Benchmark: Benchmark: flatMap vs reduce vs while vs foreach (40k)

Script Preparation code:

var n = 40000

Tests:

flatMap
[...Array(n)].flatMap((x,i) => [++i,i++])
reduce
[...Array(n).keys()].reduce((acc, cur) => [...acc, ++cur, cur++], [])
while
let res = [], i=0; while(n>i++) res.push(i, i);
foreach
let res = []; Array(n).forEach((_,i) => res.push(i+1, i+1))

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
flatMap
reduce
while
foreach

Fastest: N/A

Slowest: N/A

Latest run results:

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

LLMs can make mistakes. Check important info.

The provided JSON represents a JavaScript microbenchmark test case, specifically designed to compare the performance of four different iteration methods: `flatMap`, `reduce`, `while`, and `forEach`. The test creates an array of size 40,000 using the script preparation code, and then applies each iteration method to produce the same result.

Here's a breakdown of what's being tested:

1. **`flatMap`**: This method flattens an array by applying a transformation function to each element, which returns an array. The test case uses `++i` and `i++` as the transformation function.
2. **`reduce`**: This method applies a reduction function to an accumulator, accumulating values as it iterates through the array. The test case uses an initial value of an empty array `[]`, and then pushes the transformed values into the accumulator using `push`.
3. **`while`**: This method uses a loop variable `i` that increments on each iteration, which is pushed onto the result array.
4. **`forEach`**: This method applies a callback function to each element of an array, executing it once for each value.

Now, let's discuss the pros and cons of each approach:

*   **`flatMap`**:
    *   Pros: Can be more efficient than `reduce` when dealing with large arrays, as it avoids the overhead of creating intermediate accumulators. It also allows for more concise code.
    *   Cons: May have higher memory usage due to the creation of new arrays.
*   **`reduce`**:
    *   Pros: Allows for more flexibility in handling different types of data and can be used with other methods like `map`.
    *   Cons: Can be less efficient than `flatMap` for large arrays, as it creates intermediate accumulators. It's also more verbose in this case.
*   **`while`**:
    *   Pros: Allows for direct control over the loop variable and can be used to avoid creating temporary variables.
    *   Cons: Less concise and more error-prone than other methods, especially when dealing with complex logic.
*   **`forEach`**:
    *   Pros: Easy to use and readable, as it's a simple callback-based method.
    *   Cons: Limited control over the iteration process and can be less efficient than other methods.

As for the libraries used in this test case, there isn't any explicitly mentioned. However, `Array.prototype.flatMap` is a standard JavaScript method introduced in ECMAScript 2019 (ES10).

The special JavaScript feature used here is the ability to use arrow functions (`=>`) and template literals (`\r\n`) as shorthand syntax for function definitions.

Now, let's discuss some alternatives to these methods:

*   **`Array.prototype.map`**: Instead of `flatMap`, you could use `map` followed by a call to `flat()` or `flatMap()`. However, this would still require creating intermediate arrays.
*   **Other reduction methods**: There are other methods available for reducing arrays, such as `every()` and `some()`.
*   **Loop constructs**: In addition to `while`, you could use `for` loops or even recursive functions to iterate over the array.

As for specific browser features or syntax, there isn't any mentioned here.

Related benchmarks:

Benchmark: flatMap vs reduce vs while vs foreach (40k) (version: 0)

Comparing performance of: flatMap vs reduce vs while vs foreach

Created: 3 years ago by: Guest

Jump to the latest result

flatMap

reduce

while

foreach

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):