Benchmark: Include vs Binary search in sorted array vs Map.has() vs Object[] (100 elements)

Script Preparation code:

var array = [];
var map = new Map();
var obj = {};

for (var i = 0; i < 100; i++){
  array.push(Math.floor(Math.random()* 100));
  map.set(i,true);
  obj[i]=true;
}
var sorted = array.sort()

function binaryIndexOf(searchElement) {

 
    var minIndex = 0;
    var maxIndex = this.length - 1;
    var currentIndex;
    var currentElement;
 
    while (minIndex <= maxIndex) {
        currentIndex = (minIndex + maxIndex) / 2 | 0;
        currentElement = this[currentIndex];
 
        if (currentElement < searchElement) {
            minIndex = currentIndex + 1;
        }
        else if (currentElement > searchElement) {
            maxIndex = currentIndex - 1;
        }
        else {
            return currentIndex;
        }
    }
 
    return -1;
}

Tests:

Includes
sorted.includes(5)
binary
binaryIndexOf.call(sorted, 5)
Map.has()
map.has(5);
Obj[]
obj[5]

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Includes
binary
Map.has()
Obj[]

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/134.0.0.0 Safari/537.36

Browser/OS: Chrome 134 on Mac OS X 10.15.7

View result in a separate tab

Map.has()

166.2M/s

Obj[]

150.1M/s

Includes

70.9M/s

binary

64.0M/s

View exact numbers

Test name	Executions per second
✓ Map.has()	166,225,984 Ops/sec
Obj[]	150,058,848 Ops/sec
Includes	70,938,480 Ops/sec
binary	64,030,868 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 provided benchmark and explain what's being tested.

**Benchmark Overview**

The benchmark measures the performance of four different approaches to find an element in a sorted array:

1. `includes()` method
2. Binary search (implemented as `binaryIndexOf()` function)
3. `Map.has()` method
4. Direct array access (`obj[5]`)

**Script Preparation Code**

The script preparation code creates three data structures:

* An empty array `array`
* A Map `map` with 100 elements, where each element has a unique key and a value of `true`.
* An object `obj` with 100 elements, where each element is a property with the same value as in the `map`.

The script then sorts the `array` using the `sort()` method.

**Individual Test Cases**

Each test case measures the performance of one specific approach:

1. **Includes**: Measures the time it takes to find an element (in this case, 5) using the `includes()` method.
2. **Binary**: Measures the time it takes to find an element (in this case, 5) using the custom binary search function.
3. **Map.has()**: Measures the time it takes to check if a key exists in the Map (in this case, the key 5).
4. **Obj[]**: Measures the time it takes to access an element directly in the object (`obj[5]`).

**Library and Special JS Features**

* `includes()` method: This is a built-in method of JavaScript arrays.
* `Map.has()`: This is a built-in method of JavaScript Maps.
* Binary search function: Custom implementation, not a standard JavaScript feature. However, this approach demonstrates how to implement binary search in JavaScript.
* Direct array access (`obj[5]`): This is a direct property access on an object.

**Pros and Cons**

Here's a brief summary of the pros and cons of each approach:

1. **Includes**: Fast and simple, but may be slower than custom implementations for large datasets.
2. **Binary**: More efficient than `includes()` method for large datasets, but requires custom implementation.
3. **Map.has()**: Fast and efficient, especially for large datasets, as it uses a hash table under the hood.
4. **Obj[]**: Direct access is generally fast, but may not be applicable for large objects or complex data structures.

**Other Considerations**

* The benchmark measures the time it takes to execute each test case, which provides insight into the performance characteristics of each approach.
* The use of a sorted array and random keys in the Map and object makes it easier to compare the performance of these approaches.
* The benchmark can be useful for identifying potential bottlenecks or optimization opportunities in code.

**Alternatives**

If you were to optimize this benchmark, you might consider:

* Using more efficient data structures, such as a binary search tree or a hash table.
* Implementing caching mechanisms to reduce the number of iterations required for large datasets.
* Optimizing the custom binary search function to reduce its execution time.
* Using other performance optimization techniques, such as parallel processing or lazy loading.

Include vs Binary search in sorted array vs Map.has() vs Object[] (100 elements) (version: 0)

Comparing performance of: `Includes vs binary vs Map.has() vs Obj[]`

Created: 3 years ago by: Guest

Go to the latest result

Includes

binary

Map.has()

Obj[]

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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

Include vs Binary search in sorted array vs Map.has() vs Object[] (100 elements) (version: 0)

Comparing performance of: Includes vs binary vs Map.has() vs Obj[]

Created: 3 years ago by: Guest

Go to the latest result

Includes

binary

Map.has()

Obj[]

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: `Includes vs binary vs Map.has() vs Obj[]`