Benchmark: index vs map114 - MeasureThat.net

Script Preparation code:

var myArray = [];
for (let i = 0; i < 9999; i++) {
	myArray.push(i)
}

Tests:

1
var unique = myArray.filter((value, index, array) => array.indexOf(value) === index);
2
let map = {}; myArray.forEach(v => { map[v]=true }) Object.keys(map);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
1
2

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: one year ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/129.0.0.0 Safari/537.36

Browser/OS: Chrome 129 on Windows

View result in a separate tab

Test name	Executions per second
1	107.1 Ops/sec
2	6714.4 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 JSON data and explain what's being tested, compared, and considered.

**Benchmark Definition**
The `Script Preparation Code` section creates an array `myArray` with 9999 elements, and the `Html Preparation Code` is empty. This setup likely serves as a common base for both test cases, ensuring consistent conditions for comparison.

**Test Cases**

1. **`var unique = myArray.filter((value, index, array) => array.indexOf(value) === index);`**
	* The library used is the built-in `Array.prototype.filter()` method.
	* Purpose: This method filters an array to only include elements that pass a test provided by a function.
	* Approach:
		+ Filtered array creation ( O(n) time complexity)
		+ Array indexing and comparison (O(1) time complexity, since it's using `indexOf()`)
	* Pros: Efficient for small arrays or when the filtering condition is simple. However, for very large arrays or complex conditions, other methods may be more efficient.
	* Cons: May have a higher memory overhead due to creating an intermediate array, and its performance degrades with increasing array size.
2. **`let map = {}; myArray.forEach(v => { map[v]=true }) Object.keys(map);`**
	* The library used is the `forEach()` method of arrays.
	* Purpose: This code creates a hash map (object) by iterating over the array and assigning each element as a key with a value of `true`.
	* Approach:
		+ Array iteration and object creation (O(n) time complexity)
		+ Object property lookup and assignment (O(1) time complexity, since it's using `map[v]=true`)
		+ Object.keys() method to retrieve the keys (O(n) time complexity)
	* Pros: Can be efficient for small arrays or when creating an object with specific key-value pairs. However, its performance may degrade with increasing array size.
	* Cons: May have a higher memory overhead due to creating an intermediate object and iterating over the array.

**Comparison**
The test cases compare the execution time of two approaches:

1. `filter()` method
2. `forEach()` method followed by `Object.keys()`

The comparison aims to identify which approach is faster, considering both small arrays (e.g., 9999 elements) and large arrays ( likely beyond 10000).

**Other Considerations**

* The benchmark uses a common base script (`myArray` creation) for both test cases, ensuring consistent conditions.
* The `DevicePlatform`, `OperatingSystem`, and `Browser` information is provided to account for potential differences in performance across various environments.
* The `ExecutionsPerSecond` value represents the number of iterations completed per second.

**Alternative Approaches**

For array filtering or creating hash maps:

1. **Using a custom implementation**: Writing a tailored function to filter or create an object from scratch, potentially with optimized algorithms and data structures (e.g., using `Set` for fast lookup).
2. **Using external libraries**: Depending on the specific requirements, other libraries like Lodash or Underscore.js might offer more efficient filtering or object creation methods.
3. **Native WebAssembly (WASM)**: If performance is critical, compiling code to WASM and leveraging native optimizations could provide a significant boost.

Keep in mind that the choice of approach depends on the specific use case, array size, and desired trade-offs between efficiency, memory usage, and development complexity.

Related benchmarks:

index vs map114 (version: 0)

Comparing performance of: 1 vs 2

Created: one year ago by: Guest

Jump to the latest result

1

2

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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