Benchmark: forEach vs reduce vs map vs filter vs for Test

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:

reduce
sumReduce = arr.reduce((lastValue, item) => { return lastValue += someFn(item); });
map
arr.map(item => (sumMap += someFn(item)));
filter
arr.filter(item => (sumFilter += someFn(item)));
for
for (var j = 0; j < arr.length; j++) { sumFor += arr[j]; }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
reduce
map
filter
for

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.

**Benchmark Overview**

The provided benchmark compares the performance of four different approaches to iterate over an array and perform a calculation: `forEach`, `reduce`, `map`, `filter`, and a traditional `for` loop.

**Options Compared**

1. **forEach**: The `forEach` method executes a provided function once for each element in an array, without returning any value.
2. **reduce**: The `reduce` method applies a user-supplied function to each element of the array and reduces it to a single output value.
3. **map**: The `map` method creates a new array with the results of applying a provided function on every element in this array.
4. **filter**: The `filter` method constructs a new array with all elements that pass the test implemented by the provided function.
5. **for**: A traditional loop using a `for` statement to iterate over the array and perform the calculation.

**Pros and Cons of Each Approach**

1. **forEach**:
	* Pros: Easy to read, straightforward implementation.
	* Cons: May not be as efficient as other methods for large datasets, as it creates an intermediate object.
2. **reduce**:
	* Pros: Can reduce the array to a single value, which can be useful in certain scenarios.
	* Cons: Can be slower than `forEach` or traditional loops for small arrays, due to the overhead of accumulating values.
3. **map**:
	* Pros: Creates a new array with transformed values, making it suitable for use in array methods like `concat`, `slice`, etc.
	* Cons: May consume more memory than other methods, especially for large datasets.
4. **filter**:
	* Pros: Returns an array of filtered elements, which can be useful for certain scenarios.
	* Cons: Can be slower than `forEach` or traditional loops for small arrays, due to the overhead of filtering.

5. **for**:
	* Pros: Typically the fastest approach, especially for large datasets.
	* Cons: Requires manual indexing and calculation, making it less readable than other methods.

**Library Used**

None are explicitly mentioned in the benchmark definition or test cases.

**Special JS Feature/Syntax**

The `reduce` method uses an arrow function to define the reduction logic. Arrow functions were introduced in ECMAScript 2015 (ES6) as a concise syntax for defining small, single-expression functions.

**Other Considerations**

* The use of a simple calculation (`i * 3 * 8 / 1200 * 0.002 / 40 * 0.2`) to mask performance differences between methods.
* The large dataset size (12345 elements) used in the benchmark definition, which may not accurately represent performance differences for smaller datasets.

**Alternatives**

Other approaches to iterate over an array and perform a calculation include:

1. `forEach`: Similar to the traditional `for` loop approach, but with more concise syntax.
2. `array.prototype.every()`, `array.prototype.some()`: Method calls that can be used in combination with a callback function.
3. `while` loops: Another traditional looping construct that can be used for array iteration.

It's worth noting that the best approach depends on the specific use case and performance requirements. This benchmark provides a general idea of the relative performance differences between different methods, but may not accurately represent performance in all scenarios.

Related benchmarks:

forEach vs reduce vs map vs filter vs for Test (version: 0)

Comparing performance of: reduce vs map vs filter vs for

Created: 3 years ago by: Registered User

Jump to the latest result

reduce

map

filter

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):