quelgar

Every Argument for Static Typing Applies to Typed Errors

Think of all the arguments you've heard as to why static typing is desirable — every single one of those arguments applies equally well to using types to represent error conditions.

An odd thing I’ve observed about the Scala community is how many of its members believe that a) a language with a sophisticated static type system is very valuable; and b) that using types for error handling is basically a waste of time. If static types are useful—and if you like Scala, presumably you think they are—then using them to represent error conditions is also useful.

Here's a little secret of functional programming: errors aren't some special thing that operate under a different set of rules to everything else. Yes, there are a set of common patterns we group under the loose heading "error handling", but fundamentally we're just dealing with more values. Values that can have types associated with them. There's absolutely no reason why the benefits of static ty

dicej

Classes
* Keith Devlin - Introduction to Mathematical Thinking - https://www.coursera.org/learn/mathematical-thinking
* Michael Genesereth - Introduction to Logic - https://www.coursera.org/learn/logic-introduction
* Robert Harper - Homotopy Type Theory - http://www.cs.cmu.edu/~rwh/courses/hott/

Books and Articles
* Benjamin C. Pierce - Types and Programming Languages - https://www.cis.upenn.edu/~bcpierce/tapl/
* x775 - Introduction to Datalog - https://x775.net/2019/03/18/Introduction-to-Datalog.html
* Bartosz Milewski - Category Theory For Programmers - https://bartoszmilewski.com/2014/10/28/category-theory-for-programmers-the-preface/
* Benjamin C. Pierce et al. - Software Foundations - https://softwarefoundations.cis.upenn.edu/

kubukoz

Why you prefer cats instead of zio? TF? It’s looks like zio ecosystem more widely and zio 2.0 has better performance What do you think?

Great question!

Performance-wise, it really depends on what you're doing. The problem with benchmarks (including the ones posted for ZIO and Cats Effect) is that they apply only to abstract situations, which are often nothing like what you see in real applications. A great write-up on this problem by Daniel Spiewak here, he wrote it better than I ever could: https://gist.github.com/djspiewak/f4cfc08e0827088f17032e0e9099d292

Also, this is not an app meant for production - I don't care that much about performance under load because there will be no load. And individual operations will often be bounded by I/O anyway, so the efficiency of the underlying runtime is likely not going to make a noticeable difference in use. Then again, both the client and server are JVM apps, so even the start-up penalty of the client will slow us down than picking even the least efficient e

jackcviers

So the hacker news post said my comment was too long. Warning, long, opinionated post:

Scala dev for 10+ years here. Spark is weird. Databricks has a style guide that deliberately chooses not to use scala features that the rest of the community uses, and doesn't follow the same best practices around library and scala major version usage that the rest of the community uses [1]. It's no surprise that the project has trouble interoperating with libraries outside of the spark ecosystem, and is therefore a maintenance problem.

Spark's style and compatibility problems

Scala isn't a maintenance nightmare, but it does attract a lot of newcomers who dive in, don't stick within one of its many ecosystems, get confused, and generally leave a mess, and that is a direct result of the fact that scala is a multi-paradigm, relatively expressive language to the one(s) it is competing with and pulling developers from, and that those developers, for the large part, don't really want to change and think that Scala is just a

djspiewak

Fibers

Fibers are an abstraction over sequential computation, similar to threads but at a higher level. There are two ways to think about this model: by example, and abstractly from first principles. We'll start with the example.

(credit here is very much due to Fabio Labella, who's incredible Scala World talk describes these ideas far better than I can)

Callback Sequentialization

Consider the following three functions

katef

#!/usr/bin/awk -f

# This program is a copy of guff, a plot device. https://github.com/silentbicycle/guff
# My copy here is written in awk instead of C, has no compelling benefit.
# Public domain. @thingskatedid

# Run as awk -v x=xyz ... or env variables for stuff?
# Assumptions: the data is evenly spaced along the x-axis

# TODO: moving average

tfausak

Invertible syntax descriptions

An "invertible syntax description" is something that can be used to both parse and generate a syntax. For example, instead of defining separate toJson and fromJson functions for a given data type, an invertible syntax description could be used to provide both. There are many Haskell libraries that provide this functionality or something close to it. As far as I can tell most of them are at least inspired by Invertible syntax descriptions by Tillmann Rendel and Klaus Ostermann.

Personally I am interested in using these for HTTP routing. I frequently want to be able to define a route such as /episodes/:id.json, dispatch based on that route, and generate links to that route. Doing so manually is tedious and error prone.

mmenestret

Purity and pure FP

• purity is a syntactical property of programs, more precisely defined as referential transparency: replacing an expression for its bound value doesn't change meaning (precise definition)
• side effects are things that break referential transparency (precise definition)
• There's no direct connection between the concepts of IO, State and so on, and side effects. Traditionally, these things are side effectful (break referential transparency), but you can have abstractions that are pure (don't break ref.trans.)
• Due to the above, the term purely functional programming is not an oxymoron, it makes perfect sense.
• In haskell style pure FP, type constructors and algebras are used to represent "things that would otherwise traditionally be side effects (break ref.trans.)". We call these F[_]s effects or computational contexts for brevity. You can see for yourself how this cannot be a precise definition (especially because they could also have kind higher than * -> *, even though most famous a

eneko

$ curl --help
Usage: curl [options...] <url>
     --abstract-unix-socket <path> Connect via abstract Unix domain socket
     --alt-svc <file name> Enable alt-svc with this cache file
     --anyauth       Pick any authentication method
 -a, --append        Append to target file when uploading
     --basic         Use HTTP Basic Authentication
     --cacert <file> CA certificate to verify peer against
     --capath <dir>  CA directory to verify peer against
 -E, --cert <certificate[:password]> Client certificate file and password

ezhulenev

object InstantInsanity extends App {

  // scalastyle:off

  def undefined[T]: T = ???
  def ⊥[T]: T = undefined

  trait R
  trait G
  trait B