Go vs Python in 2024: What to Choose?
Python is slow, and Go is fast. This belief is widespread in the IT community, but in reality, it is not always true. In this article, we compare Python and Golang in terms of performance, popularity, and versatility, and find out if one language can prevail in the near future.
At Evrone we use Go and Python in in various projects for their versatility, simplicity, and strong community support. I discuss about real-world examples and the advantages that Python and Go brings to projects, particularly in data analysis and automation.
Python is a high-level language with strict dynamic typing, fully object-oriented. It is an interpreted language, which means Python programs typically have higher memory consumption and lower speed compared to compiled languages. The language was developed by Guido van Rossum, and the first version was released in 1991.
Go is a compiled, multithreaded programming language with strict static typing, created specifically for programs operating under heavy loads on distributed systems and multicore processors. The technology is developed and maintained by Google, and the first version was introduced in 2009.
Application Areas & Use Cases
Both languages are generally versatile, but there are tasks for which each is better suited due to different reasons. Golang is better for projects initially designed for high load with a microservice architecture. This is what the language was designed for, and it handles such tasks best.
Python is often chosen for working with big data, mathematical models, and machine learning. It is this programming language that students of physics and mathematics faculties study, as its ecosystem includes many tools that facilitate computations. However, Python's features and application are not limited to these areas. The language was created with rapid development in mind, which is why businesses love it. The speed of developing a Python application can be three times faster than developing one in C/C++. Also, Python has simple syntax and a friendly community, which helps novice programmers quickly master the technology and start developing.
Differences Between the Languages
Syntax
Python has relatively simple and concise syntax, making the code quick to write and easy to read. Go also has clean, but slightly more verbose syntax. For example:
Python:
def greet(name):
print(f"Hello, {name}!")
greet("Alice")Golang:
package main
import "fmt"
func greet(name string) {
fmt.Printf("Hello, %s!\n", name)
}
func main() {
greet("Alice")
}
Safety
Go is a statically typed language, so many errors are detected at compile time. Additionally, Go has built-in features for error management and type safety checks. Python is dynamically typed, so errors can emerge during runtime, negatively affecting safety. The ‘eval’ function in Python executes a string as Python code. If the user can control the input passed to ‘eval’, it allows executing arbitrary code, opening the possibility for malicious commands.
Frameworks
Python is older than Go, so it has developed a vast ecosystem, including various web frameworks. Popular web development frameworks include Django, Flask, and FastAPI, while specialized ones for computations and machine learning include NumPy, SciPy, TensorFlow, and PyTorch. Go’s tools are more modest but can still meet specific needs: Gin, Echo, Revel for web development, gRPC and Go Kit for microservices, Cobra for creating command-line interfaces, and Viper for configuration management.
Speed
Go was created for high-performance and heavily loaded projects; it is a compiled language. Interpreted Python is traditionally considered a slow language, although tools like Cython or PyPy can improve performance, they usually do not reach Go's level. Additionally, Go has efficient memory management, which also impacts speed.
Recently, we worked on a project for a new service designed for high loads for one of our long-time partners. Initially, the client wanted to reduce infrastructure support costs and create a high-performance service capable of handling 100,000 messages per second. The service was to accept messages, process them according to complex rules, and then store them in a database. The initial processing involved validating complete and incomplete requests, with incomplete messages enriched with metadata.
The bottleneck was message handling; the processing service was written in Python, which is ideal for working with big data. To avoid expanding the stack and creating integration issues, we decided to try creating the high-load service on the same stack. Our team conducted research and wrote three test services in Python, Golang, and Rust to check performance. It turned out that Python lagged behind Go within one CPU minimally - by up to 300 messages per second, while Rust showed significantly higher performance. At a load of 100,000 RPS, Python's lag behind Go increased tenfold with an equal number of services.
