Benchmark: spread operator vs Object.assign performance vs Manual assignment

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);
Manual assignment
const firstObject = { sampleData: 'Hello world' } const secondObject = { moreData: 'foo bar' } const finalObject = {}; finalObject.sampleData = firstObject.sampleData; finalObject.moreData = secondObject.moreData;

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

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

Fastest: N/A

Slowest: N/A

Latest run results:

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

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I'll break down the provided benchmark and explain what's being tested, compared, and their pros and cons.

**Benchmark Overview**

The benchmark measures the performance of three ways to create an object by merging two existing objects: using the spread operator (`...`), `Object.assign()`, and manual assignment (using dot notation).

**Options Compared**

1. **Using the Spread Operator (`...`)**
	* This approach uses the spread operator (`{ ... }`) to merge the properties of two objects.
	* Pros:
		+ Concise and readable syntax.
		+ Creates a shallow copy of the original object, which can be beneficial for performance in some cases (see below).
	* Cons:
		+ May not work as expected when dealing with nested objects or complex data structures.
2. **Using `Object.assign()`**
	* This approach uses the `Object.assign()` method to merge the properties of two objects.
	* Pros:
		+ Widely supported and well-documented.
		+ Allows for more control over the merging process (e.g., using `Object.assign()` with an array).
3. **Manual Assignment (Dot Notation)**
	* This approach uses dot notation (`finalObject.sampleData = firstObject.sampleData; finalObject.moreData = secondObject.moreData;`) to merge the properties of two objects.
	* Pros:
		+ Simple and straightforward syntax.
	* Cons:
		+ Can be verbose and less readable than other approaches.
		+ Creates a shallow copy of the original object, which can lead to performance issues when dealing with large datasets.

**Performance Considerations**

When merging objects using these different approaches, it's essential to consider the following factors:

* **Shallow vs. Deep Copy**: Using `Object.assign()` or manual assignment creates a shallow copy of the original object, which means that only the top-level properties are copied, and any nested objects or arrays remain unchanged. In contrast, using the spread operator (`...`) creates a new object with a deep copy of all properties.
* **Array Merging**: When merging arrays using `Object.assign()` or manual assignment, the resulting array will be a reference to the original arrays. If you want to create a new array with the merged elements, you need to use the spread operator (`...`) or `Array.prototype.concat()`.
* **Performance Impact**: The performance impact of these approaches depends on the size and complexity of the data being merged. In general, using the spread operator (`...`) can be faster for large datasets because it creates a new object with a deep copy of all properties.

**Library/Feature Usage**

None of the test cases use any external libraries or special JavaScript features beyond what's standard in modern browsers.

I hope this explanation helps software engineers understand the benchmark and its comparison scenarios!

Related benchmarks:

spread operator vs Object.assign performance vs Manual assignment (version: 0)

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

Created: 6 years ago by: Guest

Jump to the latest result

Using the spread operator

Using Object.assign

Manual assignment

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):