Throughout my career, I have primarily focused on developing backend systems using Python. Despite dabbling in front-end work, I always found myself gravitating back toward API development. In order to expand my skill set, I have experimented with writing backend applications using different programming languages such as JavaScript/Typescript and Rust. I have tested various technologies and frameworks, some of which I found more enjoyable than others. Nonetheless, there remains one language that I have yet to explore: Go.
When I first started using Golang, it felt a little unfamiliar due to its simplicity, unique error handling, and some unusual design choices like struct tags. However, even before getting fully accustomed to these "quirks", I quickly realized several things about Go that I genuinely appreciate. Interestingly, these same features can be seen as challenges in my primary programming language, Python.
In this article, I will share my initial thoughts on using Go as a Python engineer. I will highlight the aspects of Go that I appreciate, which may not be as prominent in Python. These include:
- built-in tools
- packaging
- backend focus
- setting up Docker
- performance
By the end of this article, you will understand why Go is an excellent option for backend applications and how it compares to Python in terms of convenience.
Tooling
When it comes to building software, the main task is writing code. However, there are several potential problems that can arise during this process. These can include:
- incorrect formatting
- bugs
- common code issues
As engineers and humans, it's impossible to guarantee that our work is completely flawless. This is where automated tools like linters, unit tests, and static analysis can be incredibly useful in providing additional assistance.
When working with Python, formatting can be a challenge. While the most popular tool for formatting is black, it requires installation and configuration. However, configuration can be difficult as there are multiple ways to do it, with pyproject.toml being the default option according to PEP 518, but not all tools support it.
In terms of linting, prior to the release of ruff in 2022, Python developers had to rely on flake8 and its various plugins, which were inconvenient to configure. Thankfully, ruff now supports many features and can be configured via the pyproject.toml file.
Similarly, if you want more powerful testing capabilities than what is offered by the built-in unittest, you'll need to install and configure additional tools and addons. It's important to ensure that all of these components work together properly in your CI system as well.
When working with Go, the development experience can be quite different. Upon installation, you will have access to a wide range of tools to aid your local development.
Compared to Python, formatting rules in Go are less strict. For example, there is no fixed line length. However, Go has a built-in formatting tool that can be accessed through the go fmt command, eliminating the need for additional installation or configuration.
Another useful tool in Go is the go vet command, which helps to identify common coding mistakes such as unreachable code or useless comparisons. In addition, external linters like staticcheck can be used to detect bugs and performance issues with ease.
For testing purposes, Go provides a go test command that automatically discovers tests within your application and supports features such as caching and code coverage. However, if you require more advanced testing capabilities such as suites or mocking, you will need to install a toolkit like testify. Overall, while Go provides a highly effective testing experience, it's worth noting that writing tests in Python using pytest is arguably one of the most enjoyable testing experiences I have encountered across all programming languages.
It's also important to remember that there's no need to use type-checking tools such as mypy with Go, as it's a compiled and statically-typed language.
In summary, once you've installed Go, you'll have access to the necessary tools to support your code writing efforts. The next step is to focus on packaging and managing dependencies.
Packaging
Managing dependencies is crucial when building software. While Golang has an extensive standard library, more complex projects often require external packages. Luckily, Go provides excellent tools for effectively managing dependencies.
Go projects come with two files: go.mod and go.sum. The go.mod file is particularly crucial as it lists all the direct and indirect dependencies along with their versions. The go.sum file is automatically generated to serve as a checksum of the dependencies.
To install a package, you use the go get <package> command. The good news is that this command saves the installed packages in the $GOPATH/pkg/mod directory, so there is no need to manually create virtual environments or similar things.
In addition, you can use the go mod tidy command to ensure consistency between the go.mod file and the source code in the module. This command will add any missing module requirements needed to build the current module's packages and dependencies. If there are any unused dependencies, go mod tidy will remove them from go.mod.
Finally, publishing packages with Go is incredibly simple. To install them, you typically just need to use the repository link, such as go get github.com/tobias-piotr/gouth0. So, to publish your own package, all you have to do is properly set up your module in the go.mod file, create a public repository, and request the package at https://pkg.go.dev/. After a short period of time, your package will be available in the Go module index. That's it.
