Benchmark: testowy benchmark123

Script Preparation code:

const counter = 2
const map = new Map()
const array = new Array(counter)

window.counter = counter
window.map = map
window.array = array
for (let i = 0; i < counter; i++) {
    map.set(i)
    array[counter] = i
}

Tests:

Hashmap
for (let i = 0; i < 20000; i++) { window.map.has(1) }
Array.filter
for (let i = 0; i < 20000; i++) { window.array.filter((value) => value === 1) }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Hashmap
Array.filter

Fastest: N/A

Slowest: N/A

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

LLMs can make mistakes. Check important info.

Let's break down what's being tested in the provided JSON data.

**Benchmark Definition**

The benchmark definition is a JavaScript code snippet that represents the test case. In this case, we have two test cases:

1. "Hashmap"
2. "Array.filter"

**Options Compared**

In these test cases, the options compared are:

* The `has` method of a Map object in the first test case ("Hashmap").
* The `filter` method of an Array object in the second test case ("Array.filter").

These two methods are being compared to measure their performance.

**Pros and Cons of Different Approaches**

1. **Using the `has` method of a Map object:**
	* Pros:
		+ Maps provide faster lookups compared to Arrays.
		+ The `has` method is optimized for exact key matching, which can be beneficial in certain scenarios.
	* Cons:
		+ May not be suitable for large datasets or complex searches.
2. **Using the `filter` method of an Array object:**
	* Pros:
		+ Arrays are widely supported and familiar to developers.
		+ The `filter` method is a more general-purpose function that can handle various filtering scenarios.
	* Cons:
		+ May be slower than using a Map for large datasets or complex searches.

**Library Usage**

In the "Hashmap" test case, the `Map` object from the JavaScript standard library is being used. A Map is a data structure that stores key-value pairs and provides fast lookups, insertions, and deletions.

**Special JS Feature/Syntax**

There are no special JavaScript features or syntax used in these test cases. The code snippets are straightforward and use standard JavaScript syntax.

**Other Considerations**

When writing benchmarks like this one, it's essential to consider the following:

* **Warm-up**: The benchmark should warm up the engine by running some initial code before measuring the performance of the specific test case.
* **Memory allocation**: Make sure that the benchmark does not allocate too much memory or cause any significant memory fragmentation.
* **Cache behavior**: Consider how caching might affect the benchmark results, especially if the test cases involve repeated lookups or iterations.

**Alternatives**

Other alternatives for measuring JavaScript performance include:

* V8 Profiler (Chrome DevTools): A built-in profiler that provides detailed information on CPU and memory usage.
* WebKit Inspector (Safari): A similar inspector to Chrome DevTools, optimized for WebKit engines.
* Node.js Performance: A benchmarking tool specifically designed for Node.js applications.
* Benchmarking libraries like `Benchmark.js` or `jsbench`: Lightweight, easy-to-use libraries that provide a simple way to write and run benchmarks.

Keep in mind that each of these alternatives has its strengths and weaknesses, and the choice of which one to use depends on your specific needs and goals.

Latest run results:

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

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/129.0.0.0 Safari/537.36

Browser/OS: Chrome 129 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
Hashmap	394.3 Ops/sec
Array.filter	255.0 Ops/sec

Related benchmarks:

testowy benchmark123 (version: 0)

Comparing performance of: Hashmap vs Array.filter

Created: one month ago by: Guest

Jump to the latest result

Hashmap

Array.filter

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):