Benchmark: Hashmap vs Array.Filter 10

Script Preparation code:

const counter = 10
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(500) }
Array.filter
for (let i = 0; i < 20000; i++) { window.array.filter((value) => value === 500) }

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

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.

Let's break down the provided benchmark and explain what's being tested.

**Benchmark Purpose:**
The benchmark compares the performance of two approaches:

1. **Hashmap**: Using the built-in `Map` data structure in JavaScript to check if an element exists.
2. **Array.filter**: Using the `filter()` method on a sorted array to find elements that match a certain condition.

**Comparison and Options:**

* **Hashmap (using `Map.has()`)**
	+ Pros:
		- Fast lookups, with an average time complexity of O(1) for existence checks.
		- Can be more memory-efficient than using arrays.
	+ Cons:
		- May not be as efficient for large datasets or when the size of the map is close to its capacity.
* **Array.filter (using `filter()` method)**
	+ Pros:
		- Suitable for larger datasets and can handle a wide range of element sizes.
		- Can be more intuitive for some developers who are familiar with this syntax.
	+ Cons:
		- Time complexity is O(n), which means it scales linearly with the size of the array. This can lead to slower performance for large datasets.

**Library:**
The benchmark uses the built-in `Map` data structure in JavaScript, which is a part of the standard library. It's a hash table implementation that provides fast lookups and efficient storage.

**Special JS Feature/Syntax:**
There are no special JavaScript features or syntaxes being tested in this benchmark. The focus is on comparing two established approaches to achieving a similar goal (checking for element existence).

**Other Alternatives:**

* For checking if an element exists in an array, you could consider using `includes()` method instead of `filter()`. While `filter()` still has a time complexity of O(n), it's often faster in practice due to its more efficient internal implementation.
* Another alternative is using a library like Lodash's `some()` function or Ramda's `any()` function, which provide faster lookup capabilities than `Array.filter()`.

**Benchmark Preparation Code:**
The script preparation code creates two variables:

1. `counter`: an integer set to 10, used as the size of both the hashmap and array.
2. `map`: a new instance of the `Map` data structure.
3. `array`: a new instance of the `Array` data structure, initialized with 10 elements.

The script then sets these variables in the global scope for the benchmark to use.

**Individual Test Cases:**
Each test case consists of:

1. A single loop iteration that checks if an element exists using either the `Map.has()` method (Hashmap) or the `filter()` method on the array (Array.filter).
2. The size of the dataset is set to 20,000 iterations.

The benchmark executes each test case multiple times and reports the raw UA string (User Agent), browser version, device platform, operating system, executions per second, and test name.

Related benchmarks:

Hashmap vs Array.Filter 10 (version: 0)

Comparing performance of: Hashmap vs Array.filter

Created: 3 years ago by: Guest

Jump to the latest result

Hashmap

Array.filter

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):