Benchmark: Filter Reduce vs. For

Script Preparation code:

var N = 100000;

Tests:

Filter Reduce
var ans = Array(N).fill(0).filter((_, i) => i % 3 === 0 || i % 5 === 0) .reduce((acc, i) => acc += i);
For
var ans = 0; for (var i = 0; i < N; ++i) { if (i % 3 === 0 || i % 5 === 0) ans += i; }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Filter Reduce
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):

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I'll break down the benchmark and explain what's being tested, compared, and the pros and cons of each approach.

**Benchmark Overview**

The benchmark is designed to compare the performance of two approaches: `Filter Reduce` and `For`. The goal is to find out which approach is faster for a specific task: filtering an array and summing up the values that meet certain conditions (in this case, numbers divisible by 3 or 5).

**Script Preparation Code**

The script preparation code defines a constant `N`, which represents the number of elements in the array. In this case, `N` is set to 100,000.

**Html Preparation Code**

There is no HTML preparation code provided, which means that the benchmark only tests the JavaScript performance and ignores any UI-related aspects.

**Benchmark Test Cases**

There are two test cases:

1. **Filter Reduce**: This approach uses the `Array.prototype.filter()` method to filter out elements that don't meet the condition (i.e., numbers not divisible by 3 or 5). The resulting array is then passed to the `Array.prototype.reduce()` method to sum up the values.
2. **For**: This approach uses a traditional `for` loop to iterate through the array, checking each element individually for the condition.

**Library and Purpose**

There are no external libraries used in this benchmark. However, the `Array.prototype.filter()` and `Array.prototype.reduce()` methods are built-in JavaScript methods that operate on arrays.

**Special JS Feature/Syntax**

None of the benchmark test cases use any special JavaScript features or syntax beyond the standard `for` loop and array methods.

**Comparison and Pros/Cons**

The main difference between the two approaches is how they handle the filtering and summation process:

* **Filter Reduce**: This approach filters out elements in a single step, which can lead to better cache locality and reduced overhead. However, it requires creating an intermediate array, which may consume more memory.
* **For**: This approach iterates through the array in multiple steps, which can lead to slower performance due to increased overhead and decreased cache locality.

In general, `Filter Reduce` is expected to perform better for large datasets, as it reduces the number of iterations required. However, for smaller datasets or when memory is a concern, `For` might be a better choice.

**Other Alternatives**

If you wanted to explore alternative approaches, here are a few options:

* **MapReduce**: Instead of using `Array.prototype.filter()` and `Array.prototype.reduce()`, you could use the `Array.prototype.map()` method followed by another array method (e.g., `Array.prototype.reduce()`). This approach might be more suitable for smaller datasets or when memory is limited.
* **Linear Scan**: You could rewrite the `For` loop using a linear scan algorithm, where you iterate through the array and perform calculations on each element. This approach would eliminate the overhead of creating an intermediate array.

However, these alternatives are not mentioned in the benchmark provided, as they were not specified in the test cases or script preparation code.

Related benchmarks:

Filter Reduce vs. For (version: 0)

Comparing performance of: Filter Reduce vs For

Created: 3 years ago by: Guest

Jump to the latest result

Filter Reduce

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):