Benchmark: JavaScript spread operator vs Object.assign performance - V2

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:

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

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Let's break down what's being tested in this benchmark.

**What is being tested:**

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

1. The spread operator (`...`)
2. `Object.assign()`

The test creates two sample objects, `firstObject` and `secondObject`, with different properties. It then attempts to merge these objects using both methods.

**Options compared:**

* **Using the spread operator**: This method uses the syntax `{ ...firstObject, ...secondObject }` to create a new object with all properties from both `firstObject` and `secondObject`.
* **Using Object.assign()**: This method uses the `Object.assign()` function to merge two objects. The original object (`firstObject`) is mutated, while the second object (`secondObject`) is merged into it.

**Pros and cons:**

* **Using the spread operator:**
	+ Pros:
		- Less mutation of original objects
		- Can be more readable and expressive in certain scenarios
	+ Cons:
		- May incur additional memory allocations or copying operations
		- Can lead to unexpected behavior if not used carefully (e.g., with object destructuring)
* **Using Object.assign():**
	+ Pros:
		- Less memory-intensive, as it modifies the original object directly
		- Can be faster, as it avoids unnecessary copies
	+ Cons:
		- Mutates the original object, which can lead to unexpected behavior in some cases

**Other considerations:**

* The benchmark does not consider other approaches, such as using `lodash.merge()` or `immer` libraries.
* It also does not account for edge cases like null or undefined values being passed to merge functions.

**Library usage:**

None of the code snippets use any external libraries. However, if we were to extend this benchmark to include other approaches, we might consider including libraries like Lodash (`_merge`) or Immer (`updateObject`).

**Special JavaScript features or syntax:**

The benchmark uses ES6+ syntax, specifically:

* The spread operator (`...`)
* Object destructuring is not used in this specific case, but it's worth noting that object destructuring can be used with `Object.assign()` to achieve similar results.

Now, if you're interested in exploring alternative approaches or optimizing the performance of these methods, here are some potential alternatives:

1. Using a custom merging function: Instead of relying on built-in functions like `Object.assign()` or spread operator, you could create your own merging function that takes into account specific requirements and optimization techniques.
2. Memoization: If you're concerned about the performance impact of repeated calls to merge functions, you could consider implementing memoization to cache results and avoid redundant calculations.
3. Use of specialized libraries like Lodash or Immer: As mentioned earlier, these libraries provide optimized merging functions that can outperform built-in approaches in certain scenarios.

Feel free to ask if you have any further questions or need help with exploring alternative approaches!

Related benchmarks:

JavaScript spread operator vs Object.assign performance - V2 (version: 0)

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

Created: 5 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):