mohsinalimat/fastdiff

General purpose diffing library with parent/children n-level diffing

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Fast Diff

General purpose, fast diff algorithm supporting [m] level nested diffs.

Why?

1. Most diffing algorithm are O(nlogn) or O(n.m)
2. Most algorithm solve Least Common Subsequence problem which is hard to grasp at first
3. Supports nested diffing as per requirement.

Installation

Via cocoapods

pod 'FastDiff'

And then in the terminal pod update. If you are new to cocoapods please check out Cocoapods Installation

Via Swift Package Manager

Declare the dependency in the swift Package.swift file like such:

dependencies: [
  ///.... other deps
  .package(url: "https://www.github.com/kandelvijaya/FastDiff", from: "1.0.0"),
]

Execute the update command swift package update and then swift package generate-xcodeproj.

Running the tests

Go to the source directory, and run:

$ swift test

Usage

Algorithm & Verification

let oldModels = ["apple", "microsoft"]
let newModels = ["apple", "microsoft", "tesla"]


/// Algorithm
let changeSet = diff(oldModels, newModels)
// [.addition("tesla", at: 2)]


/// Verification
oldModels.merged(with: changeSet) == newModels 

Note that diff produces changeset that can't be merged into old collections as is, most of the times. The changeset has to be ordered in-order for successful merge. This is also useful if you want to apply changeset to UITableView or UICollectionView.

let chnageSet = diff(["A","B"], [“C”,"D"])
// [.delete("A",0), .delete("B",1), .add("C",0), .add(“D",1)]

let orderedChangeSet = orderedOperation(from: changeSet)
// [.delete("b",1), .delete("a",0), .add("c",0), .add("d",1)]

Concept and advanced usage in List View Controller (iOS)

Please check out this presentation slides that I gave at @mobiconf 2018

Contributing

Feel free to contribute with Pull Requests. There are many tasks (trivial) left to do.

Potential Tasks

• Cocoapods support
• Documentation grammar correction
• Adding CI and build status

Authors

At the moment, solely me:

1. @kandelvijaya (https://twitter.com/kandelvijaya)

License

This project is licensed under the MIT License - see the LICENSE.md file for details

Acknowledgments

• Inspired by Paul Heckel's paper & algorithm