popularity, but it has yet to reach its full potential, especially in data science and machine learning. While the community may not have the experience to help with troubleshooting, you do have the opportunity to learn together.
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Go vs Node.js: The Final Verdict 🔗
When it comes is deciding which tool is best, it really depends on your background.
Either way, Go and Node.js both offer serious value to your skillset and provide the opportunity to level up career-wise.
Take action and learn to code:
Learn Golang: with our Learn Go. Our Go course covers everything from the basics to advanced concepts like concurrency.
All our courses are part of our wider computer science curriculum to take anyone from complete beginner to CS grad level.