Learning Golang

Lately I’ve had the opportunity to spend some time with Go. I’ve been interested in the language for a while now, but I’ve only done a couple toy projects. The best way to get a feel for a language is to write spend some consecutive time writing a real program that others will be using. Having a real project makes it possible to get a really good picture of what software development is like using a language.

First Impressions

When I first started going beyond toy projects, the most difficult aspect was the package (or module in Python terms) system. In Python, you have an abstraction of modules that uses the filesystem, but requires extra tooling to include other code. Other languages, like Ruby (if I remember it correctly!) can use a more direct include type of module system where you include the code you want. PHP is a great example of this simple module pattern where you truly just include other code as if it were written within your own. Go does something different where a package is really a directory and the import statement includes everything in that directory that has the same package declaration. I’m sure there is more to it, but this crude understanding has been enough to be dangerous.

One aspect that drew me to go was the simplified deployment. Go, aseems to be focused on producing extremely flexible binaries that can be used on a wide array of systems. The result is that a Go binary that you built with linux typically can be copied to any other linux without having any issues. While I’m the sure the same is possible with C/C++ or any other compiled language, it has been an early feature to produce all inclusive binaries that don’t have a dependency on anything on the target machine. As I’m coming from Python, I’m not an expert in these sorts of things, so this is really just my impression, validated by my limited experience.

Finally, Go is reasonably fast without being incredibly complex. I don’t have to manage memory, but I do get to play with pointers. Concurrency is a core feature of the language that helps implement the fun features of Python like generators. Go routines are poised to do the things that I always wished Python did and that is to take care of work no matter whether it is async I/O or CPU bound. Lastly, it doesn’t have a warm up period like you’d see in a JVM language, which makes it suitable for small scripts.

But What About Python?

I still enjoy Python, but the enjoyment comes from feeling fluent in the language more than enjoying any set of features. The thing I like most about programming in Python is that I feel very comfortable banging out code and knowing that it is reasonably well written with tests and can function well within the larger Python ecosystem. This fluency in Python is not something I’m willing to drop due to some hype in another language. There are some frustrating aspects of programming in Python that make me want to try something new.

The first is the packaging landscape. I don’t mean to suggest that pip is terrible or wheels are a huge mistake. Instead, it is the more general pattern of shipping source code to an environment in order to run the code. There are TONS of great tools to make this manageable and containers only make it even easier, so again, I’m not condemning the future of Python deployments. But I am tired of it.

In Python, I need to make a ton of decisions (some that have been chosen for me) such as whether or not to use the system packages or a virtualenv. I have to consider how to test a release before releasing it. I have to establish how I can be sure that what I tested is the same as what I’m releasing. All these questions can become subtlely complex over time and it gets tiring.

The second aspect of Python that is frustrating is the performance. Generally, it is fast enough, except when it isn’t. It is when things need to be faster that Python becomes painful. If you are CPU bound then get ready to scale out those processes! If I/O is where you are lacking there are a plethora of async I/O libraries to help efficiently accessing I/O. But, dealing with both CPU and I/O bound issues is complicated, especially when you are using libraries that may not be compatible with the async I/O framework you are using. It is definitely possible to write fast Python code, but there is a lot of work to do, none of which makes deployment easier, hence it is a little tiring.

When all else fails you can write a C module, use Cython or try another interpreter. Again, all interesting ideas, but the cost shows up in complexity and in the deployment.

There is no easy fix to these problems and it isn’t as fun as it used to be to think about solving it.

So Why Go Then?

The question then is why Go is a contender to unseat my most fluent language Python? The simple answer is that I’m tired of the complexity. The nice thing about Go is that takes care of some essential aspects I don’t care to delve into, specifically, memory management and concurrency. Go is also fast enough that I should not have to be terribly concerned about adopting new programming paradigms in order to work around some critical bottleneck. While I’m confident that I could make Python work for almost anything, at some point it feels like it has become a hammer and I’d like to start doing other things than hitting nails.

Now, even though I’m learning Go and have been excited about the possibilities, it doesn’t change the fact most of my day to day work is in Python. Maybe that will change at some point, but for the foreseeable future, I don’t see it changing anytime soon, which is totally fine by me.

I’m sure there are issues with Go that I haven’t run into yet. Static typing has been challenging to use after having massive freedom in Python. What is appealing is that less than perfect Go code has been good enough in quality for production uses. That is exciting because it means while I learn more about the langauge and community, it doesn’t preclude me from being productive and learning new things.