Benchmark: spred test - MeasureThat.net

Tests:

assign code
let res = {} Object.assign(res, {'rock':'hello'})
spred code
let res = {} res = {...res, 'rock': 'hello'}

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
assign code
spred code

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

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

LLMs can make mistakes. Check important info.

I'll break down the provided benchmark and explain what's being tested, compared, and some pros/cons of different approaches.

**Benchmark Overview**

The `MeasureThat.net` website allows users to create and run JavaScript microbenchmarks. The benchmark in question measures the performance difference between two approaches for creating a new object with a single property: assignment using `Object.assign()` versus spreading an existing object using the spread operator (`...`).

**Script Preparation Code**

There is no script preparation code provided, which means that the test cases are self-contained and do not require any external dependencies or setup.

**Html Preparation Code**

Again, there is no html preparation code provided, indicating that the tests focus solely on JavaScript performance and do not rely on HTML rendering or DOM manipulation.

**Individual Test Cases**

There are two individual test cases:

1. **`assign code`**: This test case uses `Object.assign()` to create a new object with a single property.
2. **`spred code`**: This test case uses the spread operator (`...`) to create a new object with a single property.

**Library Usage**

Neither of these tests relies on any external libraries, making them self-contained and focused solely on JavaScript performance.

**Special JS Features/Syntax**

The use of the spread operator (`...`) in `spred code` is an example of a modern JavaScript feature introduced in ECMAScript 2018 (ES8). The spread operator allows for more concise and expressive way of creating new objects, as well as updating existing ones. While this feature is not unique to this benchmark, its usage highlights the importance of considering modern JavaScript features when optimizing performance.

**Pros/Cons of Approaches**

Here are some pros and cons of each approach:

1. **`assign code` (Object.assign())**
	* Pros:
		+ Wide browser support (most browsers have implemented `Object.assign()` since ES5)
		+ Well-documented and widely used
	* Cons:
		+ Can be slower due to the overhead of the `Object.assign()` method invocation
		+ May lead to more memory allocations if dealing with large objects
2. **`spred code` (spread operator)**
	* Pros:
		+ More concise and expressive way of creating new objects
		+ Often faster since it involves a single operation instead of multiple method invocations
	* Cons:
		+ Not all browsers have implemented the spread operator yet (some versions of Safari, for example)
		+ May not work in older JavaScript environments

**Other Alternatives**

For creating new objects with a single property, other approaches might include:

1. **`Object.create()`**: This method creates a new object with the given prototype and initializes it with the specified values.
2. **`new Object()`.propName = value`:** This approach creates a new object using the `new` keyword and then assigns the value to a property.

However, for most use cases, either `Object.assign()` or the spread operator will be the preferred choice due to their simplicity and widespread support.

**Benchmark Result Interpretation**

The provided benchmark result shows that the `spred code` approach outperforms `assign code` on this specific test case. This is likely due to the conciseness of the `spred code` implementation, which can reduce overhead and lead to faster execution times.

Related benchmarks:

spred test (version: 0)

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Comparing performance of: assign code vs spred code

Created: 3 years ago by: Guest

Jump to the latest result

assign code

spred code

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):