Benchmark: Lodash map & filter vs reduce with push

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:

map & filter
_.filter(_.map(arr, item => item.num), num => num > 15 && num < 30);
reduce
_.reduce(arr, (newArr, item) => { if (item.num > 15 && item.num < 30) { newArr.push(item.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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Let's dive into the world of MeasureThat.net and explore what's being tested in this benchmark.

**Benchmark Overview**

The benchmark is designed to compare the performance of two approaches: using Lodash's `map` and `filter` functions versus using `reduce` with a custom function. The test case uses an array of 1 million objects, each containing a numerical value (`num`). The goal is to filter out numbers outside the range of 15-30 and push the remaining values into a new array.

**Options Being Compared**

There are two main options being compared:

1. **Lodash's `map` and `filter` functions**: This approach uses Lodash's `map` function to create a new array with transformed values, and then uses its `filter` function to filter out numbers outside the specified range.
2. **`reduce` with custom push**: This approach uses the `reduce` method to iterate over the array, pushing each number within the range (15-30) into a new array.

**Pros and Cons of Each Approach**

**Lodash's `map` and `filter` functions:**

Pros:

* Convenient and concise syntax
* Optimized for performance by Lodash

Cons:

* May incur additional overhead due to the use of an external library
* May not be as efficient as a custom implementation for very large datasets

**`reduce` with custom push:**

Pros:

* Can be optimized for performance without relying on an external library
* Allows for fine-grained control over the filtering process
* May be faster for very large datasets due to the lack of overhead from Lodash

Cons:

* Requires more code and manual management of state (the new array)
* May not be as concise or readable for complex filtering logic

**Library Used: Lodash**

Lodash is a popular JavaScript library that provides a set of utility functions, including `map`, `filter`, `reduce`, and many others. Its purpose is to simplify common programming tasks and provide a consistent interface for working with data. In this benchmark, Lodash's `map` and `filter` functions are used to perform the filtering operation.

**Special JavaScript Features or Syntax**

None mentioned in this benchmark. The code only uses standard JavaScript syntax and features.

**Alternative Approaches**

Other approaches that could be considered include:

* Using a custom implementation of the filtering logic without relying on Lodash's `map` and `filter` functions
* Using a different library or framework for data processing, such as D3.js or Chart.js
* Employing parallel processing techniques to speed up the filtering operation

Keep in mind that these alternative approaches may require more significant changes to the codebase and may not offer significant performance improvements over Lodash's optimized implementation.

Related benchmarks:

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

Lodash map & filter vs reduce with push

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):