Now compare this with Python. By default, Python doesn't even have a checksum file. If you want more advanced features, you'll need to install a tool like poetry, which has many external dependencies. Managing project dependencies can be done through a file, but you'll need to create and maintain it yourself. Compared to Go, publishing packages to PyPI can also feel inconvenient.
Without further criticism of Python packaging, Go's packaging feels much simpler and more convenient. The next chapter will cover how Go's setup is beneficial when working with Docker.
Docker
When it comes to building web applications in the modern world, Docker is usually involved. It simplifies the process of setting up a project locally and deploying it to production.
Creating a Dockerfile for a Golang project is remarkably easy for two primary reasons: the Go tooling mentioned earlier and the fact that Go is a compiled language. Additionally, the combination of these two factors leads to a very standardized method of setting up a project. This means that if you copy a Dockerfile from one Golang project to another, there is a high probability that it will work without any issues. This is quite different from the situation in Python.
With all of this in mind, take a look at a Dockerfile from one of my Go projects:
FROM golang:1.20.6-alpine AS builder
EXPOSE 8080
WORKDIR /app
COPY go.mod go.sum ./
RUN go mod download
COPY . .
RUN CGO_ENABLED=0 GOOS=linux go build -o /main
# Local development
FROM builder as dev
ENV DEBUG=true
# Install air for hot reloading
RUN go install github.com/cosmtrek/air@latest
ENTRYPOINT ["air"]
# Production
FROM gcr.io/distroless/base-debian11 AS prod
WORKDIR /
COPY --from=builder /main /main
USER nonroot:nonroot
ENTRYPOINT ["/main"]
Without diving too deep into the Docker syntax, this config simply downloads dependencies, compiles code, and runs the binary file. It's important to note that Go, being a compiled language, improves this process. For example, you can use a tool like air to use the binary file and enable hot reloading while developing locally. Additionally, in production, a smaller base image can be used because no OS dependencies are required to start the binary file. This significantly reduces the image size of your app.
Starting a new project in Go is great because it's easy to set up a fully working and optimized Dockerfile. With tooling, packaging and containerizing covered, we can finally focus on the language's features.
Backend Focus
When I first started working with Go, I had doubts about its suitability for backend development. However, it didn't take long for me to realize that Go is actually designed with backend development in mind.
One of the main things that makes Go well-suited for backend development is its standard library. This library includes a variety of useful packages for tasks such as encoding, cryptography, and database management. Most importantly, it also includes packages for building HTTP servers, which means you can create a semi-sophisticated API without needing to install any additional dependencies. For instance, I was able to develop an authentication API for interacting with Auth0 using just one external package (for structured logging). You can view the code here: goloxy.
On top of that, there are many amazing packages available for developing web applications, including:
- web frameworks: Gin, Fiber, chi, Echo
- validation: validate, ozzo-validation, Valgo
- structured logging: zap, logrus, zerolog
- database interactions: GORM, sqlx, sqlc, Ent
My preferred combination is Echo/chi as a web framework, Valgo for validation, zap for structured logging, sqlx for database interactions, and Fx for dependency injection. This setup has been effective for most of my projects, though it may vary depending on specific requirements.
To highlight a difference with Python, configuring structured logging can be a challenge, even with a great framework like FastAPI. In an Echo app, I configure the zap logger in the following way:
e.Use(middleware.RequestLoggerWithConfig(middleware.RequestLoggerConfig{
LogProtocol: true,
LogMethod: true,
LogURI: true,
LogStatus: true,
LogRequestID: true,
LogRemoteIP: true,
LogLatency: true,
LogError: true,
LogValuesFunc: func(c echo.Context, v middleware.RequestLoggerValues) error {
logger.Info(
fmt.Sprintf("%s %s %s %v", v.Protocol, v.Method, v.URI, status),
zap.String("protocol", v.Protocol),
zap.String("method", v.Method),
zap.String("uri", v.URI),
zap.Int("status", status),
zap.String("request_id", v.RequestID),
zap.String("remote_ip", v.RemoteIP),
zap.String("latency", v.Latency.String()),
zap.Error(v.Error),
)
return nil
},
}))
In addition to creating a logger instance, which is usually just a few lines, this is all that is required to have complete control over the logger for each request. An example of setting up structured logging in a FastAPI app can be found using the structlog package at this link. This is surely overwhelming, even when you are fully aware of what's going on there.
