Benchmark: Include vs Binary search in sorted array vs Map.has() vs Object[] (20 elements)

Script Preparation code:

var array = [];
var map = new Map();
var obj = {};

for (var i = 0; i < 20; 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(34)
binary
binaryIndexOf.call(sorted, 34)
Map.has()
map.has(34);
Obj[]
obj[34]

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:

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

LLMs can make mistakes. Check important info.

**Benchmark Overview**

The provided JSON represents a JavaScript benchmark test case on the MeasureThat.net website. The test aims to compare the performance of different methods for searching an element in a sorted array:

1. `includes()`
2. Binary search (`binaryIndexOf()` function)
3. Using a `Map` object with `has()` method
4. Direct array indexing (`obj[34]`)

**Options Compared**

The benchmark compares the performance of these four options on the same dataset, which consists of 20 randomly generated integers between 0 and 100.

**Pros and Cons of Each Approach**

1. **Includes()**: This method is likely to be slow because it involves a linear search through the array. However, it's often easier to implement than other methods.
	* Pros: Simple implementation, widely supported in most browsers.
	* Cons: Slow performance, may cause unnecessary iterations.
2. **Binary Search (`binaryIndexOf()` function)**: This method is generally faster than includes() because it uses a divide-and-conquer approach to find the element.
	* Pros: Efficient algorithm, relatively fast execution.
	* Cons: May be slower for small arrays or when the array is not sorted.
3. **Map.has()**: Using a `Map` object with the `has()` method can provide an efficient way to search for an element. The lookup operation has an average time complexity of O(1).
	* Pros: Fast performance, especially for large datasets.
	* Cons: Requires a Map object, which may not be available in all browsers or environments.
4. **Obj[]**: Direct array indexing is likely to be slow because it involves accessing the element at a specific index without any optimization.
	* Pros: Simple implementation, widely supported.
	* Cons: Slow performance due to direct access.

**Library and Purpose**

The `binaryIndexOf()` function uses a binary search algorithm to find an element in an array. This library is not built-in to JavaScript but is implemented by the browser or runtime environment.

**Special JS Feature/Syntax**

There are no special features or syntax used in this benchmark that would require specific knowledge of JavaScript.

**Other Considerations**

When working with large datasets, it's essential to consider data structures like Maps and arrays. In modern browsers, `Map` objects provide an efficient way to store key-value pairs, while arrays offer a convenient way to store collections of values.

In terms of optimization, the choice of data structure depends on the specific use case and performance requirements. For simple searches in small datasets, `includes()` or direct array indexing might be sufficient. However, for larger datasets or more complex search operations, using an optimized algorithm like binary search or a `Map` object with `has()` method can provide significant performance improvements.

**Alternatives**

Other alternatives to these methods include:

* Using a sorted data structure like a linked list or a balanced tree.
* Employing caching mechanisms to store previously accessed elements.
* Utilizing GPU acceleration or parallel processing for large-scale datasets.
* Leveraging browser-specific optimizations, such as SIMD instructions or just-in-time (JIT) compilation.

Related benchmarks:

Include vs Binary search in sorted array vs Map.has() vs Object[] (20 elements) (version: 0)

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

Created: 2 years ago by: Guest

Jump to the latest result

Includes

binary

Map.has()

Obj[]

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

No previous run results

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