We then selected frameworks that could best meet the service needs in each language. The Python ecosystem proved very useful here; we used the Granian framework, written in Rust, minimizing framework overhead. We optimized the tool slightly and proposed a solution that stably handled the required loads. This way, the client did not have to change the stack significantly, or find specialists to maintain the service in Go, and our solution did not require additional infrastructure costs. Additionally, maintaining our solution does not require special Python knowledge, and it can be easily managed by mid-level specialists.
Pros and Cons of Python and Golang
Python
Python's classic advantages include its syntax, which is quick to write, easy to read, and simple to learn, making it popular among beginner programmers. With a large community, Python has a highly developed ecosystem, and its standard library covers many tasks, from file handling to network programming and data processing. Traditionally, Python is chosen for prototyping due to its development speed and versatility—it can be used for classic web development, ML, and automation.
The main disadvantage is speed, although, as mentioned earlier, Python can sometimes deliver excellent results. Automatic garbage collection slows down memory-intensive tasks. GIL (Global Interpreter Lock) limits multithreading, affecting performance again. Errors are detected only during runtime, making testing and debugging harder.
Golang
Go is fast. It compiles directly into machine code, supports creating multithreaded applications through goroutines, automatically manages memory, and is designed for high performance. Go creates statically linked executables that do not depend on external libraries, simplifying deployment and application portability. Go’s syntax has strict rules, resulting in clean and easily readable code.
Go's standard library is excellent but less powerful than Python’s. The ecosystem is also less developed, meaning some tasks might require more time or custom solutions. Comparing Python and Golang, Go is less flexible—it is ideal for microservices and high-load applications, but static typing and rules do not offer as much freedom and can slow down development.
Migration from Python to Golang
The primary reason for switching from Python to Go is performance. The classic scenario involves creating an MVP in Python due to rapid development, but if the project grows quickly, requiring multitasking and scalability, Go becomes the better option.
There are different approaches to migration. The most complex is rewriting the application from scratch. This is challenging, time-consuming, and expensive but allows for maximum resource optimization. A hybrid approach is more common, where parts of the application are converted to Go, while some modules remain in Python. This is simpler and quicker than reworking everything, though it can result in a more complex application architecture and compatibility issues, which an experienced team can solve. Another approach involves creating a wrapper around Python code to use it in a Go application. This is the easiest but least practical method—the performance gain is minimal.
Demand for Python and Go
The maximum salary offered for Python programmers is $10,000 per month, while Golang developers can earn up to $15,000 before taxes. The are mostly more Python developer resumes than Golang developer resumes listed on different carrer portals.
The gap in job postings is understandable—Python is significantly older than Go, so there are more projects in it, and more people have learned it. Golang is younger, with a slightly higher entry barrier, so young developers rarely choose it as their first language. Additionally, there are many career options even within one stack: Data Science and ML/DL specialists’ salaries are high, and they often choose Python.
What does the business think? For now, Python also wins.
Python is more in demand; many existing projects are written in it, and companies are confident they can easily find employees. When choosing the Go stack, many companies face higher project development costs, fewer good developers in the market, and their salaries significantly exceed those of Python specialists. Go is interesting and trendy, but after initial assessments, many choose Python.
We should note that the most qualified specialists usually do not enter the open market and receive personal offers from HRs. However, most mid-level specialists also prefer to search for vacancies directly at companies they are interested in, and hiring offers often come through social media.
What to Choose: Python or Go?
There is no definitive answer. Like any technology, each has its pros and cons and unique features that can cause headaches in project maintenance if not planned correctly.
Go is currently a trendy language chosen by experienced programmers for features not found in traditional languages. And businesses are willing to pay for it—salaries for Go developers are higher than for Python developers.
On the other hand, Python remains a leader in popularity, with millions of applications already written in it that need support. Thus, the demand for such developers is higher. Improving skills can also increase earnings.
We advise our clients to carefully plan and compare all possible implementation paths to choose the technology that best meets the project’s needs. We are ready to provide our expertise and help develop technical requirements and a project roadmap.
If you are looking for a team to design and implement a high-load project, take care of scalability, build infrastructure, and provide support, you are in the right place! Fill out the form below, and our manager will contact you the same day.