Benchmark: Lodash map & filter vs reduce with spread (2)

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 < 1000000; i++) {
  arr[i] = { num: i };
}

Tests:

Filter & Map
_.filter(_.map(arr, item => item.num), num => num > 15 && num < 30)
Reduce
_.reduce(arr, (newArr, item) => ( item.num > 15 && item.num < 30 ? [...newArr, item.num] : newArr ), [])

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Filter & Map
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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Let's break down the provided benchmark and its test cases.

**Benchmark Overview**

The benchmark measures the performance of two approaches for filtering and mapping data in JavaScript: `Lodash map & filter vs reduce with spread`. The goal is to compare how efficient these methods are when dealing with large datasets.

**Script Preparation Code**

The script preparation code creates an array of 1,000,000 objects, each containing a single property `num` initialized with a unique integer value. This array serves as the input data for the benchmarking tests.

**Html Preparation Code**

The HTML preparation code includes a reference to the Lodash library, version 4.17.5, which is used in the benchmarking tests.

**Individual Test Cases**

There are two test cases:

1. **Filter & Map**: This test case uses the `_.filter` method from Lodash, followed by the `_.map` method, to filter and transform the input array.
	* The filtering condition checks if a number is greater than 15 and less than 30.
	* The mapping function returns the filtered numbers.
2. **Reduce**: This test case uses the `_reduce` method from Lodash to reduce the input array into an array of numbers that satisfy the filtering condition (i.e., between 15 and 30).

**Pros and Cons**

1. **Lodash `map` & `filter`**:
	* Pros: Easy to read and write, concise code.
	* Cons: May be slower due to the creation of intermediate arrays.
2. **Lodash `reduce` with spread**:
	* Pros: Can be faster since it avoids creating intermediate arrays.
	* Cons: The syntax can be less readable, especially for those unfamiliar with the reduce method.

**Library and Purpose**

The Lodash library is a popular utility library for JavaScript that provides a collection of functional programming helpers. In this benchmark, Lodash's `map`, `filter`, and `reduce` methods are used to simplify the code and make it more concise.

**Special JS Feature/Syntax**

There are no specific JavaScript features or syntaxes mentioned in this benchmark that require special attention. However, it's worth noting that some older browsers may not support modern JavaScript features like arrow functions, template literals, or spread operators (used in the `reduce` method).

**Alternatives**

Other alternatives for filtering and mapping data include:

1. **Native JavaScript methods**: Using built-in array methods like `filter`, `map`, and `reduce` can be a viable alternative.
2. **VanillaJS libraries**: Other utility libraries, such as Underscore.js or Ramda, offer similar functionality to Lodash.

Keep in mind that the performance differences between these alternatives may vary depending on the specific use case and hardware configuration.

When choosing an approach, consider factors like code readability, maintainability, and performance. In this benchmark, both approaches have their trade-offs, but the `reduce` method with spread seems to be faster according to the latest results.

Related benchmarks:

Lodash map & filter vs reduce with spread (2) (version: 0)

Lodash map & filter vs reduce with spread

Comparing performance of: Filter & Map vs Reduce

Created: 4 years ago by: Registered User

Jump to the latest result

Filter & Map

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):