Benchmark: Array.includes vs Set.has vas Map.has

Script Preparation code:

var a = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10];
var b = new Set([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]);
var c = new Map([[1, 1], [2, 2], [3, 3], [4, 4], [5, 5], [6, 6], [7, 7], [8, 8], [9, 9], [10, 10]]);

Tests:

Array includes
return a.includes(9);
Set has
return b.has(9);
Map has
return c.has(9);

Historical Performance Data aggregated from 167 community runs

Desktop Results:

Chrome 134 runs

Set has

27.8M/s

Array includes

27.0M/s

Map has

25.8M/s

View exact numbers

✓ Set has: 27,833,334 ops/sec
• Array includes: 27,016,640 ops/sec
• Map has: 25,818,656 ops/sec

Firefox 14 runs

Set has

1772.6M/s

Map has

1645.9M/s

Array includes

53.2M/s

Fastest: Set has (33.3x faster)

View exact numbers

✓ Set has: 1,772,593,408 ops/sec
• Map has: 1,645,939,712 ops/sec
• Array includes: 53,206,844 ops/sec

Mobile Results:

Chrome 14 runs

Map has

23.3M/s

Set has

22.4M/s

Array includes

16.2M/s

Fastest: Map has (1.4x faster)

View exact numbers

✓ Map has: 23,277,172 ops/sec
• Set has: 22,446,424 ops/sec
• Array includes: 16,242,416 ops/sec

Safari 5 runs

Map has

515.6M/s

Set has

509.7M/s

Array includes

70.5M/s

Fastest: Map has (7.3x faster)

View exact numbers

✓ Map has: 515,626,432 ops/sec
• Set has: 509,659,776 ops/sec
• Array includes: 70,528,304 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
Array includes
Set has
Map has

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/135.0.0.0 Safari/537.36

Browser/OS: Chrome 135 on Mac OS X 10.15.7

View result in a separate tab

Set has

193.8M/s

Map has

190.6M/s

Array includes

91.0M/s

View exact numbers

Test name	Executions per second
✓ Set has	193,776,864 Ops/sec
Map has	190,644,896 Ops/sec
Array includes	91,008,784 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 benchmark and explain what's being tested.

**Benchmark Overview**

The benchmark compares the performance of three different data structures in JavaScript: arrays, sets, and maps. The test cases focus on searching for an element within each data structure using the `has` method.

**Options Being Compared**

* **Array.includes**: This method checks if a specific element is present in the array.
* **Set.has**: This method checks if a specific value is present in the set.
* **Map.has**: This method checks if a specific key-value pair exists in the map.

**Pros and Cons of Each Approach**

1. **Array.includes**:
	* Pros: Simple, widely supported, and fast for small arrays.
	* Cons: Slow for large arrays (O(n) search), may not be as efficient as other data structures for very large datasets.
2. **Set.has**:
	* Pros: Fast for sets with a limited number of elements (O(1) lookup).
	* Cons: May not be suitable for very large sets, and its performance degrades as the set size increases.
3. **Map.has**:
	* Pros: Fast for maps with a limited number of key-value pairs (O(1) lookup).
	* Cons: May not be suitable for very large maps, and its performance degrades as the map size increases.

**Library Used**

None explicitly mentioned in the provided benchmark definition and test cases. However, it's likely that the JavaScript engine being used (e.g., V8 in Chrome) provides an implementation of these data structures.

**Special JS Feature/Syntax**

The benchmark uses the `var` keyword for variable declarations, which is a legacy syntax in JavaScript. In modern JavaScript, you would typically use `let` or `const`.

**Alternative Approaches**

If you need to search for an element within a large dataset, other alternatives might include:

1. **Array.prototype.indexOf()**: Similar to `Array.includes`, but returns the index of the first occurrence instead of a boolean value.
2. **Binary Search**: For sorted arrays, binary search can be significantly faster than linear search (O(log n) vs O(n)).
3. **Data Structures like Hash Tables or Trie**: These data structures are optimized for fast lookups and might be more suitable for large datasets.

Keep in mind that the best approach depends on the specific requirements of your use case and the characteristics of your dataset.

Array.includes vs Set.has vas Map.has (version: 0)

Comparing performance of: `Array includes vs Set has vs Map has`

Created: 5 years ago by: Guest

Go to the latest result

Array includes

Set has

Map has

Historical Performance Data aggregated from 167 community runs

Desktop Results:

Chrome 134 runs

Firefox 14 runs

Mobile Results:

Chrome 14 runs

Safari 5 runs

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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

Array.includes vs Set.has vas Map.has (version: 0)

Comparing performance of: Array includes vs Set has vs Map has

Created: 5 years ago by: Guest

Go to the latest result

Array includes

Set has

Map has

Historical Performance Data aggregated from 167 community runs

Desktop Results:

Chrome 134 runs

Firefox 14 runs

Mobile Results:

Chrome 14 runs

Safari 5 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: `Array includes vs Set has vs Map has`