Benchmark: Lodash map & filter vs reduce with push and desctructuring (10 000 samples )

HTML Preparation code:

<script src='https://cdnjs.cloudflare.com/ajax/libs/lodash.js/4.17.5/lodash.min.js'></script>

Script Preparation code:

var arr = [];
for (var i = 0; i < 10000; i++) {
  arr[i] = { num: i };
}

Tests:

map & filter
_.filter(_.map(arr, item => item.num), num => num > 15 && num < 30);
reduce
_.reduce(arr, (newArr, { num }) => { if (num > 15 && num < 30) { newArr.push(num); } return newArr; }, []);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
map & filter
reduce

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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**Benchmark Explanation**

MeasureThat.net is a website that allows users to create and run JavaScript microbenchmarks. The provided benchmark compares the performance of two approaches: `Lodash map & filter` vs `reduce with push`. In this explanation, we'll break down what each approach does, its pros and cons, and other considerations.

**Script Preparation Code**

The script preparation code initializes an array `arr` with 10,000 elements, where each element is an object containing a single property `num`, which represents the index of the element in the array. This setup is used to create a large dataset for the benchmark.

**Html Preparation Code**

The HTML preparation code includes a reference to the Lodash library, specifically version 4.17.5, via a CDN. Lodash is a utility library that provides various functions for tasks such as array manipulation, string formatting, and more. In this case, we're using it to perform `map` and `filter` operations.

**Individual Test Cases**

There are two test cases:

1. **map & filter**: This test case uses the Lodash `_.map` function to transform each element in the `arr` array into a new object containing only the `num` property, followed by an application of the `_.filter` function to narrow down the filtered results based on conditions that ensure the result's `num` is between 15 and 30.
2. **reduce**: This test case uses the Lodash `_.reduce` function to iterate over the `arr` array, accumulating a new array `newArr` containing only the numbers that fall within the same range as in the `map & filter` test case.

**Pros and Cons of Each Approach**

1.  **Lodash Map & Filter:**
    *   **Pros:**
        *   Easier to write, with less boilerplate code.
        *   Built-in support for functional programming patterns.
    *   **Cons:**
        *   May have a higher overhead due to the added layer of abstraction.
        *   Can be slower than direct implementation if optimized correctly.

2.  **Reduce With Push:**
    *   **Pros:**
        *   Often faster, as it avoids unnecessary function calls and returns early when finished iterating over all array elements.
        *   Provides more control over the execution flow and state changes.
    *   **Cons:**
        *   Requires a deeper understanding of functional programming concepts.
        *   May be less readable due to its concise syntax.

**Library: Lodash**

Lodash is a JavaScript library that provides various utility functions for tasks such as array manipulation, string formatting, and more. It includes functions like `_.map`, `_filter`, `_reduce`, and others that are commonly used in functional programming. In this benchmark, we're using Lodash to perform the `map` and `filter` operations.

**Special JS Feature/Syntax**

None of the provided test cases explicitly use special JavaScript features or syntax beyond what's considered standard for most developers. However, it's worth noting that some specific optimizations might be used in production code depending on context.

**Other Alternatives**

For this type of benchmark, alternatives like using native JavaScript functions (e.g., `map`, `filter`) instead of Lodash can be considered. This approach would involve implementing the same logic manually, but with less overhead since it avoids calling external library functions.

Related benchmarks:

Lodash map & filter vs reduce with push and desctructuring (10 000 samples ) (version: 1)

Lodash map & filter vs reduce with push and desctructuring

Comparing performance of: map & filter vs reduce

Created: 4 years ago by: Registered User

Jump to the latest result

map & 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):