datastation VS filter

There was a technique that existed already where you could use `go test -cover` and the `-o` flag to produce a binary from `go test` rather than actually running tests. So you could build a binary that had coverage enabled. Then when you ran

Here's an example: https://github.com/multiprocessio/datastation/blob/main/runn....

I can't remember where I found this technique but it's been around for a while.

This new option is the same thing but a way to `go build` with `-cover` instead of `go test -cover -o $out`? Do I have that right?

If my employer doesn't consider paying for dbt cloud, I will use u/eatonphil 's datastation, run the queries on a dev database then put them in dbt.

I build a somewhat similar app, DataStation [0], that is in JavaScript and Go. It supports scripting in Python, Julia, R, JavaScript, Ruby, etc.

The server version of it exists and I run it myself but that process is not documented yet. (Most people use it as a desktop app today.)

[0] https://github.com/multiprocessio/datastation

My biggest issue with Pyodide is the long wait times. I haven't figured out a way around a ~5 second load time where the entire UI hangs every single time you load the page.

My app (similar to Simon's, a lite mode of a data IDE): https://app.datastation.multiprocess.io.

My code: https://github.com/multiprocessio/datastation/blob/main/shar....

I use Go heavily cross-platform developing DataStation [0] and dsq [1]. I am not an expert. And I don't have proof for it but on some rudimentary benchmarks the Linux-specific file idioms in the Go standard library definitely don't seem to translate well to even macOS let alone Windows. For example some good streaming techniques for reading large files on Linux that work really well there seemed to be pretty bad on macOS.

I think Amos has presented more proof than I can on the topic of just how Linux-influenced Go is. And I think it is fine for the majority of Go users because the majority users of Go are building server apps or Linux CLIs.

Amos has spent some time building cross-platform desktop systems with Go for itch.io and I think I'm seeing some of the same things they are in that scenario.

I think this is a reasonable article. If Amos gets flame-y at any point I think it's worth ignoring because there does seem to be something up with Go in cross-platform applications.

I like Go a lot and for most things I'd keep using it still. Just sharing some observations.

[0] https://github.com/multiprocessio/datastation

[1] https://github.com/multiprocessio/dsq

I keep a page of good first projects for two big projects I work on. The only expectation is that you know Go. I've had a couple of people who've never contributed to OSS come in and get some meaningful features merged.

I've got some good first projects if you're interested in OSS data tools and have some Go experience.

Check out: https://github.com/multiprocessio/datastation/blob/main/GOOD...

Some example projects: DataStation (desktop GUI for querying every kind of database, scripting and graphing the results) and dsq (a CLI companion for running SQL queries on many kinds of files), and go-json (a library for fast JSON encoding of arrays of large objects).

I'm building a desktop-first (SaaS-eventual) data IDE for developers [0]. Making a living? Not yet.

It being desktop-first makes it as easy to try out in a corporate environment as Sublime. The data never leaves your machine. Desktop-first is a big deal in devtools for this reason.

[0] https://github.com/multiprocessio/datastation

Here’s Rob Pike’s (one of the original Go designers) attempt to “see what the hubbub is all about”: https://github.com/robpike/filter

I find the language really solid but asking on r/golang is quite an adventure. It's extremely distant from go's spirit, the grammar is even more rich than Rust. Typical example: let, run, with, apply, and also - they all practically do the same but with a different scope of this and return value. Just looking at the flow API can get your head spinning. To illustrate how much it's completely the opposite of Go, see how Rob Pike pokes fun at map/filter and tells people they should not use it . I guess you can't force all developers to adhere to this mental model, but that's about it, but that's about it, technical arguments are irrelevant except for extremely niche concerns about memory and startup time

I mean sure, let's praise the ergonomics of channels and the reliability of maps. As for datastructures, we already have datastructures at home . They just work fine. Nobody needs more than that because rob pike told us so

Go will never have a map/filter syntax, to the point rob pike even makes fun of it , do you really want to use it for that kind of domain ?

Also since generators are mentioned I recently came across this rob pike moment, he implemented a reduce function that takes and returns all interface{} types and uses reflection to check if the call is valid at runtime - that's the most typical Go that can ever be written in 40 lines - all that to make the point that it's useless. Such a great spirit. https://github.com/robpike/filter

A few points that stood out to me: error handling in Go is generally pretty good. It's much more performant compared to throwing exceptions and the high frequency of error handling helps a lot with debugging and avoiding unexpected errors. What you've described as "poor OOP'ish" is partly true, yes Go does poor OOP, because it doesn't try to do OOP. The language favours composition over inheritance. Strongly applying OOP concepts in Go is simply not using the language in its intended way. For implicit interfaces, it's completely fair that you don't like them, but it's not a disadvantage of the language. I for one find implicit interfaces very intuitive and feel it's the right way for it to be done. No function overloading and lack of ternary operations is absolutely intentional, both of these are overcome by writing more expressive code, which is not a bad thing. Similarly with no built in map/filter/find, these can be achieved using for-loops. Reference https://github.com/robpike/filter for Rob Pike's implementation of filter, stating in the readme that there's not much use for it and to just use for-loops instead. Last thing, enums are expressed using iota: https://go.dev/ref/spec#Iota

> I didn't get that desire for purity that you gleaned from it.

'Folks who develop an allergic reaction to "big balls of mutable state without sum types" tend to gravitate towards languages that gives them control over mutability, lifetimes, and lets them build abstractions.'

This mutability argument is present throughout the article. Seems like nothing sans Rust or niche functional languages is enough.

> Nil pointer exceptions, for example, don't have to exist anymore..

The language most notorious for those is Java due to almost everything being passed via a nullable reference. When everything can be nullable, how can you know where to check for it? Go addresses this to an extent by explicitly separating pointers from values. Values are the default and cannot be nil, so the opportunity for null dereferences is greatly diminished. It's not a perfect solution, but it's not nothing either.

> and yet they do in Go because they couldn't be bothered to add sum types.

Damn those lazy Go devs!

> Its type system is barely a step above a dynamic language.

Turns out even a basic type system is a huge improvement over none. Just being able to restrict values to concrete types goes a long way.

> You have to write the same imperative looping code over and over because Rob Pike would rather just use a for loop than something mildly expressive like map or filter (https://github.com/robpike/filter).

There are arguments to be made either way, but I definitely agree generics (along with iterators) should have been there since day 1.

> Every function that does meaningful work is littered with if err != nil { return err }.

One big positive of this that I don't see in other languages is every `return` in a function must be on the start of a line. That is, every single exit path of a function is easily findable by visually scanning