Benchmark: JavaScript spread operator vs Object.assign performance (rkfork)

Tests:

Using the spread operator
const firstObject = { sampleData: 'Hello world' } const secondObject = { moreData: 'foo bar' } const finalObject = { ...firstObject, ...secondObject };
Using Object.assign
const firstObject = { sampleData: 'Hello world' } const secondObject = { moreData: 'foo bar' } const finalObject = Object.assign({}, firstObject, secondObject);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Using the spread operator
Using Object.assign

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 break down the provided benchmark and explain what's being tested, compared, and other considerations.

**Benchmark Overview**

The benchmark compares the performance of two approaches to merge objects in JavaScript:

1. Using the spread operator (`...`).
2. Using `Object.assign()`.

**What is being tested?**

The test cases are comparing the execution time of each approach on a simple object merging scenario. The scenarios involve creating two separate objects, `firstObject` and `secondObject`, and then merging them into a single object using either the spread operator or `Object.assign()`. The resulting merged object is stored in `finalObject`.

**Options being compared**

The benchmark is comparing the performance of two specific options:

1. **Using the spread operator (`...`)**: This approach uses the spread operator to merge objects by creating a new object and copying properties from both input objects.
2. **Using `Object.assign()`**: This approach uses the `Object.assign()` method to merge objects by cloning an existing object and assigning properties from one or more source objects.

**Pros and Cons**

Here are some pros and cons of each approach:

* **Using the spread operator (`...`)**:
	+ Pros:
		- More concise and readable code.
		- Can handle nested objects and arrays.
	+ Cons:
		- May incur additional overhead due to the creation of a new object.
* **Using `Object.assign()`**:
	+ Pros:
		- More efficient in terms of memory allocation, as it reuses an existing object.
		- Can be more flexible with multiple source objects.
	+ Cons:
		- Requires creating a new object and assigning properties, which can be slower than using the spread operator.

**Library used**

In this benchmark, `Object.assign()` is the only library used. It's a built-in method in JavaScript that merges one or more source objects into an existing target object.

**Special JS feature or syntax**

There are no special JS features or syntax mentioned in this benchmark. The scenarios only use basic JavaScript syntax and do not rely on any advanced features like async/await, promise chaining, or closures.

**Other alternatives**

If you're interested in exploring alternative approaches to merging objects in JavaScript, here are a few options:

1. **Destructuring assignment**: You can use destructuring assignment to extract properties from objects and assign them to variables.
2. **Object spread with `...` and `Object.create()`**: This approach uses `Object.create()` to create a new object and then spreads properties onto it using the `...` operator.
3. **Using `Array.prototype.reduce()` or other accumulator functions**: You can use accumulator functions like `reduce()` to merge objects by iterating over their properties.

Keep in mind that these alternatives may have different performance characteristics, readability, or maintainability compared to using `Object.assign()` or the spread operator.

Related benchmarks:

JavaScript spread operator vs Object.assign performance (rkfork) (version: 0)

Comparing performance of: Using the spread operator vs Using Object.assign

Created: 4 years ago by: Guest

Jump to the latest result

Using the spread operator

Using Object.assign

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):