Benchmark: reduce.concat() vs flat() vs spread

Tests:

reduce + Array.prototype.concat
var params = [[ 1, 2 ], [ "hello", true, 7 ]]; var other = params.reduce((acc, val) => acc.concat(val), []);
Array.prototype.flat
var params = [[1, 2, params], [ "hello", true, 7 ]]; var other = params.flat();
... spread
var params = [[ 1, 2 ], [ "hello", true, 7 ]]; var other = params.reduce((acc, val) => [...acc, val], []);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
reduce + Array.prototype.concat
Array.prototype.flat
... spread

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 the provided benchmark and explain what's being tested.

**Benchmark Definition**

The benchmark compares three different approaches to concatenate arrays in JavaScript:

1. `Array.prototype.concat()`
2. `Array.prototype.flat()` (a relatively new method introduced in ECMAScript 2019)
3. Spread operator (`...`)

These methods are used to combine one or more arrays into a single array.

**Options Compared**

The benchmark is comparing the performance of these three methods:

* `reduce + Array.prototype.concat`: This method uses the `reduce()` method to iterate over the arrays and concatenate them using `Array.prototype.concat()`.
* `Array.prototype.flat()`: This method uses the `flat()` method to flatten an array with nested arrays.
* Spread operator (`...`): This method uses the spread operator to create a new array by concatenating elements from multiple arrays.

**Pros and Cons of Each Approach**

1. **Reduce + Array.prototype.concat**:
	* Pros: Well-established, widely supported, easy to understand.
	* Cons: May be slower due to the overhead of the `reduce()` method and `Array.prototype.concat()`.
2. **Array.prototype.flat()**:
	* Pros: Efficient, modern method that's gaining popularity.
	* Cons: May not work as expected if the arrays are deeply nested or have circular references. Also, older browsers may not support it.
3. **Spread operator (`...`)**:
	* Pros: Concise, expressive, and widely supported in recent browsers.
	* Cons: May not be suitable for very large datasets due to memory constraints.

**Library Usage**

None of the test cases use any external libraries.

**Special JS Features or Syntax**

The benchmark uses a feature that's relatively new: `Array.prototype.flat()`. This method was introduced in ECMAScript 2019 and is supported by most modern browsers. However, it may not work as expected in older browsers.

**Other Considerations**

When choosing an approach, consider the specific use case and requirements:

* For simple concatenation tasks, spread operator (`...`) might be a good choice due to its conciseness and wide support.
* For more complex scenarios or large datasets, `Array.prototype.flat()` might be a better option if you need efficient and modern performance.
* If you're targeting older browsers that don't support `Array.prototype.flat()`, the spread operator (`...`) or the traditional approach with `Array.prototype.concat()` might be necessary.

**Alternative Approaches**

If you need to concatenate arrays, here are some alternative approaches:

1. Using `Array.prototype.push()`: This method is simple but may not be as efficient as the other options.
2. Using a custom loop: You can write a simple loop to iterate over the arrays and push elements into a new array.
3. Using a library like Lodash or Ramda: These libraries provide various utility functions, including array concatenation methods.

Keep in mind that the choice of approach depends on your specific requirements, performance constraints, and target browsers.

Related benchmarks:

reduce.concat() vs flat() vs spread (version: 0)

Compare array concatination reduce, spread, flat

Comparing performance of: reduce + Array.prototype.concat vs Array.prototype.flat vs ... spread

Created: 4 years ago by: Guest

Jump to the latest result

reduce + Array.prototype.concat

Array.prototype.flat

... spread

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):