Benchmark: filter-vs-reduce

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:

filter
arr.filter(item => (sumFilter += someFn(item)));
reduce
sumReduce = arr.reduce((lastValue, item) => { return sumReduce += someFn(item); });

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
filter
reduce

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.

Let's break down the provided benchmark definition and test cases to understand what's being tested.

**Benchmark Definition JSON**

The benchmark is defined as two separate tests:

1. `filter-vs-reduce`: This is not a specific individual test, but rather a container for multiple test cases. It seems to be an example of how to create a benchmark with multiple test cases.
2. `arr.filter(item => (sumFilter += someFn(item)));` and `sumReduce = arr.reduce((lastValue, item) => {\r\n  return sumReduce += someFn(item);\r\n});`: These are individual test cases.

**Individual Test Cases**

The two test cases measure the performance of JavaScript arrays using the `filter()` and `reduce()` methods, respectively. Both tests use a common function `someFn(i)` to compute a value based on the input parameter `i`. The purpose of this function is not explicitly stated in the benchmark definition, but it appears to be a simple calculation.

**Options Compared**

The two test cases compare the performance of:

1. **Filtering**: Using the `filter()` method to create a new array with elements that pass a certain condition.
2. **Reducing**: Using the `reduce()` method to accumulate a value from an iterable (in this case, an array) by applying a function to each element.

**Pros and Cons**

* **Filtering**:
	+ Pros: Can be more efficient for small datasets or when only a subset of elements is needed.
	+ Cons: Creates a new array with additional memory allocation and potential overhead.
* **Reducing**:
	+ Pros: Accumulates values in a single variable, which can be more memory-efficient than creating a new array.
	+ Cons: Can be slower for large datasets due to the accumulation process.

**Library**

None. The benchmark only uses built-in JavaScript methods (`filter()` and `reduce()`) without referencing any external libraries.

**Special JS Feature or Syntax**

The benchmark does not use any special JavaScript features or syntax that would affect its performance characteristics. It's a straightforward comparison of two common array processing methods.

**Other Considerations**

When running this benchmark, consider the following:

* The size of the input dataset (`arr` in the example code).
* The complexity of the `someFn(i)` function.
* The target use case (e.g., client-side vs. server-side applications).

**Alternatives**

Other alternatives for array processing methods include:

1. **Map()**: Similar to `filter()` but returns a new array with transformed values instead of filtering elements out.
2. **forEach()`: Iterates over an array without accumulating values, which can be faster for large datasets.
3. **Array.prototype.every()`, `Array.prototype.some()`: Similar to `filter()` but return boolean results indicating whether all or at least one element passes a condition.

Keep in mind that the performance characteristics of these alternatives might vary depending on the specific use case and input data.

Related benchmarks:

filter-vs-reduce (version: 0)

Comparing performance of: filter vs reduce

Created: 5 years ago by: Guest

Jump to the latest result

filter

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):