Benchmark: lodash xor perf v2

Script Preparation code:

function dec2hex (dec) {
  return dec < 10
    ? '0' + String(dec)
    : dec.toString(16)
}

function generateId (len) {
  var arr = new Uint8Array((len || 40) / 2)
  window.crypto.getRandomValues(arr)
  return Array.from(arr, dec2hex).join('')
}

var a = []
var b = []
for( let i = 0; i < 1000; i++)
{
  a[i] = generateId(16)
  b[i] = generateId(16)
}

Tests:

lodash xor
const res = _.xor(a, b).length === 0;
with Sets
var as = new Set(a) var bs = new Set(b) const res = a .filter((row_id) => bs.has(row_id)) .concat(b.filter((row_id) => !as.has(row_id))).length === 0;
isEqual + sort
const res = _.isEqual(_.sortBy(a), _.sortBy(b));
diff x 2
const res = _.difference(a, b).length === 0 && _.difference(b, a).length === 0;

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
lodash xor
with Sets
isEqual + sort
diff x 2

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 dive into the world of JavaScript microbenchmarks.

**Benchmark Definition**

The provided JSON defines a benchmark for testing the performance of different approaches to solving the _.xor(a, b) function from the Lodash library. The benchmark consists of four individual test cases:

1. **_.xor(a, b)**: This test case uses the original _.xor function from Lodash.
2. **With Sets**: This test case uses a manual implementation with Set data structures to calculate the difference between two arrays.
3. **IsEqual + sort**: This test case compares the results of sorting both input arrays and then checking if their intersection is empty using the _.isEqual function from Lodash.
4. **Diff x 2**: This test case calculates the difference between two arrays twice, which can be optimized.

**Options Compared**

The benchmark compares four different approaches to solving the _.xor(a, b) problem:

1. **_.xor(a, b)**: Uses the original implementation from Lodash.
2. **With Sets**: Manually implements a solution using Set data structures.
3. **IsEqual + sort**: Uses the _.isEqual function from Lodash and sorts both input arrays before comparing them.
4. **Diff x 2**: Calculates the difference between two arrays twice.

**Pros and Cons of Each Approach**

Here's a brief analysis of each approach:

1. **_.xor(a, b)**:
	* Pros: Optimized for performance, uses Lodash's internal optimizations.
	* Cons: May not be suitable for all use cases or languages that don't support the _.xor function.
2. **With Sets**:
	* Pros: Simple to understand and implement, works well with Set data structures.
	* Cons: May not be as efficient as other approaches, especially for large inputs.
3. **IsEqual + sort**:
	* Pros: Easy to understand, uses a simple sorting algorithm.
	* Cons: Sorts the input arrays, which can lead to increased memory usage and slower performance for large inputs.
4. **Diff x 2**:
	* Pros: Optimized for calculating differences between two arrays twice.
	* Cons: May not be suitable for other use cases where a single difference calculation is sufficient.

**Other Considerations**

When evaluating the benchmark results, consider the following factors:

1. **Memory usage**: The amount of memory allocated by each approach can impact performance and scalability.
2. **Cache locality**: The order in which elements are accessed within an array can affect cache locality, leading to different performance outcomes.
3. **Language-specific optimizations**: Some languages or libraries may optimize for specific use cases or data structures, affecting the performance of each approach.

**Lodash Library**

The _.xor function is part of the Lodash library, a popular JavaScript utility library that provides a wide range of functions for common programming tasks. In this benchmark, the _.xor function serves as a reference implementation to compare against other approaches.

**Special JS Features or Syntax**

This benchmark doesn't explicitly use any special JavaScript features or syntax beyond what's required by the individual test cases. However, it does rely on modern JavaScript features like Set data structures and async/await (implied through the `ExecutionsPerSecond` metric).

**Alternatives**

If you're interested in exploring alternative approaches to solving the _.xor(a, b) problem, consider the following:

1. **Manual implementation**: Implement a custom solution using arrays or other data structures.
2. **Other libraries or frameworks**: Explore alternative libraries or frameworks that provide optimized _.xor implementations, such as Underscore.js or FastJS.
3. **Custom caching mechanisms**: Implement custom caching mechanisms to optimize performance for specific use cases.

Keep in mind that the best approach will depend on your specific requirements and constraints.

Latest run results:

Run details: (Test run date: 10 months ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36

Browser/OS: Chrome 120 on Windows

View result in a separate tab

Test name	Executions per second
lodash xor	4547.0 Ops/sec
with Sets	8690.5 Ops/sec
isEqual + sort	1407.9 Ops/sec
diff x 2	15437.4 Ops/sec

Related benchmarks:

lodash xor perf v2 (version: 0)

Searching for faster solution for _.xor(a, b)

Comparing performance of: lodash xor vs with Sets vs isEqual + sort vs diff x 2

Created: 2 years ago by: Guest

Jump to the latest result

lodash xor

with Sets

isEqual + sort

diff x 2

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):