To summarize, Go has excellent built-in and externally installable solutions for developing web apps. However, implementation is just the beginning. The next chapter will cover the performance of Golang code.
Performance
When examining the most popular uses of Go, examples include proxies, infrastructure management, and "typical" web development. One of the reasons for its popularity is the exceptional performance of Go, especially when compared to Python, which is known to have weaker performance.
There are numerous factors that contribute to Go's superior performance. such as:
- lightweight design, which reduces runtime overhead
- efficient garbage collection
- impressive concurrency model with Goroutines
- strong typing and static analysis
These are just a few examples of the reasons why Go is so well-performing. All of these features enable the creation of highly optimized applications that can efficiently handle large workloads and easily scale even further.
It's possible that one might argue that performance isn't crucial in many applications. In such cases, you could simply select Python, take code from a previous FastAPI project, base it around asyncio, and it should perform well enough. This argument may be valid. However, performance isn't just about speed; it also relates to resources.
You might not notice speed differences when comparing an app written in Golang to one in Python - the differences are in nanoseconds. However, you will definitely see the difference in resource usage. The app developed in Go will use significantly fewer resources. This will be evident through lower costs. I recommend watching Ben Davis' video where he explains how rewriting a SAAS project in Go resulted in reduced costs.
When it comes to performance, Python doesn't have the best reputation. This is largely due to the GIL, dynamic typing, and awkward concurrency model. While there are optimizations available, such problems are less frequent in Go. Therefore, when prioritizing performance, Go may be the better option.
Summary
Based on my experience with various technologies, I have some personal favorites. However, when building software, it is important to consider multiple factors before choosing the best tech stack. For backend development, Go stands out as an excellent option, especially when performance and cost are crucial. Nevertheless, it's not without any flaws. I believe that in modern Python, FastAPI and Pydantic are its strongest features for web development. They offer features like dependency injection, Swagger documentation, exceptional validation, and error handling right out of the box. I also find pytest to be the most well-designed test writing tool I've encountered. In summary, Go lacks some of the drawbacks that Python has, but Python has a wealth of features and libraries for app development. Ultimately, it depends on your specific needs and trade-offs.
Top comments (5)
I just couldn't get on with Python, I tried hard to like it but despite its popularity it just feels "scatty" for want of a better word! I came from C++, through Java to Go over the last 30 years and something about the strict nature of Go's demands just suits me. Compared to Python's zillions of 3rd party offerings there's not a huge amount for Go but what there is is well built and very functional, as a novice I found the 3rd party libs easy to pull apart and learn what's going under rather than pulling down libraries and ending up with a Node type dependency tree that sends shivers down your spine! Ha ha!
My colleague at work is a committed Python nutter, everything is Python and the biz people love it too so he's in good company but when you code 100 lines for a backend util and it's running it's tasks concurrently, safely and lightning fast, you can keep you Python.
I got so into Golang my wife even crocheted me my own gopher! Ha ha!!
I'm moving from Python to Go too. Pretty helpful
Very nice. Thank you. Can you please explain one part:
Additionally, in production, a smaller base image can be used because no OS dependencies are required to start the binary file. This significantly reduces the image size of your app.
Whats stopping you to use same base image for dev as well? Thats where I got lost a bit.
Hey DiM, thank you for your comment!
In general, nothing stops you to use the same images both locally and on production. However, in this example, air is installed locally to enable hot reloading for the web server. On top of that, I like to have possibility to exec into the locally working container and run some scripts from there. With extremely optimized production image, you would have limited capabilities when doing so. Plus, of course, locally you don't really have to worry too much about the image size, as long as you optimize the production one.
Let me know if that answers your question :)
Yes, it totally does. With this full explanation its clear. And yes, I agree. Who wouldnt :) Thank you