| import zio.* | |
| import zio.prelude.* | |
| import zio.prelude.fx.* | |
| object BaseSyntax: | |
| type Program[S, R, E, A] = ZPure[Nothing, S, S, R, E, A] | |
| type LoggedProgram[W, S, R, E, A] = ZPure[W, S, S, R, E, A] |
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)
Consider the following three functions
Cats Instances for Java Time
Cats Instances for Scalacheck
| // Sync Model from CSV.ajs | |
| // | |
| // Requires jArchi - https://www.archimatetool.com/blog/2018/07/02/jarchi/ | |
| // | |
| // This script allows easy integration of data coming from other systems through CSV files. | |
| // Each CSV file is described through a configuration object that you can later load and sync. | |
| // Most of the mapping informations (Id, Name, Documentation, Properties...) can be expressed through | |
| // the name of a column from the CSV file or through a function (which allows more advanced computation). | |
| // | |
| // Dataset used as example comes from https://datahub.io/core/country-codes |
| " Personal settings | |
| syntax on | |
| set number | |
| set relativenumber | |
| set ruler | |
| set cursorline | |
| set modeline | |
| set vb | |
| set incsearch |
This is a project structure I’ve found useful. Looking for any
thoughts/comments/feedback. Roughly, I found a tension between the style
nixpkgs expects and the style conducive to development, so I extracted the
common portion into a derivation.nix which is used by the remaining .nix
files. This setup allows me to use nix build, nix-shell, overlays, Hydra,
alternate packaging schemes, cross-compiling, etc.
A collection of links to useful cheat sheets.
Only what's properly printable can get a spot at the top of this list. It should also be small enough to fit on a few pages. almost all links in this document were designed by their creators so that YOU can print them in a good-looking format and store them however is best for you. Anything that isn't really something you could print in A4/UfS Letter format, but is still a well-made cheatsheet can get a spot at the end of the page.
Pleae contribute any you remember you've seen and liked. It would be wonderful if we can get these to be something more commonly made.
flatMap, especially if the following operation will result in high memory usage. The flatMap op usually results in a DataFrame with a [much] larger number of rows, yet the number of partitions will remain the same. Thus, if a subsequent op causes a large expansion of memory usage (i.e. converting a DataFrame of indices to a DataFrame of large Vectors), the memory usage per partition may become too high. In this case, it is beneficial to repartition the output of flatMap to a number of partitions that will safely allow for appropriate partition memory sizes, based upon the