Benchmark: double destructuring vs lodash get

HTML Preparation code:

<script src="https://cdn.jsdelivr.net/lodash/4.16.0/lodash.min.js"></script>

Script Preparation code:

var dataObject = {foo: 'Frederick', bar: 'Corcino Alejo'};

Tests:

destructure
const {foo,bar} = dataObject
lodash
const dataObject = { foo: '1', bar: '2' } const foo = _.get(dataObject,'foo') const bar = _.get(dataObject,'bar')

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
destructure
lodash

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

This benchmark does not have any results yet. Be the first one to run it!

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

LLMs can make mistakes. Check important info.

Let's break down the provided benchmarking test cases and explain what is being tested, compared, and the pros/cons of each approach.

**Benchmark Definition JSON**

The benchmark definition defines two test cases:

1. "destructure"
2. "lodash"

The script preparation code for both test cases is the same:
```javascript
var dataObject = {foo: 'Frederick', bar: 'Corcino Alejo'};
```
This creates a JavaScript object `dataObject` with two properties: `foo` and `bar`.

**Test Cases**

#### 1. "destructure"

The test case uses the destructuring syntax to extract values from the `dataObject`:
```javascript
const {foo, bar} = dataObject;
```
This is a built-in JavaScript feature that allows you to create multiple variables from an object.

Pros:

* Simple and concise syntax.
* Fast execution, as it only requires a single assignment operation.

Cons:

* Only works with objects that have the same number of properties as the destructured variables.
* Can lead to errors if the object is missing or has unexpected properties.

#### 2. "lodash"

The test case uses the Lodash library's `_.get()` function to extract values from the `dataObject`:
```javascript
const dataObject = { foo: '1', bar: '2' }
const foo = _.get(dataObject, 'foo')
const bar = _.get(dataObject, 'bar')
```
The `_` symbol refers to the Lodash library, which is loaded via a CDN link in the HTML preparation code.

Pros:

* Works with objects that have nested properties.
* Reduces errors due to object property name ambiguity.

Cons:

* Requires loading an external library (Lodash), which may incur additional overhead.
* May be slower than native destructuring syntax due to function call overhead.

**Library: Lodash**

Lodash is a popular JavaScript utility library that provides various functions for data manipulation, iteration, and string manipulation. In this test case, the `_` symbol refers to the `lodash.get()` function, which is used to safely navigate object property paths.

**Special JS Feature/Syntax**

There are no special JavaScript features or syntax mentioned in the benchmarking test cases.

**Other Alternatives**

If you prefer not to use Lodash or built-in destructuring syntax, other alternatives for extracting values from an object include:

1. Using `Object.assign()` and a temporary variable: `const foo = Object.assign({}, dataObject)[foo]`.
2. Using a library like underscore.js (alternative to Lodash) or moment.js.
3. Implementing a recursive function to traverse the object property paths.

However, these alternatives may introduce additional overhead, complexity, or errors compared to using native destructuring syntax or Lodash's `_.get()` function.

In summary, the benchmarking test cases compare two approaches for extracting values from an object:

* Native destructuring syntax (built-in JavaScript feature)
* Lodash's `_.get()` function (external library)

The pros and cons of each approach are discussed above. The choice between these alternatives depends on your specific use case, performance requirements, and personal preference.

Related benchmarks:

double destructuring vs lodash get (version: 0)

Comparing performance of: destructure vs lodash

Created: 3 years ago by: Guest

Jump to the latest result

destructure

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):