Benchmark: dadsforEach vs reduce vs map vs filter vs forsdddffsfsff qwfdw

Script Preparation code:

var arr = [];
for (var i = 0; i < 12345; i++) {
  arr[i] = i;
}

function someFn(i) {
  return (i * 3 * 8 / 1200 * 0.002 / 40 * 0.2);
}

var sumForEach = 0,
  sumReduce = 0,
  sumMap = 0,
  sumFilter = 0,
  sumFor = 0;

Tests:

map
arr.map(item => { if(item == 123) { return item * 2; } return item; });
for
var hung = [] for (var j = 0; j < arr.length; j++) { if(arr[j] == 123) { hung[j] = arr[j] * 2; } hung[j] = arr[j]; }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
map
for

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

This benchmark does not have any results yet. Be the first one to run it!

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

LLMs can make mistakes. Check important info.

I'll break down the provided benchmark definition and test cases, explaining what's being tested, compared, and their pros and cons.

**Benchmark Definition JSON:**

The benchmark is testing four different approaches to perform an operation on an array:

1. `map()`
2. `for` loop
3. `reduce()` (although it seems like a typo and should be `filter()` based on the test case)
4. `filter()` (not explicitly stated, but implied by the "forsdddffsfsfs qwfdw" name)

**Script Preparation Code:**

The script creates an array `arr` with 12345 elements and assigns each element a unique value using the `someFn(i)` function.

```javascript
function someFn(i) {
    return (i * 3 * 8 / 1200 * 0.002 / 40 * 0.2);
}
```

This function is used to calculate a complex expression and returns its result, which will be used as the value for each element in the array.

**Html Preparation Code:**

There is no HTML preparation code provided.

**Individual Test Cases:**

There are two test cases:

1. **map**

The benchmark definition for this test case uses the `map()` method to create a new array with the same number of elements as the original array, but with each element being multiplied by 2 if it's equal to 123.

```javascript
arr.map(item => {
    if (item == 123) {
        return item * 2;
    }
    return item;
});
```

2. **for**

The benchmark definition for this test case uses a `for` loop to create a new array with the same number of elements as the original array, but with each element being multiplied by 2 if it's equal to 123.

```javascript
var hung = [];
for (var j = 0; j < arr.length; j++) {
    if (arr[j] == 123) {
        hung[j] = arr[j] * 2;
    }
    hung[j] = arr[j];
}
```

**Pros and Cons of Each Approach:**

1. **map()**
	* Pros:
		+ Concise and readable code
		+ No explicit loop or indexing required
	* Cons:
		+ Can be slower than `for` loops due to the overhead of creating a new array
2. **for**
	* Pros:
		+ Can be faster than `map()` due to avoiding array creation overhead
		+ Allows for more control over the iteration process
	* Cons:
		+ Requires explicit loop and indexing management
3. **filter()** (implied by the "forsdddffsfsfs qwfdw" name)
	* Pros:
		+ Concise code, similar to `map()`
	* Cons:
		+ Not explicitly stated in the benchmark definition, so it's unclear if this is a typo or intentional

**Other Considerations:**

* The use of `someFn(i)` to calculate complex expressions might introduce unnecessary overhead.
* The benchmark only tests these four approaches, which might not be representative of all possible use cases.

**Alternatives:**

If you want to test other approaches, consider adding additional test cases for:

* `reduce()`
* `forEach()` (a built-in method in JavaScript)
* Other libraries or frameworks that provide similar functionality (e.g., Lodash)

Keep in mind that the specific alternatives will depend on the requirements and goals of the benchmark.

Related benchmarks:

dadsforEach vs reduce vs map vs filter vs forsdddffsfsff qwfdw (version: 0)

Comparing performance of: map vs for

Created: 3 years ago by: Guest

Jump to the latest result

map

for

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):