Benchmark: Differrence vs some + includes

HTML Preparation code:

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

Script Preparation code:

var arr1 = ['past','present','future']
var arr2 = ['past', 'future']

Tests:

Difference
_.difference(arr1, arr2).length > 0
Some + includes
arr2.some(item => arr1.includes(item))

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Difference
Some + includes

Fastest: N/A

Slowest: N/A

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

LLMs can make mistakes. Check important info.

I'll break down the provided benchmark and explain what's being tested, compared, and considered.

**Benchmark Overview**

The benchmark measures the performance difference between two approaches:

1. `arr2.some(item => arr1.includes(item))`: This approach uses the `some()` method with a callback function that checks if each element in `arr2` is included in `arr1`.
2. `_.difference(arr1, arr2).length > 0`: This approach uses the Lodash library to compute the difference between two arrays and then checks if the resulting length is greater than 0.

**Options Compared**

The benchmark compares the performance of these two approaches:

Pros of `some()` method:

* Easy to read and write
* Efficient for small arrays
* Does not require array iteration

Cons of `some()` method:

* May be slower for large arrays due to function call overhead
* May incur additional memory allocations

Pros of Lodash's `_difference` method:

* Optimized for performance on large arrays
* Uses a more efficient algorithm than manual iteration
* Reduces boilerplate code and improves readability

Cons of Lodash's `_difference` method:

* Requires external library inclusion (Lodash)
* May have additional dependencies

**Other Considerations**

* **Array Iteration**: Both approaches involve iterating over the arrays. However, the `some()` method may be slower due to function call overhead, while Lodash's `_difference` method uses a more efficient algorithm.
* **Memory Usage**: The `some()` method may incur additional memory allocations for each element in the array, whereas Lodash's `_difference` method avoids this overhead.

**Library (Lodash)**

Lodash is a popular JavaScript library that provides various utility functions, including array manipulation methods like `difference()`. It's designed to be efficient and performant, making it suitable for large-scale applications.

**Special JS Feature/Syntax**

There are no special JavaScript features or syntax mentioned in the benchmark. The code uses standard JavaScript syntax and doesn't rely on any experimental features or proprietary APIs.

**Alternatives**

If you need to implement an array difference calculation without using Lodash, you can use a simple iterative approach:

```javascript
function diff(arr1, arr2) {
  const result = [];
  for (const item of arr1) {
    if (!arr2.includes(item)) {
      result.push(item);
    }
  }
  return result;
}
```

Alternatively, you can use the `Set` data structure to improve performance:

```javascript
function diff(arr1, arr2) {
  const set1 = new Set(arr1);
  const set2 = new Set(arr2);
  for (const item of set1) {
    if (!set2.has(item)) {
      result.push(item);
    }
  }
  return result;
}
```

These implementations have different performance characteristics and trade-offs compared to using Lodash's `_difference` method.

Latest run results:

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

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36

Browser/OS: Chrome 117 on Windows

View result in a separate tab

Test name	Executions per second
Difference	1745904.8 Ops/sec
Some + includes	6088118.5 Ops/sec

Related benchmarks:

Differrence vs some + includes (version: 0)

Comparing performance of: Difference vs Some + includes

Created: one year ago by: Guest

Jump to the latest result

Difference

Some + includes

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):