Benchmark: lodash partition vs remove 3

HTML Preparation code:

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

Script Preparation code:

function randomIntFromInterval(min, max) { // min and max included 
  return Math.floor(Math.random() * (max - min + 1) + min)
}

var tabl = Array.from({ length: 10000 }).map((value, i) => ({ id: i, value: randomIntFromInterval(0, i) }))

Tests:

lodash partition
const [a, b] = _.partition(tabl, e => e.value % 2)
lodash remove
const a = _.remove(tabl, e => e.value % 2) tabl = [...tabl, ...a]

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
lodash partition
lodash remove

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 break down the provided benchmark and explain what's being tested, compared, and their pros and cons.

**Benchmark Overview**

The benchmark compares two approaches: `lodash partition` and `lodash remove`. The test case uses an array of 10,000 objects with random values. The objective is to determine which approach is faster for these specific operations.

**Test Case 1: Lodash Partition**

```javascript
const [a, b] = _.partition(tabl, e => e.value % 2)
```

Lodash `partition` returns two arrays: one containing elements that pass the test (in this case, values with even numbers) and another containing elements that fail the test. The resulting arrays are assigned to `a` and `b`.

**Test Case 2: Lodash Remove**

```javascript
const a = _.remove(tabl, e => e.value % 2)
tabl = [...tabl, ...a]
```

Lodash `remove` returns an array of elements that fail the test (values with odd numbers). The resulting array is assigned to `a`, and then the original array is merged with the removed elements using the spread operator (`...`) to form a new array.

**Library: Lodash**

Lodash is a popular JavaScript utility library that provides a collection of helper functions for common tasks, such as:

* String manipulation
* Array manipulation
* Object manipulation
* Miscellaneous utilities

In this benchmark, Lodash is used to implement the `partition` and `remove` functions. The library's purpose is to provide a standardized way to perform these operations, making it easier to write efficient and consistent code.

**Pros and Cons**

Both approaches have their advantages and disadvantages:

**Lodash Partition:**

Pros:

* Easier to read and understand
* Less memory allocation overhead

Cons:

* Creates two separate arrays
* May be slower for large datasets due to array creation

**Lodash Remove:**

Pros:

* More concise and expressive
* No array creation overhead

Cons:

* Can be less intuitive for some developers
* May require more memory due to the `...` spread operator

**Other Considerations**

When choosing between these approaches, consider the following factors:

* Code readability and maintainability
* Performance characteristics (e.g., cache friendliness)
* Memory constraints
* Desired output format (two separate arrays or a single merged array)

**Alternatives**

If you don't want to use Lodash, you can implement the `partition` and `remove` functions using standard JavaScript techniques. Here's an example of how you might implement `lodash remove` without the library:
```javascript
function remove(arr, predicate) {
  return arr.filter(predicate).map((item) => !predicate(item));
}
```
Keep in mind that this implementation is less efficient than Lodash's optimized version.

In summary, the benchmark compares two approaches using Lodash: `partition` and `remove`. While both have pros and cons, Lodash provides a standardized way to perform these operations, making it easier to write efficient code.

Related benchmarks:

lodash partition vs remove 3 (version: 0)

Comparing performance of: lodash partition vs lodash remove

Created: 2 years ago by: Guest

Jump to the latest result

lodash partition

lodash remove

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):