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HTML Preparation code:

<script src="https://cdn.jsdelivr.net/npm/lodash@4.17.4/lodash.min.js"></script>

Script Preparation code:

var arrLength = 100;
var arr = new Array(arrLength).fill().map((_, idx) => ({ id: idx }));
var foo = Math.floor(Math.random() * arrLength);

function _findUser(id) {
  const userIdx = _(arr).findIndex({ id });
  return arr[userIdx] || null;
}

var userEnum = {};
function findUser(id) {
  if (Object.keys(userEnum).length === 0) {
  	_(arr).each(user => userEnum[user.id] = user);
  }
  return userEnum[id] || null;
}

Tests:

enum
findUser(foo);
lodash
_findUser(foo);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
enum
lodash

Fastest: N/A

Slowest: N/A

Latest run results:

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Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):

LLMs can make mistakes. Check important info.

**Overview of the Benchmark**

The provided JSON represents a JavaScript microbenchmark that measures the performance of two different approaches to find an element in an array: one using the `_findUser` function and another using the `findUser` function with the Lodash library.

**Script Preparation Code**

The script preparation code generates a large array of objects, each with an `id` property. The `foo` variable is assigned a random value between 0 and the length of the array. This setup is used to create a realistic test case for finding an element in the array.

**Html Preparation Code**

The HTML preparation code includes a script tag that loads the Lodash library version 4.17.4, which provides the `find` function used in one of the benchmark cases.

**Individual Test Cases**

There are two individual test cases:

1. **"enum"`**: This test case uses the `_findUser` function to find an element in the array by its index. The `_findUser` function is a custom implementation that uses the `findIndex` method of the Array prototype.
2. **"lodash"`**: This test case uses the `findUser` function from the Lodash library to find an element in the array. The `findUser` function is designed to work with objects and returns the first matching element or `null` if no match is found.

**Comparison of Options**

The two approaches have different pros and cons:

* **_findUser` function**: This approach uses a custom implementation that relies on the `findIndex` method of the Array prototype. It has good performance because it avoids creating an object for each element in the array.
	+ Pros: Simple, efficient, and works well for large arrays.
	+ Cons: Not widely supported by browsers, as it depends on the Array prototype's behavior.
* **Lodash `findUser` function**: This approach uses a popular library to find an element in the array. It has good performance because Lodash provides optimized implementations of various functions, including `find`.
	+ Pros: Widely supported by browsers and other environments, as it relies on a well-maintained library.
	+ Cons: Requires loading an external library, which may introduce overhead.

**Other Considerations**

When choosing between these approaches, consider the following factors:

* **Performance**: If you need to perform this operation frequently, choose the approach that provides better performance. The `_findUser` function is likely to be faster for large arrays.
* **Browser support**: If you want your code to work across multiple browsers and environments, choose the Lodash `findUser` function approach.
* **Library dependencies**: If you're concerned about loading additional libraries, consider using the `_findUser` function.

**Special JS Features or Syntax**

There are no special JavaScript features or syntax used in this benchmark. The focus is on comparing two different approaches to find an element in an array.

Related benchmarks:

sdadsadsadsadsadsa (version: 0)

Comparing performance of: enum vs lodash

Created: 4 years ago by: Guest

Jump to the latest result

enum

lodash

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):