Benchmark: flat() vs reduce/concat()

Script Preparation code:

var params = [[ 1, 2 ], [ "hello", true, 7 ]];

Tests:

reduce + concat
params.reduce((acc, val) => acc.concat(val), []);
flat
params.flat();

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
reduce + concat
flat

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 3 years ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/94.0.4606.81 Safari/537.36

Browser/OS: Chrome 94 on Windows

View result in a separate tab

Test name	Executions per second
reduce + concat	3133891.0 Ops/sec
flat	712809.0 Ops/sec

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

LLMs can make mistakes. Check important info.

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

**Benchmark Description**

The test compares two approaches to flatten an array of arrays in JavaScript: the `flat()` method versus the `concat()` method within the `reduce()` function.

**Script Preparation Code**

The script preparation code defines a variable `params` with an array of arrays:
```javascript
var params = [[ 1, 2 ], [ "hello", true, 7 ));
```
This is just a setup for the benchmarking process and doesn't affect the actual test.

**Html Preparation Code**

There's no HTML preparation code provided, which means this benchmark is focusing solely on JavaScript performance and doesn't involve any HTML rendering or DOM manipulation.

**Individual Test Cases**

The benchmark consists of two individual test cases:

1. `reduce + concat`: This test uses the `reduce()` function to concatenate all elements in each sub-array using `concat()`.
2. `flat`: This test uses the `flat()` method to flatten the array of arrays directly.

**Libraries and Special JS Features**

There are no libraries mentioned, as this benchmark is focusing on native JavaScript methods.

**Test Case Interpretation**

In the first test case (`reduce + concat`), the `reduce()` function is used to iterate over each sub-array in `params`, concatenating all elements using `concat()`. The initial accumulator (`acc`) starts empty (`[]`).

The second test case (`flat`) uses the `flat()` method, which returns a new array with all sub-arrays flattened.

**Pros and Cons of Each Approach**

1. **Reduce + Concat**: This approach can be more flexible since it allows for any iteration logic using the `reduce()` function.
	* Pros: More control over the iteration process.
	* Cons: Can lead to higher overhead due to the function call overhead and repeated concatenations.
2. **Flat**: The `flat()` method is a concise and efficient way to flatten arrays, with less overhead compared to `concat()`.
	* Pros: Faster execution, less memory allocation.
	* Cons: Less control over the iteration process.

**Other Considerations**

The test doesn't consider factors like:

* The size of the input array
* The distribution of elements in each sub-array (e.g., if some arrays contain many large values)
* Any potential performance impact from other operations happening concurrently

**Alternatives to Native Methods**

If you need more control or flexibility, you could consider using other methods or libraries, such as:

* `Array.prototype.flat.call()`: This method is similar to `flat()` but can be called on any array, not just native arrays.
* `Array.prototype.map().flat()`: This approach flattens an array of arrays by mapping each element to an array and then flatting the result.
* Third-party libraries like `lodash` or `ramda`, which provide additional functional programming utilities.

However, for simple cases where performance is crucial, the native methods (`flat()` and `concat()`) are usually sufficient.

Related benchmarks:

flat() vs reduce/concat() (version: 2)

Compare the .flat() method with the .concat() method in .reduce()

Comparing performance of: reduce + concat vs flat

Created: 3 years ago by: Registered User

Jump to the latest result

reduce + concat

flat

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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