Benchmark: flatMap vs reduce vs reduce (list push)

Script Preparation code:

var arr = Array(10).fill(0)

Tests:

reduce with concat
arr.reduce((acc, x) => [...acc, x, x], [])
flatMap
arr.flatMap(x => [x, x])
reduce with push
arr.reduce((acc, x) => { acc.push(x, x); return acc }, [])

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
reduce with concat
flatMap
reduce with push

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

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

LLMs can make mistakes. Check important info.

**Benchmark Overview**

MeasureThat.net is a website that allows users to create and run JavaScript microbenchmarks. The provided benchmark definition represents three individual test cases: `reduce with concat`, `flatMap`, and `reduce with push`. These test cases aim to compare the performance of different approaches in reducing an array.

**Test Cases and Options Compared**

The three test cases are:

1. **`reduce with concat`**: This test case uses the `Array.prototype.reduce()` method with a callback function that concatenates two elements using the spread operator (`...`).
2. **`flatMap`**: This test case uses the `Array.prototype.flatMap()` method, which flattens an array of arrays into a single array.
3. **`reduce with push`**: This test case uses the `Array.prototype.reduce()` method with a callback function that pushes two elements to the accumulator using the `push()` method.

**Pros and Cons of Each Approach**

Here's a brief overview of each approach:

*   **`reduce with concat`**:
    *   **Pros**: Simple and familiar syntax, easy to understand.
    *   **Cons**: May incur extra overhead due to string concatenation, which can lead to slower performance in modern JavaScript engines.
*   **`flatMap`**:
    *   **Pros**: Efficient use of the spread operator (`...`) for creating new arrays without intermediate steps. Modern browsers and JavaScript engines support `flatMap` more efficiently than `concat`.
    *   **Cons**: Less control over the array structure, as it modifies the original array.
*   **`reduce with push`**:
    *   **Pros**: Can be more efficient than `concat` when dealing with large arrays, as it avoids creating a new string. However, it's still less efficient than `flatMap`.
    *   **Cons**: May incur additional overhead due to the `push()` method, which can lead to slower performance in some cases.

**Library and Purpose**

None of the provided test cases use external libraries. The `Array.prototype.reduce()` and `Array.prototype.flatMap()` methods are part of the JavaScript standard library, providing a way to perform array reduction and flattening, respectively.

**Special JS Feature or Syntax (None)**

There is no special JavaScript feature or syntax used in these test cases beyond the standard library methods mentioned above.

**Other Alternatives**

If you're interested in exploring alternative approaches for array reduction and flattening, here are a few options:

*   **`Array.prototype.map()`**: While not as efficient as `flatMap`, `map()` can be used to flatten an array of arrays into a single array.
*   **`Array.prototype.forEach()`**: This method is generally less efficient than `reduce()` or `flatMap`.
*   **Manual iteration using `for` loops**: You can use manual iteration with `for` loops and indexing to achieve the same results as `reduce()` or `flatMap`. However, this approach typically incurs more overhead and may not be as readable.

Keep in mind that these alternative approaches might not always offer better performance than the tested methods. The best choice of method depends on your specific use case and requirements.

Related benchmarks:

flatMap vs reduce vs reduce (list push) (version: 0)

Comparing performance of: reduce with concat vs flatMap vs reduce with push

Created: 3 years ago by: Guest

Jump to the latest result

reduce with concat

flatMap

reduce with push

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):