Benchmark: Jewels and Stones

Tests:

JS-Set
var numJewelsInStones = function (J, S) { const set = new Set(J.split("")); let count = 0; for (let i = 0; i < S.length; i++) { if (set.has(S[i])) { count++; } } return count; }; numJewelsInStones('aA', 'AaaaAAaaaaAaAAAAaaaaaAabbsdfdffojeworwejoijovweojoweijoewjfwejfowejfowejofjeworjwejtweojweojfweojfwepfpoewjfp')
JS-Array
var numJewelsInStones = function(J, S) { if (!J || !S) return 0; let count = 0; for (const c of S) { if (J.includes(c)) count++; } return count; }; numJewelsInStones('aA', 'AaaaAAaaaaAaAAAAaaaaaAabbsdfdffojeworwejoijovweojoweijoewjfwejfowejfowejofjeworjwejtweojweojfweojfwepfpoewjfp')

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
JS-Set
JS-Array

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 3 years ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:89.0) Gecko/20100101 Firefox/89.0

Browser/OS: Firefox 89 on Mac OS X 10.15

View result in a separate tab

Test name	Executions per second
JS-Set	244998.3 Ops/sec
JS-Array	214450.7 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's break down the provided benchmark and explain what's being tested.

**Benchmark Definition**

The benchmark is defined as a JavaScript function that calculates the number of jewels in a given set of stones. The input parameters are two strings: `J` (the set of jewels) and `S` (the set of stones). The goal is to count the number of jewels in the set of stones.

**Test Cases**

There are two test cases:

1. **JS-Set**: This test case uses a Set data structure to store the jewels, which automatically removes duplicates. It iterates through each character in the set of stones and checks if it's present in the set of jewels using the `has()` method. If found, it increments the count.
2. **JS-Array**: This test case uses an array to represent the set of jewels. It iterates through each character in the set of stones and checks if it's present in the array using the `includes()` method. If found, it increments the count.

**Options Compared**

The two test cases compare the performance of using a Set data structure versus an array to represent the set of jewels.

**Pros and Cons**

* **JS-Set (using Set)**:
	+ Pros:
		- Efficient lookup time (~O(1) on average)
		- Removes duplicates automatically
	+ Cons:
		- Requires more memory for storing the set
		- May be slower in certain scenarios due to the overhead of creating and managing sets
* **JS-Array (using Array)**:
	+ Pros:
		- Less memory usage compared to Set
		- Can be faster in certain scenarios due to the efficiency of array lookups
	+ Cons:
		- May have duplicate elements, which can slow down lookup time

**Library/Utility**

There is no specific library used in this benchmark. However, JavaScript provides built-in data structures and methods that make it easy to work with sets (e.g., `Set`, `has()`) and arrays (e.g., `Array.prototype.includes()`).

**Special JS Feature/Syntax**

The benchmark uses the following special feature/syntax:

* `const set = new Set(J.split(""));`: Creates a new Set object from an array of strings, which splits the input string `J` into individual characters using the `split()` method.

**Other Alternatives**

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

* Using a Trie data structure to store the set of jewels would likely provide faster lookup times compared to both Set and Array.
* Utilizing a more efficient algorithm for counting duplicates, such as using a hash table or a frequency map, could potentially outperform both Set and Array.

Keep in mind that these alternative approaches might come with additional overheads, complexity, or memory usage.

Related benchmarks:

Jewels and Stones (version: 0)

Comparing performance of: JS-Set vs JS-Array

Created: 3 years ago by: Guest

Jump to the latest result

JS-Set

JS-Array

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):