Benchmark: Hashmap vs Array.Filter

Script Preparation code:

const counter = 10000
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:

Run details: (Test run date: 3 days ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/130.0.0.0 Safari/537.36

Browser/OS: Chrome 130 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
Hashmap	735.6 Ops/sec
Array.filter	0.4 Ops/sec

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

LLMs can make mistakes. Check important info.

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

**Benchmark Definition**

The benchmark is designed to compare two approaches:

1. **Hashmap**: Using the `Map` data structure in JavaScript to test its performance for fast lookups.
2. **Array.filter**: Using the `filter()` method on an array to test its performance for filtering elements based on a condition.

**Options Compared**

In this benchmark, we have two options being compared:

*   Hashmap (using `Map`)
*   Array.filter

These two approaches are being tested for their execution speed and efficiency in performing fast lookups or filtering elements.

**Pros and Cons of Each Approach:**

### Hashmap

Pros:

*   Hashmaps provide fast lookups with an average time complexity of O(1), making them suitable for applications that require frequent searches.
*   They can handle a large number of key-value pairs efficiently.

Cons:

*   Hashmaps are not designed specifically for filtering elements, which can lead to slower performance compared to other data structures like arrays.

### Array.filter

Pros:

*   The `filter()` method is optimized for filtering elements in an array and has a time complexity of O(n), making it suitable for applications that require element removal.
*   It's a built-in method, so you don't need to create a custom implementation.

Cons:

*   The `filter()` method scans the entire array, which can lead to slower performance compared to other data structures like hashmaps.
*   It may not be as efficient for large datasets since it requires additional memory allocations and copies of elements.

**Library and Purpose**

In this benchmark, no specific library is being used. However, it's worth noting that some JavaScript environments might use a `Map` implementation that's derived from the V8 engine (used in Chrome). This could affect the performance results.

**Special JS Feature or Syntax**

There are no special JavaScript features or syntax mentioned in this benchmark. The code is straightforward and uses standard JavaScript constructs.

**Other Alternatives**

Some other data structures you might consider for this type of benchmark include:

*   **Set**: Similar to `Map`, but it's more lightweight and optimized for fast lookups.
*   **Sqrtcache**: A custom implementation of a cache that uses a square root table for fast lookups.
*   **Trie** (also known as a prefix tree): An efficient data structure for storing and searching key-value pairs, especially when the keys are strings.

Keep in mind that these alternatives might have different performance characteristics depending on your specific use case and requirements.

Related benchmarks:

Hashmap vs Array.Filter (version: 0)

Comparing performance of: Hashmap vs Array.filter

Created: 4 years 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):