nchammas

I don't understand this concern. How exactly are you copy/pasting code such that significant indentation causes "real problems"?

I remember the creators of Go explained [1] that they chose explicit block delimiters because of problems they saw when embedding snippets of Python in other languages. But this seems like a very niche kind of problem.

[1]: https://go.dev/talks/2012/splash.article#:~:text=we%20have%2...

nchammas

tahun laludiscuss

There is an old project out of Berkeley called BOOM [1] that developed a language for distributed programming called Bloom [2].

I don't know enough about it to map it to the author's distributed programming paradigms, but the Bloom features page [3] is interesting:

> disorderly programming: Traditional languages like Java and C are based on the von Neumann model, where a program counter steps through individual instructions in order. Distributed systems don’t work like that. Much of the pain in traditional distributed programming comes from this mismatch: programmers are expected to bridge from an ordered programming model into a disordered reality that executes their code. Bloom was designed to match–and exploit–the disorderly reality of distributed systems. Bloom programmers write programs made up of unordered collections of statements, and are given constructs to impose order when needed.

[1]: https://boom.cs.berkeley.edu

[2]: http://bloom-lang.net/index.html

[3]: http://bloom-lang.net/features/

nchammas

2 tahun yang laludiscuss

The examples I'm referring to are in that page I linked to in my comment above.

Here's one of them:

  # Polars
  df.select(
    pl.col("foo").sort().head(2),
    pl.col("bar").sort(descending=True).head(2),
  )

In SQL and Spark DataFrames, it doesn't make sense to sort columns of the same table independently like this and then just juxtapose them together. It's in fact very awkward to do something like this with either of those interfaces, which you can see in the equivalent Spark code on that page. SQL will be similarly awkward.

But in Polars (and maybe in Pandas too) you can do this easily, and I'm not sure why. There is something qualitatively different about the Polars DataFrame that makes this possible.

nchammas

2 tahun yang laludiscuss

There is something I don't get about the Polars DataFrame API.

https://docs.pola.rs/user-guide/migration/spark/

Look at the examples on this page of the Spark vs. Polars DataFrame APIs. (Disclaimer: I contributed this documentation. [1])

Having used SQL and Spark DataFrames heavily, but not Polars (or Pandas, for that matter), my impression is that Spark's DataFrame is analogous to SQL tables, whereas Polars's DataFrame is something a bit different, perhaps something closer to a matrix.

I'm not sure how else to explain these kinds of operations you can perform in Polars that just seem really weird coming from relational databases. I assume they are useful for something, but I'm not sure what. Perhaps machine learning?

[1]: https://github.com/pola-rs/polars-book/pull/113

nchammas

3 tahun yang laludiscuss

They _did_ serve the data. From TFA:

> To demonstrate its scale, we operated the instance with 100M bots posting 3,500 times per second at 403 average fanout.

The linked post about their demo [1] has more details about the challenges involved, including rendering home timelines.

[1]: https://blog.redplanetlabs.com/2023/08/15/how-we-reduced-the...

nchammas

3 tahun yang laludiscuss

This is pretty neat, and it reminds me of my experiment solving the water jug problem from Die Hard 3 using Hypothesis [1] (a Python library for property-based testing).

Though I doubt Hypothesis's stateful testing capabilities can replicate all the capabilities of Datalog (or its elegance for this kind of logic problem), I think you could port the author's expression of the game rules to Hypothesis pretty straightforwardly.

[1]: https://nchammas.com/writing/how-not-to-die-hard-with-hypoth...

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