Benchmark: Array filter vs Set delete

Script Preparation code:

function arrayWithLength(length) {
    return Array(length).fill().map((_, i) => i);
}
var arr10 = arrayWithLength(10);
var arr1000 = arrayWithLength(1000);
var arr1000000 = arrayWithLength(1000000);
var set10 = new Set(arr10);
var set1000 = new Set(arr1000);
var set1000000 = new Set(arr1000000);

Tests:

Filter arr10
arr10.filter(i => i === 5);
Filter set10
set10.delete(5);
Filter arr1000
arr1000.filter(i => i === 500);
Filter set1000
set1000.delete(500);
Filter arr1000000
arr1000000.filter(i => i === 500000);
Filter set1000000
set1000000.delete(500000);

Historical Performance Data aggregated from 18 community runs

Desktop Results:

Chrome 18 runs

Filter set10

21.3M/s

Filter set1000000

21.1M/s

Filter set1000

20.5M/s

Filter arr10

18.2M/s

Filter arr1000

1.1M/s

Filter arr1000000

151/s

Fastest: Filter set10 (141067.6x faster)

View exact numbers

✓ Filter set10: 21,257,392 ops/sec
• Filter set1000000: 21,133,252 ops/sec
• Filter set1000: 20,450,984 ops/sec
• Filter arr10: 18,155,472 ops/sec
• Filter arr1000: 1,084,282 ops/sec
• Filter arr1000000: 151 ops/sec

Results show median performance across all community submissions. Run this benchmark yourself to verify on your device.

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Filter arr10
Filter set10
Filter arr1000
Filter set1000
Filter arr1000000
Filter set1000000

Fastest: N/A

Slowest: N/A

Latest run results:

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

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:138.0) Gecko/20100101 Firefox/138.0

Browser/OS: Firefox 138 on Mac OS X 10.15

View result in a separate tab

Filter set1000000

102.1M/s

Filter set1000

96.4M/s

Filter set10

63.8M/s

Filter arr10

39.8M/s

Filter arr1000

301K/s

Filter arr1000000

302/s

View exact numbers

Test name	Executions per second
✓ Filter set1000000	102,126,112 Ops/sec
Filter set1000	96,420,704 Ops/sec
Filter set10	63,776,312 Ops/sec
Filter arr10	39,760,424 Ops/sec
Filter arr1000	301,379 Ops/sec
Filter arr1000000	302 Ops/sec

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

LLMs can make mistakes. Check important info.

**Benchmark Explanation**

The provided benchmark measures the performance of two approaches to filter out elements from an array: using the `filter()` method and deleting individual elements from a `Set` data structure.

**Options Compared**

1. **Array Filter**: The `filter()` method creates a new array with all elements that pass a test implemented by providing a function. This approach modifies the original array.
2. **Set Delete**: Deleting an element from a `Set` removes it from the set, making it more efficient for large datasets.

**Pros and Cons**

* **Array Filter**:
	+ Pros: Easy to implement and understand, works with arrays of any type.
	+ Cons: Creates a new array, which can be memory-intensive for large datasets. The performance is generally slower than using `Set` delete.
* **Set Delete**:
	+ Pros: More efficient for large datasets since it modifies the original set. Faster execution times compared to `Array Filter`.
	+ Cons: Requires a `Set` data structure, which may not be suitable for all use cases (e.g., non-numeric values). The implementation can be less intuitive.

**Library Used**

In this benchmark, the `Set` library is used to create and manipulate sets. Sets are implemented as hash tables in modern JavaScript engines.

**Special JS Feature/Syntax**

None mentioned explicitly, but note that the `filter()` method uses a callback function (e.g., `(i => i === 5)`) which is a common feature of functional programming in JavaScript. The use of arrow functions (`(i => i === 5)`) is also supported.

**Other Alternatives**

For large datasets, other alternatives to `Set` delete could be:

* **Using a custom data structure**, like a balanced binary search tree or a hash table.
* **Parallel processing** techniques, like using multiple CPU cores to process elements in parallel.
* **GPU acceleration**, using libraries that can execute JavaScript on the GPU.

However, these alternatives are typically more complex and may not be suitable for all use cases. The `Set` delete approach is generally a good choice when dealing with large datasets and performance-critical code.

Array filter vs Set delete (version: 0) Benchmark.js

Comparing performance of: `Filter arr10 vs Filter set10 vs Filter arr1000 vs Filter set1000 vs Filter arr1000000 vs Filter set1000000`

Created: 5 years ago by: Guest

Go to the latest result

Filter arr10

Filter set10

Filter arr1000

Filter set1000

Filter arr1000000

Filter set1000000

Historical Performance Data aggregated from 18 community runs

Desktop Results:

Chrome 18 runs

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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

Array filter vs Set delete (version: 0) Benchmark.js

Comparing performance of: Filter arr10 vs Filter set10 vs Filter arr1000 vs Filter set1000 vs Filter arr1000000 vs Filter set1000000

Created: 5 years ago by: Guest

Go to the latest result

Filter arr10

Filter set10

Filter arr1000

Filter set1000

Filter arr1000000

Filter set1000000

Historical Performance Data aggregated from 18 community runs

Desktop Results:

Chrome 18 runs

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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

Related benchmarks

Comparing performance of: `Filter arr10 vs Filter set10 vs Filter arr1000 vs Filter set1000 vs Filter arr1000000 vs Filter set1000000`