Benchmark: Spread vs Array.prototype.concat

Script Preparation code:

function generateRandomNumbers(m) {
  const array = new Uint32Array(m);
  window.crypto.getRandomValues(array);
  return array;
}

function generateRandomArrays(n, m) {
  const array = [];
  for (let i = 0; i < n; i++) {
	array.push(generateRandomNumbers(m))
  }
  return array;
}


var array = generateRandomArrays(1e3, 1e2);

Tests:

Array.prototype.concat
b = array.reduce((accumulator, value) => accumulator.concat(value));
Spead
b = array.reduce((accumulator, value) => [...accumulator, ...value])

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Array.prototype.concat
Spead

Fastest: N/A

Slowest: N/A

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

LLMs can make mistakes. Check important info.

Let's break down the provided JSON and explain what is being tested in the benchmark.

**Benchmark Definition**

The benchmark is testing two approaches to combine arrays: using `Array.prototype.concat` and using the spread operator (`...`). The test case is defined as:

1. Creating a large array of random numbers with `generateRandomNumbers(m)` function.
2. Reducing the array by concatenating each element with the accumulator, using `Array.prototype.concat`.
3. Reducing the array by spreading each element into the accumulator.

**Options Compared**

The two options being compared are:

* Using `Array.prototype.concat` to combine arrays.
* Using the spread operator (`...`) to combine arrays.

**Pros and Cons of Each Approach**

1. **Using `Array.prototype.concat`**:
	* Pros: This method is widely supported across browsers and has been used for a long time, making it a familiar choice for many developers.
	* Cons: It creates a new array, which can lead to increased memory usage, especially when dealing with large arrays. Additionally, it's generally less efficient than the spread operator due to its use of `Array.prototype.concat`.
2. **Using the Spread Operator (`...`)**:
	* Pros: This method is more efficient and uses less memory than `Array.prototype.concat`, as it only creates a new array reference without actually creating a new array.
	* Cons: The spread operator requires support in modern browsers, which might not be the case for older versions. Additionally, some developers might find it less familiar or less readable.

**Library Used**

None of the provided code uses a library beyond JavaScript's built-in `Array` prototype methods and the `Uint32Array` type.

**Special JS Feature/Syntax**

The test case uses the spread operator (`...`) which is a modern JavaScript feature introduced in ECMAScript 2015 (ES6). This means that older browsers or environments without ES6 support might not be able to run this benchmark.

**Other Alternatives**

If you're concerned about using the spread operator, an alternative approach could be to use `Array.prototype.push.apply()` instead:

```javascript
function reduceWithPushApply(array, callback) {
  const accumulator = [];
  for (let i = 0; i < array.length; i++) {
    callback(accumulator, array[i]);
  }
  return accumulator;
}
```

However, this approach still creates a new array reference and might not be as efficient as using the spread operator.

If you need to support older browsers or environments without ES6 support, alternative methods like `Array.prototype.push.apply()` or even using `Array.prototype.concat()` could be used. However, these approaches have their own trade-offs in terms of performance and memory usage.

It's worth noting that MeasureThat.net provides detailed results for each benchmark, including execution time per second, which can help you decide on the best approach for your specific use case.

Related benchmarks:

Spread vs Array.prototype.concat (version: 0)

Comparing performance of: Array.prototype.concat vs Spead

Created: 7 years ago by: Guest

Jump to the latest result

Array.prototype.concat

Spead

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):