Benchmark: Test Lodash Filter vs Remove

HTML Preparation code:

<script src="https://cdn.jsdelivr.net/npm/lodash@4.17.21/lodash.min.js"></script>

Script Preparation code:

const limit = 10000000;
var array = [];

for (let i = 0; i < limit; i++) {
  array.push(i);
}

function getRandomIndex() {
  return Math.floor(Math.random() * array.length)
}

var element = array[getRandomIndex()];

Tests:

LoDash Remove
_.remove(array, x => x === element);
Lodash filter
_.filter(array, x=> x !== element);

Historical Performance Data aggregated from 10 community runs

Desktop Results:

Chrome 10 runs

LoDash Remove

3/s

Lodash filter

2/s

Fastest: LoDash Remove (1.4x faster)

View exact numbers

✓ LoDash Remove: 3 ops/sec
• Lodash filter: 2 ops/sec

Results show median performance across all community submissions. Run this benchmark yourself to verify on your device.

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
LoDash Remove
Lodash filter

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: one year ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/138.0.0.0 Safari/537.36

Browser/OS: Chrome 138 on Mac OS X 10.15.7

View result in a separate tab

LoDash Remove

109/s

Lodash filter

10/s

View exact numbers

Test name	Executions per second
✓ LoDash Remove	109 Ops/sec
Lodash filter	10 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's break down the provided JSON for the JavaScript microbenchmark Test Lodash Filter vs Remove.

**Benchmark Definition**

The benchmark tests two different approaches using the popular library Lodash:

1. `_.remove(array, x => x === element);` - This test removes elements from an array that match a specific condition (in this case, `element` is a random index generated in the script preparation code).
2. `_.filter(array, x=> x !== element);` - This test filters out elements from an array that do not match a specific condition (in this case, `x !== element`).

**Options Compared**

The two tests compare the performance of Lodash's `remove` and `filter` functions when applied to a large array (`limit = 10000000`). The differences in execution times will give insight into which approach is more efficient.

**Pros and Cons of Each Approach**

1. `_.remove(array, x => x === element);`
	* Pros: This approach has a lower overhead since it only needs to iterate through the array once to find the elements to remove.
	* Cons: If the `element` index is not found in the array, `remove` will return an empty array. This could lead to unnecessary iterations if the condition is often false.
2. `_.filter(array, x=> x !== element);`
	* Pros: This approach has a simpler implementation and might be more readable for those familiar with Lodash's API.
	* Cons: This approach requires two iterations through the array (one to filter out elements and another to remove them), which could lead to higher overhead.

**Lodash Library**

In this benchmark, Lodash is used as a utility library to provide functional programming helpers like `filter` and `remove`. The `lodash.min.js` file is included in the HTML preparation code, making it easily accessible during benchmarking.

**Other Considerations**

When writing such benchmarks, consider the following:

* **Randomization**: Using random values (like indices) helps ensure that the results are representative of typical use cases.
* **Array size**: Chosen a suitable array size (`limit = 10000000`) to capture performance differences in large datasets.
* **Benchmarking strategy**: Test both approaches with and without any additional optimizations or caching mechanisms to isolate pure algorithmic performance.

**Alternative Approaches**

For comparison, you could consider testing alternative implementations of `remove` and `filter`, such as:

1. Vanilla JavaScript array methods (e.g., `splice`, `forEach`)
2. Other library functions (e.g., from Moment.js for date filtering)
3. Caching or memoization techniques to optimize performance

Keep in mind that these alternatives might not be suitable for all use cases and may add complexity to the benchmarking process.

Overall, this benchmark provides a great example of how different approaches can have varying performance characteristics, especially when dealing with large datasets.

Test Lodash Filter vs Remove (version: 0)

Filtering a large array using lodash filter and remove

Comparing performance of: `LoDash Remove vs Lodash filter`

Created: 3 years ago by: Guest

Go to the latest result

LoDash Remove

Lodash filter

Historical Performance Data aggregated from 10 community runs

Desktop Results:

Chrome 10 runs

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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

Test Lodash Filter vs Remove (version: 0)

Filtering a large array using lodash filter and remove

Comparing performance of: LoDash Remove vs Lodash filter

Created: 3 years ago by: Guest

Go to the latest result

LoDash Remove

Lodash filter

Historical Performance Data aggregated from 10 community runs

Desktop Results:

Chrome 10 runs

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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

Related benchmarks

Comparing performance of: `LoDash Remove vs Lodash filter`