DeepDiff
DeepDiff tells the difference between 2 collections and the changes as edit steps. It also supports Texture, see Texture example
Usage
Basic
The result of diff
is an array of changes, which is [Change]
. A Change
can be
.insert
: The item was inserted at an index.delete
: The item was deleted from an index.replace
: The item at this index was replaced by another item.move
: The same item has moved from this index to another index
By default, there is no .move
. But since .move
is just .delete
followed by .insert
of the same item, it can be reduced by specifying reduceMove
to true
.
Here are some examples
let old = Array("abc")
let new = Array("bcd")
let changes = diff(old: old, new: new)
// Delete "a" at index 0
// Insert "d" at index 2
let old = Array("abcd")
let new = Array("adbc")
let changes = diff(old: old, new: new)
// Move "d" from index 3 to index 1
let old = [
User(id: 1, name: "Captain America"),
User(id: 2, name: "Captain Marvel"),
User(id: 3, name: "Thor"),
]
let new = [
User(id: 1, name: "Captain America"),
User(id: 2, name: "The Binary"),
User(id: 3, name: "Thor"),
]
let changes = diff(old: old, new: new)
// Replace user "Captain Marvel" with user "The Binary" at index 1
DiffAware protocol
Model must conform to DiffAware
protocol for DeepDiff to work. An model needs to be uniquely identified via diffId
to tell if there have been any insertions or deletions. In case of same diffId
, compareContent
is used to check if any properties have changed, this is for replacement changes.
public protocol DiffAware {
associatedtype DiffId: Hashable
var diffId: DiffId { get }
static func compareContent(_ a: Self, _ b: Self) -> Bool
}
Some primitive types like String
, Int
, Character
already conform to DiffAware
Animate UITableView and UICollectionView
Changes to DataSource
can be animated by using batch update, as guided in Batch Insertion, Deletion, and Reloading of Rows and Sections
Since Change
returned by DeepDiff
follows the way batch update works, animating DataSource
changes is easy.
For safety, update your data source model inside updateData
to ensure synchrony inside performBatchUpdates
let oldItems = items
let newItems = DataSet.generateNewItems()
let changes = diff(old: oldItems, new: newItems)
collectionView.reload(changes: changes, section: 2, updateData: {
self.items = newItems
})
Take a look at Demo where changes are made via random number of items, and the items are shuffled.
How does it work
Wagner–Fischer
If you recall from school, there is Levenshtein distance which counts the minimum edit distance to go from one string to another.
Based on that, the first version of DeepDiff
implements Wagner–Fischer, which uses dynamic programming to compute the edit steps between 2 strings of characters. DeepDiff
generalizes this to make it work for any collection.
Some optimisations made
- Check empty old or new collection to return early
- Use
diffId
to quickly check that 2 items are not equal - Follow "We can adapt the algorithm to use less space, O(m) instead of O(mn), since it only requires that the previous row and current row be stored at any one time." to use 2 rows, instead of matrix to reduce memory storage.
The performance greatly depends on the number of items, the changes and the complexity of the equal
function.
Wagner–Fischer algorithm
has O(mn) complexity, so it should be used for collection with < 100 items.
Heckel
The current version of DeepDiff
uses Heckel algorithm as described in A technique for isolating differences between files. It works on 2 observations about line occurrences and counters. The result is a bit lengthy compared to the first version, but it runs in linear time.
Thanks to
- Isolating Differences Between Files for explaining step by step
- HeckelDiff for a clever move reducer based on tracking
deleteOffset
More
There are other algorithms that are interesting
Benchmarks
Benchmarking is done on real device iPhone 6, with random items made of UUID strings (36 characters including hyphens), just to make comparisons more difficult.
You can take a look at the code Benchmark. Test is inspired from DiffUtil
Among different frameworks
Here are several popular diffing frameworks to compare
let (old, new) = generate(count: 2000, removeRange: 100..<200, addRange: 1000..<1200)
benchmark(name: "DeepDiff", closure: {
_ = DeepDiff.diff(old: old, new: new)
})
benchmark(name: "Dwifft", closure: {
_ = Dwifft.diff(old, new)
})
benchmark(name: "Changeset", closure: {
_ = Changeset.edits(from: old, to: new)
})
benchmark(name: "Differ", closure: {
_ = old.diffTraces(to: new)
})
benchmark(name: "ListDiff", closure: {
_ = ListDiff.List.diffing(oldArray: old, newArray: new)
})
Result
DeepDiff: 0.0450611114501953s
Differ: 0.199673891067505s
Dwifft: 149.603884935379s
Changeset: 77.5895738601685s
ListDiff: 0.105544805526733s
Increasing complexity
Here is how DeepDiff
handles large number of items and changes
DeepDiff: 0.233131170272827s
DeepDiff: 0.453393936157227s
DeepDiff: 1.04128122329712s
Are you sure?
Installation
CocoaPods
Add the following to your Podfile
pod 'DeepDiff'
Carthage
Add the following to your Cartfile
github "onmyway133/DeepDiff"
Swift Package Manager
Add the following to your Package.swift file
.package(url: "https://github.com/onmyway133/DeepDiff.git", .upToNextMajor(from: "2.3.0"))
DeepDiff can also be installed manually. Just download and drop Sources
folders in your project.
Author
Khoa Pham, [email protected]
Contributing
We would love you to contribute to DeepDiff, check the CONTRIBUTING file for more info.
License
DeepDiff is available under the MIT license. See the LICENSE file for more info.