Benchmark: Spread Operator vs. Lodash for POJO

HTML Preparation code:

<script src='https://cdnjs.cloudflare.com/ajax/libs/lodash.js/4.17.5/lodash.min.js'></script>

Tests:

Spread Operator
const obj = {test: {test: {test: {test: {test: 'hi'}}}}}; const test = {...obj};
Lodash Clone
const obj = {test: {test: {test: {test: {test: 'hi'}}}}}; const test = _.clone(obj);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Spread Operator
Lodash Clone

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 JavaScript microbenchmarks and explore what's being tested in this specific benchmark.

**Benchmark Definition**
The benchmark is defined by a JSON object that provides metadata about the test, including:

* `Name`: The name of the benchmark, which is "Spread Operator vs. Lodash for POJO".
* `Description`: An empty string, indicating that there is no description provided.
* `Script Preparation Code`: An empty string, meaning that no special script preparation code is required.
* `Html Preparation Code`: A link to a CDN-hosted copy of the Lodash library (version 4.17.5) in JavaScript format.

**Individual Test Cases**
The benchmark consists of two test cases:

1. **Spread Operator**: The first test case uses the spread operator (`...`) to create a shallow copy of an object.
	* `Benchmark Definition`: A JSON string that defines the test case, using an object with nested properties to demonstrate how the spread operator works.
2. **Lodash Clone**: The second test case uses Lodash's `clone()` function to create a deep copy of an object.

**Comparison**
The benchmark compares the performance of two approaches:

* Spread Operator: A simple and concise way to create a shallow copy of an object using the spread operator.
* Lodash Clone: A more robust approach that uses a dedicated library to create a deep copy of an object, which may be more suitable for complex objects with many nested properties.

**Pros and Cons**

* **Spread Operator**: Pros:
	+ Fast and lightweight
	+ Easy to understand and use
	+ Works well for simple objects
Cons:
	+ May not work correctly for objects with many nested properties
	+ Can lead to shallow copies if not used carefully
* Lodash Clone: Pros:
	+ More robust and reliable for creating deep copies
	+ Handles complex objects with ease
Cons:
	+ Larger binary size (due to the library)
	+ May have slower execution times due to the overhead of the library

**Library: Lodash**
Lodash is a popular JavaScript utility library that provides a wide range of functions for tasks such as array manipulation, object creation, and more. In this benchmark, Lodash's `clone()` function is used to create a deep copy of an object.

**Special JS Features or Syntax**
Neither test case uses any special JavaScript features or syntax beyond the standard language. However, it's worth noting that the spread operator was introduced in ECMAScript 2018 (ES2018) and provides a concise way to create copies of objects.

**Other Alternatives**
If you're interested in exploring alternative approaches for creating copies of objects in JavaScript, here are a few options:

* `JSON.parse(JSON.stringify(obj))`: This method uses JSON serialization to create a copy of an object.
* `Object.assign()`: This method can be used to create a shallow copy of an object by spreading its properties onto a new object.
* `Array.prototype.slice()` or `Array.prototype.copyWithin()`: These methods can be used to create copies of arrays, but may not work directly for objects.

Keep in mind that each approach has its own trade-offs and limitations. The choice of method depends on the specific use case and performance requirements.

Related benchmarks:

Spread Operator vs. Lodash for POJO (version: 0)

Comparing performance of: Spread Operator vs Lodash Clone

Created: 4 years ago by: Guest

Jump to the latest result

Spread Operator

Lodash Clone

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):