Benchmark: slice vs filter for index filtering

Script Preparation code:

var test=Array.from({length: 100},()=>Math.random())

Tests:

slice
test.slice(0,50)
filter
test.filter((e,i)=>i<=50)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
slice
filter

Fastest: N/A

Slowest: N/A

Autogenerated LLM Sumamry (model llama3.2:3b, generated 3 days ago):

LLMs can make mistakes. Check important info.

Let's break down the provided benchmark definition and test cases.

**Benchmark Definition:**

The benchmark is comparing two approaches to filter an array of numbers: using `slice()` and using `filter()`. The goal is to measure which approach performs better in terms of execution time.

**Script Preparation Code:**
```javascript
var test = Array.from({ length: 100 }, () => Math.random());
```
This code creates an array of 100 random numbers using the `Array.from()` method and the spread operator (`...`). This will be used as the input data for both benchmark tests.

**Html Preparation Code:**
There is no HTML preparation code provided, which means that the browser's JavaScript engine will not have to parse any HTML before running the benchmarks.

**Individual Test Cases:**

The two test cases are:

1. `slice`
```javascript
test.slice(0, 50)
```
This line of code uses the `slice()` method to extract a subset of 50 elements from the original array starting from index 0.

2. `filter`
```javascript
test.filter((e, i) => i <= 50)
```
This line of code uses the `filter()` method with an arrow function ( anonymous function that takes two arguments, `e` and `i`) to filter out elements where `i` is greater than 50.

**Library Usage:**

There is no explicit library usage mentioned in the benchmark definition. However, it's worth noting that both `slice()` and `filter()` are built-in methods of the Array prototype in JavaScript.

**Special JS Features or Syntax:**

There are a few features used here:

* **Arrow Functions:** The `filter()` method uses an arrow function to define the callback function.
* **Spread Operator (`...`):** The `Array.from()` method uses the spread operator to create an array from an object literal.

**Pros and Cons of Different Approaches:**

1. **slice():**
	* Pros:
		+ Efficient for fixed-size subarrays
		+ Can be inlined by some JavaScript engines (e.g., V8)
	* Cons:
		+ Does not preserve the original array's data structure
		+ Can be slower for large arrays due to cache locality issues
2. **filter():**
	* Pros:
		+ Preserves the original array's data structure
		+ Suitable for dynamic subarrays (e.g., when the filter condition is based on a variable)
	* Cons:
		+ Can be slower than `slice()` for large arrays due to overhead of iterating over the elements
		+ May not be inlined by some JavaScript engines

**Other Considerations:**

* **Browser Cache:** Some browsers may cache the results of these benchmarks, which can affect the accuracy of the measurements.
* **JavaScript Engine Optimizations:** Modern JavaScript engines (e.g., V8) are optimized to improve performance and reduce overhead. However, this can also lead to variations in execution times between different runs.

**Alternatives:**

Other methods for filtering arrays include:

* Using `forEach()` with a callback function
* Using `reduce()` with an accumulator function
* Using a third-party library like Lodash

Keep in mind that these alternatives may have trade-offs in terms of performance, readability, or maintainability.

Latest run results:

Run details: (Test run date: 9 months ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36

Browser/OS: Chrome 120 on Windows

View result in a separate tab

Test name	Executions per second
slice	5286580.0 Ops/sec
filter	1705283.0 Ops/sec

Related benchmarks:

slice vs filter for index filtering (version: 0)

Comparing performance of: slice vs filter

Created: 9 months ago by: Guest

Jump to the latest result

slice

filter

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Sumamry (model llama3.2:3b, generated 3 days ago):