Benchmark: Immer vs shallow vs ramda lens (nested structure)

HTML Preparation code:

<script src="https://unpkg.com/immer@9.0.7/dist/immer.umd.production.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/ramda/0.25.0/ramda.min.js"></script>

Script Preparation code:

var { set, lensPath } = R

var object = { one: 1, two: 2, three: 3, four: 4, five: 5, array: Array.from({ length: 10000 }).map(() => Math.random()) }

var INITIAL_DATA = {
  one: { two: { three: { four: { five: 5, object }, object }, object }, object },
  two: { three: { four: { five: 5, object }, object }, object },
  three: { four: { five: 5, object }, object },
  four: { five: 5, object },
  five: 5
}

var produce = immer.default

Tests:

immer
data = produce(INITIAL_DATA, draft => { draft.one.two.three.four.five = 0; })
shallow copy
data = { one: { two: { three: { four: { five: 0, ...INITIAL_DATA.one.two.three.four }, ...INITIAL_DATA.one.two.three }, ...INITIAL_DATA.one.two }, ...INITIAL_DATA.one }, ...INITIAL_DATA }
ramda lens
data = set(lensPath(['one', 'two', 'three', 'four', 'five']), 0, INITIAL_DATA)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
immer
shallow copy
ramda lens

Fastest: N/A

Slowest: N/A

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

LLMs can make mistakes. Check important info.

Let's break down the benchmark and its test cases.

**What is tested:**

The provided benchmark compares three different approaches to update nested structures in JavaScript:

1. **Immer**: A library that provides an immutable data structure, allowing for efficient updates without mutating the original object.
2. **Shallow copy**: A basic approach that creates a new object by copying the existing one, which can lead to performance issues with deep structures.
3. **Ramda lens**: A library that provides a functional programming style for updating nested objects using a path-based API.

**Options compared:**

The benchmark compares the performance of these three approaches on a complex nested structure:

* `immer`: updates the original object using the `immer` library's `produce` function.
* `shallow copy`: creates a new object by copying the existing one, without mutating the original.
* `ramda lens`: uses Ramda's `lensPath` and `set` functions to update specific parts of the nested structure.

**Pros and cons:**

1. **Immer**: Pros:
	* Immutable data structures can provide thread-safety guarantees.
	* Immutability can make code easier to reason about and debug.
2	Cons:
	* Can introduce additional overhead due to object creation and cloning.
	* May require more memory allocation for large datasets.
3. **Shallow copy**: Pros:
	* Simple and lightweight implementation.
4. Cons:
	* Can lead to performance issues with deep structures, as new objects are created recursively.
5. **Ramda lens**: Pros:
	* Provides a functional programming style for updating nested objects.
	* Can be more concise and expressive than traditional imperative approaches.
Cons:
	* May require additional memory allocation due to the creation of intermediate objects.

**Library usage:**

The benchmark uses two external libraries:

1. **Immer**: Used for creating an immutable data structure and providing efficient updates.
2. **Ramda**: Used for its `lensPath` and `set` functions, which provide a functional programming style for updating nested objects.

**Special JS features or syntax:**

No special JavaScript features or syntax are used in this benchmark.

**Alternative approaches:**

Other alternatives to these approaches include:

1. **ES6 spread operator**: Using the spread operator (`{ ...obj }`) to create a shallow copy of an object.
2. **Object.assign()**: Using `Object.assign()` to create a shallow copy of an object.
3. **Prototype-based inheritance**: Using prototype-based inheritance to avoid creating new objects and instead update existing ones.

These alternatives have their own trade-offs in terms of performance, memory usage, and code readability. The choice of approach ultimately depends on the specific requirements and constraints of the project.

Latest run results:

Run details: (Test run date: 2 years ago)

User agent: Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:98.0) Gecko/20100101 Firefox/98.0

Browser/OS: Firefox 98 on Ubuntu

View result in a separate tab

Test name	Executions per second
immer	20201.7 Ops/sec
shallow copy	721516.6 Ops/sec
ramda lens	136910.3 Ops/sec

Related benchmarks:

Immer vs shallow vs ramda lens (nested structure) (version: 0)

Comparing performance of: immer vs shallow copy vs ramda lens

Created: 2 years ago by: Registered User

Jump to the latest result

immer

shallow copy

ramda lens

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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