Benchmark: Spread Operator vs Lodash CloneDeep

HTML Preparation code:


<script src='https://cdnjs.cloudflare.com/ajax/libs/lodash.js/4.17.5/lodash.min.js'></script>

Script Preparation code:

var arr = new Array(100000000).fill(0);

Tests:

Spread Operator
[...arr];
Lodash CloneDeep
_.cloneDeep(arr);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Spread Operator
Lodash CloneDeep

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

This benchmark does not have any results yet. Be the first one to run it!

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

LLMs can make mistakes. Check important info.

Let's dive into the details of this benchmark.

**What is being tested?**

The provided JSON represents a JavaScript microbenchmark that compares two approaches: using the spread operator (`[...arr]`) and using Lodash's `cloneDeep` function (`_.cloneDeep(arr)`).

**Options compared:**

Two options are being compared:

1. **Spread Operator**: `[...arr]`. This is a modern JavaScript feature that allows you to create a new array from an existing array by spreading its elements into an array literal.
2. **Lodash CloneDeep**: `_.cloneDeep(arr)`. Lodash is a utility library for JavaScript that provides a number of helper functions, including `cloneDeep`, which creates a deep copy of an object (or array).

**Pros and Cons:**

* **Spread Operator**:
	+ Pros: concise, efficient, and readable.
	+ Cons: may not work as expected with nested arrays or complex data structures. Additionally, it only works in modern browsers that support the `Array.prototype spread` method.
* **Lodash CloneDeep**:
	+ Pros: robust, reliable, and widely supported (works in most JavaScript environments).
	+ Cons: larger binary size due to the inclusion of the Lodash library.

**Other considerations:**

* The benchmark assumes that the input array `arr` has 100 million elements. This may not be representative of real-world scenarios, where arrays are often smaller.
* The benchmark does not account for any potential side effects or changes to the original array.
* It's worth noting that Lodash's `cloneDeep` function creates a deep copy of the object, which means it will recursively create new objects and arrays as needed. This can be an advantage in certain situations, but may also lead to increased memory usage.

**Library explanation:**

Lodash is a popular JavaScript utility library that provides a wide range of helper functions for tasks like array manipulation, string manipulation, function composition, and more. The `cloneDeep` function is one of its most useful tools, as it allows you to create a deep copy of an object or array.

**Special JS feature/syntax:**

There is no special JavaScript feature or syntax being used in this benchmark. Both the spread operator and Lodash's `cloneDeep` function are standard features that have been around for a while.

As for alternative approaches, there are other ways to create a deep copy of an array or object in JavaScript:

* **Array.prototype.slice()**: This method creates a shallow copy of an array by returning a new array containing references to the original elements.
* **JSON.parse(JSON.stringify(arr))**: This method creates a deep copy of an object by recursively parsing its contents and creating new objects as needed.
* **Object.assign()**: This method creates a new object with copies of all the properties from an existing object. However, this approach is not guaranteed to work for arrays or other complex data structures.

These alternative approaches may have different performance characteristics or trade-offs compared to using Lodash's `cloneDeep` function or the spread operator.

Related benchmarks:

Spread Operator vs Lodash CloneDeep (version: 0)

Answering https://stackoverflow.com/questions/58702513/lodash-clone-array-vs-spread-operator#

Comparing performance of: Spread Operator vs Lodash CloneDeep

Created: 4 years ago by: Guest

Jump to the latest result

Spread Operator

Lodash CloneDeep

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

No previous run results

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