Benchmark: Native 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
arr.map(item => item.num).filter(num => num > 15 && num < 30);
reduce
arr.reduce((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:

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.

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**Benchmark Definition JSON**

The provided benchmark definition is for comparing two approaches to data processing: `map` and `filter` with `reduce` in combination with `push`. The script preparation code creates an array of 10,000 objects, each with a unique `num` property. The HTML preparation code includes the Lodash library.

**Options Compared**

The benchmark compares two options:

1. **Native map & filter**: Using native JavaScript methods for filtering and mapping data.
2. **Reduce with push**: Using the `reduce` method to combine multiple operations into one, including filtering and pushing new values.

**Pros and Cons of Each Approach**

### Native Map & Filter

Pros:

* Fast execution due to native implementation
* Simple and straightforward code structure

Cons:

* Requires manual handling of edge cases (e.g., null or undefined values)
* May not be as efficient for large datasets

### Reduce with Push

Pros:

* Efficient for large datasets, as it reduces the number of iterations
* Can handle edge cases automatically (e.g., null or undefined values)

Cons:

* More complex code structure due to the use of `reduce`
* May have performance overhead due to the creation and management of intermediate arrays

**Library: Lodash**

In this benchmark, Lodash is used for its `map` and `filter` functions. Lodash provides a simple and efficient way to perform common data processing tasks, making it a popular choice among developers.

**Special JavaScript Feature/Syntax**

The test cases use the `=>` syntax for arrow functions, which is a shorthand way of defining small, single-purpose functions in JavaScript. This syntax was introduced in ECMAScript 2015 (ES6) and has since become a standard part of modern JavaScript.

**Other Alternatives**

If you want to explore alternative approaches or libraries, here are some options:

* For large datasets, consider using other libraries like `d3.js` for data manipulation and visualization.
* For specific use cases, such as image processing or graphics rendering, look into libraries like `canvas.js` or `sharp`.
* For more complex data processing tasks, explore frameworks like React, Angular, or Vue.js.

When working with JavaScript benchmarks like this one, keep in mind that the performance differences between approaches can be significant. Experimenting with different techniques and libraries can help you write more efficient code and improve your overall development workflow.

Related benchmarks:

Native map & filter vs reduce with push and desctructuring (10 000 samples ) (version: 0)

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):