Benchmark: Native map & filter vs reduce with spread

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
arr.map(item => item.num).filter(num => num > 15 && num < 30)
Reduce
arr.reduce((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 benchmark and its test cases.

**Benchmark Definition JSON**

The provided JSON defines a JavaScript microbenchmarking framework, where users can create and run benchmarks to measure the performance of different approaches. The benchmark is called "Native map & filter vs reduce with spread", and it compares two approaches:

1. **Lodash's `map` and `filter` functions**: These functions are part of the Lodash library, which provides a set of utility functions for functional programming.
2. **Native JavaScript `reduce` function with spread syntax**: This approach uses the `reduce` method to iterate over an array and create a new array with elements that satisfy a certain condition.

**Pros and Cons**

Both approaches have their strengths and weaknesses:

* **Lodash's `map` and `filter` functions**: These functions are designed for functional programming and provide a concise way to transform arrays. However, they may incur a higher overhead due to the library's presence.
	+ Pros: Concise code, easy to read and maintain.
	+ Cons: May be slower due to the library's overhead.
* **Native JavaScript `reduce` function with spread syntax**: This approach uses a more traditional iterative method to transform arrays. It is often faster than using libraries like Lodash, but can be more verbose and less readable.
	+ Pros: Faster execution time, no library overhead.
	+ Cons: May require more code, harder to read and maintain.

**Library: Lodash**

Lodash is a popular JavaScript library that provides a set of utility functions for functional programming. The `map` and `filter` functions are part of the core library, which means they are optimized for performance. However, using these functions may incur an overhead due to the library's presence.

**Special JS Feature/Syntax**

There doesn't appear to be any special JavaScript features or syntax being used in this benchmark. The code uses standard JavaScript features and syntax.

**Other Alternatives**

If you want to compare other approaches for filtering and mapping arrays, here are some alternatives:

* Using `forEach` loop: This approach would use a traditional iterative method to iterate over the array and filter out elements that don't satisfy the condition.
* Using `Array.prototype.some()` function: This function checks if at least one element in the array satisfies a certain condition. It can be used as an alternative to `filter`.
* Using `Array.prototype.every()` function: This function checks if all elements in the array satisfy a certain condition. It can be used as an alternative to `every`.

Keep in mind that these alternatives may have different performance characteristics compared to the approaches tested in this benchmark.

**Benchmark Preparation Code**

The provided code prepares an array with 1 million elements, each containing a `num` property. This array is used as input for both benchmarking approaches.

**Individual Test Cases**

There are two test cases:

* **Filter & Map**: This test case uses the Lodash `map` and `filter` functions to transform the array.
* **Reduce**: This test case uses the native JavaScript `reduce` function with spread syntax to transform the array.

Related benchmarks:

Native map & filter vs reduce with spread (version: 6)

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):