Benchmark: Lodash.js - MeasureThat.net

HTML Preparation code:

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

Script Preparation code:

var obj = {
    x: 1,
    y: 2,
    arr: new Array(200).fill({
        x: 'q',
        f: 'asdfasdfafdsfasddddddddddddddddddddd',
      d: 'asdfasdf'
    }),
}

Tests:

Lodash.js clone deep
_.cloneDeep(obj);
qwer
return 1;

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Lodash.js clone deep
qwer

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 2 years ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/103.0.0.0 Safari/537.36

Browser/OS: Chrome 103 on Windows

View result in a separate tab

Test name	Executions per second
Lodash.js clone deep	55292.3 Ops/sec
qwer	1185387392.0 Ops/sec

Autogenerated LLM Summary (model llama3.2:3b, generated 6 months ago):

LLMs can make mistakes. Check important info.

I'd be happy to help you understand the JavaScript microbenchmark on MeasureThat.net.

**What is tested?**

The provided JSON represents two benchmark test cases:

1. Lodash.js clone deep: This test case measures the performance of the `_.cloneDeep()` function from the Lodash library, which clones an object deeply.
2. Simple return statement: This test case measures the execution time of a simple JavaScript function that returns 1.

**Options compared**

In both test cases, the options being compared are:

* **Browser**: The browser used to run the benchmark (in this case, Chrome 103 on Windows).
* **DevicePlatform**: The device platform used to run the benchmark (in this case, Desktop).

The options not mentioned in the JSON are likely controlled by the MeasureThat.net framework and include factors such as:

* CPU architecture (x86, ARM, etc.)
* CPU frequency
* Number of cores
* Memory allocation

**Pros and Cons**

Here's a brief analysis of each option:

1. **Browser**: Using a specific browser can introduce variability in the benchmark results, as different browsers may optimize or interpret the code differently.
	* Pros: Allows for direct comparison between browsers with similar specifications.
	* Cons: May not accurately represent real-world scenarios where users may use different browsers.
2. **DevicePlatform**: Focusing on device platforms helps to isolate hardware-related variations in performance.
	* Pros: Helps to identify performance differences between devices or platforms.
	* Cons: May not account for differences in software or system configuration.

**Library (Lodash.js)**

The `_.cloneDeep()` function is part of the Lodash library, a popular JavaScript utility library that provides a set of functional programming helpers. The purpose of this library is to provide a convenient way to perform common tasks, such as array and object manipulation, without requiring manual implementation.

**Special JS feature or syntax**

There are no special JavaScript features or syntax mentioned in this benchmark. It appears to be a straightforward test case for the `_.cloneDeep()` function and a simple return statement.

**Alternatives**

Other alternatives for creating microbenchmarks include:

1. **Benchmark.js**: A popular JavaScript benchmarking library that provides a simple way to create benchmarks.
2. **BenchMarkDaly**: A Python-based benchmarking framework for JavaScript that allows for more complex benchmarking scenarios.
3. **Google Benchmark**: A C++-based benchmarking library that can be used to create microbenchmarks for JavaScript.

These alternatives offer different features and flexibility, but MeasureThat.net provides a simple and straightforward way to create benchmarks without requiring extensive setup or configuration.

Related benchmarks:

Lodash.js (version: 0)

Comparing performance of: Lodash.js clone deep vs qwer

Created: 2 years ago by: Registered User

Jump to the latest result

Lodash.js clone deep

qwer

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 6 months ago):