Benchmark: _.pickBy vs filter on array

HTML Preparation code:

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

Script Preparation code:

var obj = Array(1000)
  .fill()
  .reduce((acc, curr, i) => {
    acc[i] = i;
    return acc;
  }, []);
var predicate = v => v % 2 === 0;

Tests:

_.pickBy
_.pickBy(obj, predicate);
filter
obj.filter(e => predicate(e))

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
_.pickBy
filter

Fastest: N/A

Slowest: N/A

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

LLMs can make mistakes. Check important info.

Let's break down the provided JSON data and explain what's being tested.

**Benchmark Definition**

The benchmark measures the performance difference between two JavaScript functions: `_.pickBy` from Lodash and `filter` (built-in array method).

**Script Preparation Code**

The script creates an array of 1000 elements, where each element is assigned a unique index using the `reduce` method. This array will be used as the input data for both benchmark tests.

```javascript
var obj = Array(1000)
  .fill()
  .reduce((acc, curr, i) => {
    acc[i] = i;
    return acc;
  }, []);
```

**Html Preparation Code**

The HTML code includes a script tag that loads the Lodash library version 4.17.5.

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

**Benchmark Test Cases**

There are two test cases:

1. `_.pickBy(obj, predicate)`
2. `obj.filter(e => predicate(e))`

These test cases compare the performance of the `_.pickBy` function from Lodash and the built-in `filter` method.

**Options Compared**

The benchmark tests the following options:

* `_._pickBy`: A function from the Lodash library that creates a new array with only the elements for which the predicate returns true.
* `filter`: A built-in array method that creates a new array with only the elements for which the callback function returns true.

**Pros and Cons of Each Approach**

1. **_.pickBy` (Lodash)**:
	* Pros: Often more concise and readable, especially for complex predicates or when working with large datasets.
	* Cons: Adds external dependency on Lodash, may have slower startup times due to lazy loading.
2. **filter** (built-in):
	* Pros: Fastest option, as it's a built-in method that doesn't require any additional libraries.
	* Cons: May be less readable or more verbose for complex predicates.

**Library: Lodash**

Lodash is a popular JavaScript utility library that provides various functions to simplify common programming tasks. In this case, the `_pickBy` function creates a new array with filtered elements, making it an efficient and concise solution for filtering arrays.

**Special JS Feature/Syntax**

There are no special features or syntaxes used in these benchmark tests. They rely on standard JavaScript syntax and built-in methods.

**Alternatives**

Other alternatives to `_.pickBy` include:

* Using a custom filter function with the `Array.prototype.filter()` method.
* Using a library like Ramda, which provides a similar `_pickBy` function with various options for filtering arrays.

In summary, the benchmark measures the performance difference between the Lodash `_pickBy` function and the built-in `filter` method in JavaScript. It tests these two approaches on an array of 1000 elements to determine which one is faster and more efficient.

Latest run results:

Run details: (Test run date: 8 days ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:132.0) Gecko/20100101 Firefox/132.0

Browser/OS: Firefox 132 on Mac OS X 10.15

View result in a separate tab

Test name	Executions per second
_.pickBy	9302.1 Ops/sec
filter	226899.6 Ops/sec

Related benchmarks:

_.pickBy vs filter on array (version: 0)

Comparing performance of: _.pickBy vs filter

Created: 3 years ago by: Guest

Jump to the latest result

_.pickBy

filter

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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