Benchmark: lodash filter vs splice

HTML Preparation code:

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

Script Preparation code:

window.test = [
  { id: 1, source: true },
  { id: 2, source: true },
  { id: 3, source: false }
]

Tests:

lodash filter
_.filter(window.test, function (item) { return item.source === false })
array splice
window.test.splice(2, 1)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
lodash filter
array splice

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 on MeasureThat.net.

**Benchmark Definition JSON**

The provided JSON represents a benchmark with two test cases: `lodash filter` and `array splice`. The benchmark is designed to compare the performance of using the `_.filter()` method from the Lodash library versus directly modifying an array using the `splice()` method.

**Options Compared**

In this benchmark, we have two options:

1. **Lodash _.filter()**: This method applies a callback function to each element in the array and returns a new array with only the elements for which the callback returned `true`.
2. **Array Splice**: This method modifies the original array by removing or adding elements at a specified position.

**Pros and Cons**

Here are some pros and cons of each approach:

* **Lodash _.filter()**:
	+ Pros: More efficient memory usage, as it returns a new array without modifying the original one.
	+ Cons: May have overhead due to the creation of a new array, especially for large datasets. Additionally, it requires the Lodash library to be included and initialized.
* **Array Splice**:
	+ Pros: No additional memory allocation or object creation is required, as it modifies the original array in-place. However, this approach can lead to performance issues if the array is modified repeatedly, as it invalidates the cache of the JavaScript engine.
	+ Cons: Can be slower due to the overhead of modifying the array and invalidating the cache.

**Library: Lodash**

Lodash is a popular JavaScript utility library that provides a set of high-quality, predictable functions for working with arrays, objects, and function arguments. The `_.filter()` method is one of its most useful features, allowing you to easily apply a callback function to each element in an array. In this benchmark, the use of Lodash _.filter() is used to compare its performance with the built-in Array Splice method.

**Special JS Feature or Syntax**

None mentioned in this specific benchmark. However, if you're interested in exploring other JavaScript features or syntax, some notable ones include:

* **Arrow Functions**: Introduced in ES6, arrow functions provide a concise way to define small, single-expression functions.
* **Promises**: Used for asynchronous programming, promises allow you to write code that's easier to read and maintain, especially when dealing with multiple asynchronous operations.

**Other Alternatives**

If you're interested in exploring alternative approaches or libraries, here are some options:

1. **Native Array Methods**: Some browsers provide native methods like `filter()` and `splice()` in their array implementations. You can use these methods as alternatives to the Lodash library.
2. **ES6 Array Methods**: ES6 introduced a set of new array methods, including `filter()`, `map()`, and `reduce()`. These methods are more concise and expressive than traditional array manipulation techniques.

When writing benchmarks or comparing performance, it's essential to consider the trade-offs between different approaches and libraries. By understanding the pros and cons of each method, you can make informed decisions about which approach best suits your needs.

Related benchmarks:

lodash filter vs splice (version: 0)

Comparing performance of: lodash filter vs array splice

Created: 8 years ago by: Guest

Jump to the latest result

lodash filter

array splice

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):