Benchmark: flatMap vs reduce 99984545987325

Script Preparation code:

var values = []
for(i=0; i<10000; i++){ values.push(i) }

Tests:

flatMap
values.flatMap(i => [i, i * 2, i *3])
reduce
values.reduce((acc, x) => { acc.push(x, x * 2, x * 3) return acc; }, [])

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
flatMap
reduce

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 benchmark and analyze what's being tested.

**Benchmark Definition**

The benchmark defines two test cases:

1. `flatMap`: This function takes an array of values as input, and for each value, it creates a new array with three transformed versions of that value: the original value multiplied by 2 (`i * 2`), the original value multiplied by 3 (`i * 3`), and an empty array containing only the original value.
2. `reduce`: This function takes an accumulator array as input, and for each value in the input array, it pushes three transformed versions of that value onto the accumulator array: the value itself, the value multiplied by 2, and the value multiplied by 3.

**Script Preparation Code**

The script preparation code creates a large array `values` with 10,000 elements, where each element is an integer from 0 to 9,999.

**Html Preparation Code**

There is no HTML preparation code provided.

**Test Cases**

The test cases compare the performance of `flatMap` and `reduce` functions in executing their respective transformations on the `values` array. The benchmark measures the time taken by each function to execute its transformation and reports the results.

**Pros and Cons**

1. **flatMap**:
	* Pros: This approach is concise and expressive, as it uses a built-in Array method to create a new array with transformed values.
	* Cons: This approach may lead to unexpected behavior if the original array is modified concurrently, as `flatMap` will return an empty array when the input array is exhausted. Additionally, `flatMap` may be slower than `reduce` due to its overhead of creating a new array.
2. **reduce**:
	* Pros: This approach can be more efficient than `flatMap`, especially for large arrays, since it avoids the overhead of creating a new array and returns an accumulator array that can be modified in place.
	* Cons: This approach is less concise and expressive, as it requires explicitly pushing transformed values onto the accumulator array.

**Library/Util Library**

None.

**Special JS Features/Syntax**

None mentioned.

**Alternatives**

Other approaches to achieve similar results could include:

1. Using `map` and `concat`:
```javascript
values.map(i => [i, i * 2, i * 3]).reduce((acc, arr) => acc.concat(arr), [])
```
This approach is less efficient than `flatMap`, as it creates multiple intermediate arrays.

2. Using a custom loop:
```javascript
let result = [];
for (let i = 0; i < values.length; i++) {
  const x = values[i];
  result.push(x);
  result.push(x * 2);
  result.push(x * 3);
}
return result;
```
This approach is less concise and expressive than `flatMap`, but can be more efficient for very large arrays.

Keep in mind that these alternatives may have different performance characteristics and trade-offs compared to the original `flatMap` and `reduce` approaches.

Related benchmarks:

flatMap vs reduce 99984545987325 (version: 0)

Comparing performance of: flatMap vs reduce

Created: 3 years ago by: Guest

Jump to the latest result

flatMap

reduce

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):