chaotic3quilibrium

Effective Scala Case Class Patterns

Version: 2022.03.02

Available As

dacr

// summary : ZIO learning - zio tests cheat sheet as tests
// keywords : scala, scalacli, zio, ziotest, @testable, cheatsheet
// publish : gist
// authors : David Crosson
// license : Apache NON-AI License Version 2.0 (https://raw.githubusercontent.com/non-ai-licenses/non-ai-licenses/main/NON-AI-APACHE2)
// id : 372e9d7a-34ab-4130-ab39-f5d060b0a2f7
// created-on : 2021-11-13T10:01:55+01:00
// managed-by : https://github.com/dacr/code-examples-manager
// run-with : scala-cli $file

fanf

Jan Nasiadka from scalac contacted me to get some feedback about our usage of ZIO. My answers below, since they can benefit the community more broadly.

Context: we use ZIO in the context of https://rudder.io, a scala application that is 11 years old - far from a greenfield application with ZIO as base architectural choice. We used ZIO as a better framework to cleanly manage errors, porting piece of existing code to it. We are part of ZIO community since 2018 (since ZIO inception, when it was not yet ZIO but a part of scalaz, and when there was only a bifunctor, no context in it). Given our usage, we use ZIO in a specific way: we have tons of entry points and evaluation of ZIO effects, not one main entry point in the "main" method of the app. For correctness, that forced us to call a lot of blocking effects (you never know what a java lib is doing), and so we stressed the thread pool ergonomics, and helped make that part better (and some part

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

djspiewak

Understanding Comparative Benchmarks

I'm going to do something that I don't normally do, which is to say I'm going to talk about comparative benchmarks. In general, I try to confine performance discussion to absolute metrics as much as possible, or comparisons to other well-defined neutral reference points. This is precisely why Cats Effect's readme mentions a comparison to a fixed thread pool, rather doing comparisons with other asynchronous runtimes like Akka or ZIO. Comparisons in general devolve very quickly into emotional marketing.

But, just once, today we're going to talk about the emotional marketing. In particular, we're going to look at Cats Effect 3 and ZIO 2. Now, for context, as of this writing ZIO 2 has released their first milestone; they have not released a final 2.0 version. This implies straight off the bat that we're comparing apples to oranges a bit, since Cats Effect 3 has been out and in production for months. However, there has been a post going around which cites various compar

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

non

COLLECTION                              0        1        5       10       50      100      500     1000
Array[Int]                             16       24       40       56      216      416     2016     4016
immutable.BitSet                       24       24       24       24       24       32       96      160
immutable.IntMap[Int]                  16       40      328      688     3568     7168    35968    71968
immutable.List[Int]                    16       56      216      416     2016     4016    20016    40016
immutable.Map[Int,Int]                 16       40      304      720     4856     9680    53504   111760
immutable.Queue[Int]                   40       80      240      440     2040     4040    20040    40040
immutable.Set[Int]                     16       32      264      480     3016     5840    27696    57952
immutable.SortedMap[Int,Int]           40       88      280      520     2440     4840    30008    62008
immutable.SortedSet[Int]               40      104      296      536     2456  

enzief

Scala effect systems

Table of Contents

1. Programming with purity
2. Scala's standard Future
3. Superior alternatives
   1. cats-effect IO
   2. Monix Task
   3. Scalaz 8 Zio
4. Features comparison

gvolpe

Shared State in pure Functional Programming

Newcomers to Functional Programming are often very confused about the proper way to share state without breaking purity and end up having a mix of pure and impure code that defeats the purpose of having pure FP code in the first place.

Reason why I decided to write up a beginner friendly guide :)

Use Case

We have a program that runs three computations at the same time and updates the internal state to keep track of the

non

introduction

ScalaCheck 1.14.0 was just released with support for deterministic testing using seeds. Some folks have asked for examples, so I wanted to produce a Gist to help people use this feature.

simple example

These examples will assume the following imports: