Benchmark: Immerjs vs Immutable vs Shallow copy vs Object.assign (performance test) 3

HTML Preparation code:

<script src="https://cdn.jsdelivr.net/npm/immer/dist/immer.umd.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/immutable/3.8.2/immutable.js"></script>

Script Preparation code:

//const { fromJS } = require('immutable');
var data = { items: {}, count: 0, keys: [] }

for (let index = 0; index < 100000; index++) {
  data[index] = { id: index, name: `ITEM-${index}`, value: Math.random() }
  data.count++
  data.keys.push(index)
}

var NEW_ITEM_ID = data.count +1

var produce = immer.default

var im = Immutable.fromJS(data);

Tests:

immer
data = produce(data, draft => { draft.items[NEW_ITEM_ID] = { id: NEW_ITEM_ID, name: 'ITEM-NEW', value: 0 } draft.counter++ draft.keys.push(NEW_ITEM_ID) })
Immutable
var im = Immutable.fromJS(data); im = im.set('items', im.get('items').set(NEW_ITEM_ID, { id: NEW_ITEM_ID, name: 'ITEM-NEW', value: 0 })); im = im.set('count', im.count +1); im = im.set('keys', im.get('keys').push(NEW_ITEM_ID));
Shallow copy
data = { ...data, items: { ...data.items, [NEW_ITEM_ID]: { id: NEW_ITEM_ID, name: 'ITEM-NEW', value: 0 }, }, count: data.count + 1, keys: [...data.keys, NEW_ITEM_ID] }
Object.assign
data = Object.assign({}, data, { ...data, items: {...data.items, [NEW_ITEM_ID]: { id: NEW_ITEM_ID, name: 'ITEM-NEW', value: 0 }}, count: data.count + 1, keys: [...data.keys, NEW_ITEM_ID] })

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
immer
Immutable
Shallow copy
Object.assign

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 8 months ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/139.0.0.0 Safari/537.36 Edg/139.0.0.0

Browser/OS: Chrome 139 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
immer	38.7 Ops/sec
Immutable	16.3 Ops/sec
Shallow copy	5444.0 Ops/sec
Object.assign	119.8 Ops/sec

Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):

LLMs can make mistakes. Check important info.

Let's dive into the benchmark and explain what is being tested.

**Benchmark Definition**

The benchmark is testing four different approaches to add a new item to an object: Immutable.js, Immer.js, Shallow copy using JavaScript's spread operator, and Object.assign(). The goal is to measure which approach performs best in terms of performance.

**Options Compared**

1. **Immutable.js**: This library provides a way to work with immutable data structures, ensuring that the state of the application never changes unexpectedly.
2. **Immer.js**: This library provides a way to work with mutable data structures while maintaining a predictable and easy-to-debug workflow.
3. **Shallow copy using JavaScript's spread operator** (`...`): This approach creates a new object by copying all enumerable properties from an existing object, without creating a deep copy of nested objects.
4. **Object.assign()**: This method copies all enumerable own properties from one or more source objects to a target object.

**Pros and Cons**

1. **Immutable.js**:
	* Pros: Ensures predictable and immutable state, can reduce bugs related to state changes.
	* Cons: Can be slower due to the overhead of creating new objects and updating references.
2. **Immer.js**:
	* Pros: Provides a predictable and easy-to-debug workflow, can improve performance by avoiding unnecessary object creation.
	* Cons: Requires more memory than traditional mutation, as it creates drafts that are updated when the original object is modified.
3. **Shallow copy using JavaScript's spread operator**:
	* Pros: Fast and efficient way to create a new object without deep copying nested objects.
	* Cons: Does not update existing objects, may lead to unexpected behavior if not used carefully.
4. **Object.assign()**:
	* Pros: Fast and easy to use, works well with shallow copies.
	* Cons: Can be slower than the spread operator for large objects, may lead to unexpected behavior if not used carefully.

**Library and Purpose**

1. **Immutable.js**: Provides a way to work with immutable data structures, ensuring that the state of the application never changes unexpectedly.
2. **Immer.js**: Provides a way to work with mutable data structures while maintaining a predictable and easy-to-debug workflow.

**Special JS Features or Syntax**

The benchmark uses several JavaScript features:

* `let` and `const` declarations for variable scope
* Arrow functions (`() => { ... }`)
* Destructuring assignment (`...data.items`)
* Spread operator (`...`)

These features are commonly used in modern JavaScript development, but may not be familiar to all developers.

**Alternatives**

If you're interested in exploring alternative approaches to these libraries, here are a few options:

1. **Lodash**: A utility library that provides various functions for working with objects and arrays.
2. **Ramda**: A functional programming library that provides a way to work with immutable data structures.
3. **Mongolian Merkle Trees**: An alternative approach to storing and updating large datasets in a predictable and efficient manner.

Keep in mind that each of these alternatives has its own strengths and weaknesses, and may not be suitable for every use case.

Related benchmarks:

Immerjs vs Immutable vs Shallow copy vs Object.assign (performance test) 3 (version: 0)

Comparing performance of: immer vs Immutable vs Shallow copy vs Object.assign

Created: 2 years ago by: Guest

Jump to the latest result

immer

Immutable

Shallow copy

Object.assign

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):