Benchmark: ES6 equivalent to lodash _.mapValues without console.log

HTML Preparation code:

<script src="https://raw.githubusercontent.com/lodash/lodash/4.17.15-npm/core.js"></script>

Tests:

native approach with reduce
var fruits= { 'apple': { 'name': 'apple', 'number': 5}, 'orange': { 'name': 'orange', 'number': 10 } }; Object.entries(fruits) .reduce((a, [key, { number }]) => { a[key] = number; return a; }, {} );
native approach fromEntries
var fruits= { 'apple': { 'name': 'apple', 'number': 5}, 'orange': { 'name': 'orange', 'number': 10 } }; Object.fromEntries( Object.entries(fruits).map(([key, { number }]) => [key, number]) );
lodash mapValues
var fruits= { 'apple': { 'name': 'apple', 'number': 5}, 'orange': { 'name': 'orange', 'number': 10 } }; _.mapValues(fruits, 'number');

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
native approach with reduce
native approach fromEntries
lodash mapValues

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 benchmark.

**Overview**

The provided JSON represents a JavaScript microbenchmark test case for measuring the performance of different approaches to map values in an object. The test consists of three individual benchmark definitions, each comparing two methods: a native approach using `reduce`, another native approach using `fromEntries` with `map`, and a third approach using the Lodash library's `mapValues`.

**Native Approaches**

1. **`native approach with reduce`**: This method uses `reduce` to iterate over the object entries, mapping each key-value pair to the value (in this case, the "number" property).
	* Pros: Generally efficient and easy to implement.
	* Cons: Can be slower for large datasets due to the overhead of the `reduce` function. Additionally, it may require more memory allocations than necessary.
2. **`native approach fromEntries`**: This method uses the `fromEntries` method to create a new array of key-value pairs and then maps each pair to an object with only the "number" property.
	* Pros: May be faster than the `reduce` approach, especially for large datasets, as it avoids the overhead of function calls. However, it creates an additional array of objects, which may require more memory allocations.
	* Cons: The `fromEntries` method itself is a relatively expensive operation.

**Lodash Approach**

1. **`lodash mapValues`**: This method uses the Lodash library's `mapValues` function to create a new object with values mapped from the original object.
	* Pros: Generally efficient and easy to implement, as it leverages an optimized implementation in the Lodash library.
	* Cons: Requires including the Lodash library in the test, which may add overhead. Additionally, if not used carefully, it can lead to slower performance due to unnecessary function calls.

**Other Considerations**

* The use of `Object.entries` and `Object.fromEntries` methods is specific to JavaScript versions 26 and above.
* The Safari browser version (16) seems to be quite efficient in executing the benchmark tests. This might indicate that other browsers or versions could produce different results.

**Alternatives**

In addition to these three approaches, other possible methods for mapping values in an object include:

* Using a `forEach` loop with a callback function
* Utilizing modern JavaScript features like `for...of` loops and arrow functions
* Leveraging the `Object.assign()` method (although this might be less efficient than the native approaches)

However, these alternatives are not explicitly tested on MeasureThat.net.

Related benchmarks:

ES6 equivalent to lodash _.mapValues without console.log (version: 0)

Comparing performance of: native approach with reduce vs native approach fromEntries vs lodash mapValues

Created: 5 years ago by: Guest

Jump to the latest result

native approach with reduce

native approach fromEntries

lodash mapValues

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):