Benchmark: spread array performance (vs slice, splice, concat)S

Tests:

Splice
const testArray = [1, 2, 3]; const newTestArray = testArray.splice(2,0,10)
Concat
const testArray = [1, 2, 3]; const newTestArray = testArray.concat(4);
Spread
const testArray = [1, 2, 3]; const newTestArray = [...testArray,4];

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Splice
Concat
Spread

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 4 years ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/83.0.4103.116 Safari/537.36

Browser/OS: Chrome 83 on Windows

View result in a separate tab

Test name	Executions per second
Splice	10592348.0 Ops/sec
Concat	2893708.5 Ops/sec
Spread	22650984.0 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's break down the benchmark test cases.

**Benchmark Definition**

The benchmark definition specifies that three different approaches will be tested:

1. `Splice`: This method modifies the original array by removing and replacing elements.
2. `Concat`: This method creates a new array by concatenating another array to the end of the original array.
3. `Spread`: This method creates a new array by spreading the elements of an existing array into a new array.

**Options Compared**

The benchmark compares these three approaches in terms of their performance. The goal is to determine which approach is the fastest, most efficient, and most suitable for different use cases.

**Pros and Cons of Each Approach:**

1. **Splice**:
	* Pros:
		+ Modifies the original array, reducing memory allocation.
		+ Can be faster than `concat` or spread if used correctly.
	* Cons:
		+ Modifies the original array, which can lead to unexpected behavior in some cases.
		+ Can be slower than other approaches for large arrays due to the overhead of modifying the array.
2. **Concat**:
	* Pros:
		+ Creates a new array, reducing modifications to the original array.
		+ Can be faster than `splice` for large arrays due to the reduced overhead.
	* Cons:
		+ Allocates new memory for the concatenated array, which can lead to increased memory usage.
		+ Can be slower than `splice` if used correctly, as it involves creating a new array.
3. **Spread**:
	* Pros:
		+ Creates a new array with minimal overhead, reducing allocations and modifications.
		+ Can be faster than other approaches due to the efficient use of arrays.
	* Cons:
		+ Requires modern JavaScript features (ES6+), which may not be supported in older browsers.
		+ May have performance issues if used incorrectly or with large arrays.

**Libraries Used**

None, as this is a native JavaScript benchmark.

**Special JS Features**

The `Spread` approach uses the spread operator (`...`) introduced in ECMAScript 2015 (ES6). This feature allows for efficient array creation and manipulation. If you're using an older version of JavaScript, you may need to use alternative approaches or transpile your code to a compatible version.

**Other Alternatives**

If you need to test other array methods or approaches not covered in this benchmark, consider the following alternatives:

* `Array.prototype.filter()`, `Array.prototype.map()`, and `Array.prototype.reduce()` for array manipulation.
* `Array.prototype.slice()` for slicing arrays.
* `Array.prototype.forEach()` for iterating over arrays.

To create a new benchmark on MeasureThat.net, you'll need to define your own benchmark definition, including the test cases, script preparation code, and HTML preparation code. You can then run your benchmark and analyze the results using the platform's tools.

Related benchmarks:

spread array performance (vs slice, splice, concat)S (version: 0)

Comparing performance of: Splice vs Concat vs Spread

Created: 4 years ago by: Guest

Jump to the latest result

Splice

Concat

Spread

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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