Benchmark: lodash.each vs Object.forEach

HTML Preparation code:

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

Script Preparation code:

var value = [];
for(var i = 0; i < 1000; i++){
  value[i] = i;
}

Tests:

lodash.map
_.map(value, (x) => x*x);
native
value.map((x) => x*x);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
lodash.map
native

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: one year ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/127.0.0.0 Safari/537.36

Browser/OS: Chrome 127 on Windows

View result in a separate tab

Test name	Executions per second
lodash.map	486161.0 Ops/sec
native	336993.3 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's dive into the explanation of the provided benchmark.

**Benchmark Overview**

The benchmark compares two approaches to perform an iteration over an array in JavaScript: using Lodash (a popular utility library) and the native `forEach` method.

**Options Compared**

1. **Lodash `_.map()`**: This function creates a new array with the results of applying a provided function to each element in the original array.
2. **Native `value.map((x) => x*x)`**: This is a simple example of using the `map` method on an array, where the callback function multiplies each element by itself.

**Pros and Cons**

* **Lodash `_.map()`**:
	+ Pros: Lodash provides a well-documented and widely-used library for common tasks, making it easier to find help and resources. The `.map()` function is also generally fast and efficient.
	+ Cons: Adding another dependency (Lodash) increases the overhead of the benchmark, as the test needs to load the library before running the benchmark. This might not be a concern in production environments where Lodash is already included.
* **Native `forEach`**:
	+ Pros: Using native methods reduces the overhead of loading additional libraries, making the benchmark faster and more representative of real-world scenarios.
	+ Cons: The native `forEach` method can lead to slower performance due to the overhead of the browser's event loop and the need to iterate over the array.

**Library Used**

In this benchmark, Lodash is used for its `.map()` function. Lodash is a popular utility library that provides a wide range of functions for common tasks, such as string manipulation, array operations, and more. The `_.map()` function is specifically designed for mapping arrays to new values.

**Special JavaScript Features**

There are no special JavaScript features or syntax used in this benchmark beyond the standard language features provided by ECMAScript.

**Other Considerations**

* **Testing Environment**: The benchmark is run on a desktop browser (Chrome 127) with a Windows operating system.
* **Execution Frequency**: Each test case executes approximately every second, resulting in a high execution frequency for both benchmarks.

**Alternatives**

If you're interested in exploring alternative approaches to iterating over arrays in JavaScript, here are some options:

1. **For...of loop**: This is another native method for iterating over arrays.
2. **Array.prototype.forEach()**: Similar to the native `forEach` method but with a slightly different syntax.
3. **Other libraries or frameworks**: Depending on your specific requirements, you might consider using other libraries or frameworks that provide optimized array iteration methods, such as React's `useEffect` hook.

Keep in mind that these alternatives might introduce additional dependencies or overhead, so be sure to consider the trade-offs when choosing an approach for your specific use case.

Related benchmarks:

lodash.each vs Object.forEach (version: 0)

Comparing performance of: lodash.map vs native

Created: 8 years ago by: Guest

Jump to the latest result

lodash.map

native

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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