Speeding up JavaScript Arrays with Transducers. RxJS vs Ramda vs Lodash скачать в хорошем качестве

Speeding up JavaScript Arrays with Transducers. RxJS vs Ramda vs Lodash

Transducers are a fairly unknown trend in functional programming that provides a significant boost to array performance in JavaScript. This talk was originally done at KCDC 2021. The most important thing about transducers has to do with their ability to do multiple transformations on each item in a pipeline prior to outputting those results. Most functional pipelines do a single action across a number of items for each step in the pipeline. Transducers do all this work once for each item and then choose when or how to set those back into a collection if at all. While they're not widely known, I provide benchmarks displaying their performance and even go over the implementation I wrote for the presentation itself. We review various transducer libraries including JavaScript built-in Array.prototype methods, RxJS, Ramda, Thi.ng, Lodash, and others. My first look at transducers came from Eric Elliot:    / @_ericelliott   0:00 Speeding up JavaScript Arrays with transducers 0:59 Overview 1. Loops (yes, really...). 2. Array methods. 1:19 Let's start with a for loop. 1:30 Increment the index. 2:16 stop computing length each loop. 2:28 Why are we looping? 3:23 map returns a new array 4:03 implicit return 4:16 common Array.prototype methods 4:58 and functional programming 6:14 composition 7:37 reduce has state 8:21 reduce is a 'ducer 8:25 WHY Array.prototype? 8:30 easier to perform multiple operations on an array 8:40 removes boilerplate 8:47 Array.prototype issues 8:51 constantly creating new arrays 9:06 Array.prototype alternatives 11:27 synchronous 12:20 standard functional programming 12:40 What's a transducer? 13:28 I learned about transducers from Eric Elliott 13:50 that's a trans-ducer 13:53 it transforms a reducer into another reducer 14:01 Why use transducers? 14:19 iterate loop all items 15:16 examples 16:39 transducers work backward 17:50 easier to use 19:01 transform run all transformations 20:35 let's write transducers 22:09 index is a counter 22:16 counters requires state 23:24 don't worry about it 25:35 let's write map 25:47 calling map transducer 27:03 let's write filter 31:40 reduce stores an array of values 31:43 reduce waits for all values 34:26 transducers are hard 34:55 incrementing transform 37:18 duplicate up 40:01 stats 41:40 Ramda transducers don't run at scale 45:01 Array.prototype is fast 49:16 i have a story 50:39 bad JS leads to bad performance 51:40 only when looping 5K items 52:01 functional code is readable 52:04 maintenance performance