Benchmark: Lodash#uniq + Native v2

HTML Preparation code:

<script src='https://cdn.jsdelivr.net/npm/lodash@4.17.10/lodash.min.js'></script>

Script Preparation code:

var list = new Array(10000)
for (let i = 0; i < list.length; ++i) {
    list[i] = { v: Math.ceil(100 * Math.random()) }
}

function getValue(v) {
  return v.v
}

function comparator(v1, v2) {
  return v1.v === v2.v
}

function deduplicateWithSet(l) {
    const s = new Set()
    return l.filter(v => {
      	const vv = getValue(v)
        if (s.has(vv)) return false
        s.add(vv)
        return true
    })
}

function deduplicateWithIncludes(l) {
    const s = []
    return l.filter(v => {
       	const vv = getValue(v)
        if (s.includes(vv)) return false
        s.push(vv)
        return true
    })
}

Tests:

_.uniqWith
return _.uniqWith(list, comparator);
Using set
return deduplicateWithSet(list)
Using Array#includes
return deduplicateWithIncludes(list)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
_.uniqWith
Using set
Using Array#includes

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/111.0.0.0 Safari/537.36

Browser/OS: Chrome 111 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
_.uniqWith	869.1 Ops/sec
Using set	711.1 Ops/sec
Using Array#includes	631.9 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's break down what is being tested on the provided JSON benchmark.

**Benchmark Definition**

The script preparation code generates an array of 10,000 objects with a random `v` property. The script defines three functions:

1. `getValue(v)`: returns the value of the `v` property.
2. `comparator(v1, v2)`: compares two values using the `===` operator.
3. Two functions that attempt to deduplicate the array:
	* `deduplicateWithSet(l)`: uses a Set to keep track of unique values.
	* `deduplicateWithIncludes(l)`: uses Array#includes to check for duplicate values.

**Options Compared**

The benchmark compares three approaches:

1. **_.uniqWith(list, comparator)**: uses Lodash's `uniqWith` function, which takes a comparator function as an argument. This approach is likely to be the most efficient, as it leverages optimized C code under the hood.
2. **Using set**: uses a Set to keep track of unique values. This approach has a time complexity of O(n), making it potentially slower than Lodash's implementation.
3. **Using Array#includes**: uses Array#includes to check for duplicate values. This approach also has a time complexity of O(n), but may be slower due to the overhead of calling `includes` on each iteration.

**Pros and Cons**

* **_.uniqWith(list, comparator)**:
	+ Pros: likely to be the most efficient, leverages optimized C code.
	+ Cons: depends on Lodash's implementation, may not provide a clear understanding of the underlying algorithm.
* **Using set**:
	+ Pros: simple and easy to understand, can be faster than Array#includes if implemented correctly.
	+ Cons: time complexity is O(n), may be slower for large datasets.
* **Using Array#includes**:
	+ Pros: easy to implement and understand, can provide a good benchmarking baseline.
	+ Cons: time complexity is O(n), may be slower due to the overhead of calling `includes` on each iteration.

**Library and Its Purpose**

The benchmark uses Lodash, a popular JavaScript utility library. The `uniqWith` function takes a comparator function as an argument, allowing users to customize the comparison logic.

**Special JS Features or Syntax**

There are no special JS features or syntax mentioned in the benchmark definition. However, it's worth noting that the use of Set and Array#includes demonstrates good understanding of JavaScript's built-in data structures and algorithms.

**Other Alternatives**

If you're interested in exploring alternative approaches, here are some suggestions:

* Use a different comparison function instead of `===`.
* Implement your own deduplication algorithm using bitwise operations or bit manipulation.
* Compare the performance of using a Trie data structure for deduplication.
* Experiment with different caching strategies to improve performance.

Related benchmarks:

Lodash#uniq + Native v2 (version: 0)

Comparing performance of: _.uniqWith vs Using set vs Using Array#includes

Created: one year ago by: Guest

Jump to the latest result

_.uniqWith

Using set

Using Array#includes

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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