Benchmark: filters vs for loop

Tests:

filters
let array = ["ab", "ac", "ba", "cd", "bd", "ce", "ae", "bb", "bm", "cm", "dd", "af", "ag", "cf", "dm"] array = [...array, ...array, ...array, ...array, ...array, ...array] array = [...array, ...array, ...array, ...array, ...array, ...array] const a = array.filter((i) => i.startsWith("a")) const b = array.filter((i) => i.startsWith("b"))
for loop
let array = ["ab", "ac", "ba", "cd", "bd", "ce", "ae", "bb", "bm", "cm", "dd", "af", "ag", "cf", "dm"] array = [...array, ...array, ...array, ...array, ...array, ...array] array = [...array, ...array, ...array, ...array, ...array, ...array] const a = [] const b = [] for (let i of array) { if (i.startsWith("a")) { a.push(i) } else if (i.startsWith("b")) { b.push(i) } }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
filters
for loop

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 the provided benchmark definition and test cases.

**Benchmark Definition**
The benchmark is defined as a comparison between two approaches: using filters and using a for loop to achieve the same result. The script and HTML preparation codes are empty, which means that the focus is solely on the JavaScript code itself.

**Test Cases**

There are two test cases:

1. **Filters**: This test case uses the `filter()` method to create an array of elements that start with "a" or "b". The benchmark definition includes the creation of a large array and repeated concatenation operations to generate more data, which is then filtered.
2. **For Loop**: This test case uses a traditional for loop to iterate through the array and push elements into two separate arrays based on whether they start with "a" or "b".

**Options Compared**

The benchmark compares two approaches:

1. **Filters**: Uses the `filter()` method to create an array of filtered elements.
2. **For Loop**: Uses a traditional for loop to iterate through the array and push elements into two separate arrays.

**Pros and Cons of Each Approach**

* **Filters**:
	+ Pros: Efficient, concise, and easy to read.
	+ Cons: Can be slower than iterative approaches due to function call overhead.
* **For Loop**:
	+ Pros: Can be faster for large datasets since it avoids the function call overhead, but can be more verbose and harder to read.
	+ Cons: More error-prone and less concise.

**Library Used**
None explicitly mentioned in the provided benchmark definition. However, the `filter()` method is a built-in JavaScript method that creates a new array with elements that meet a condition.

**Special JS Feature/Syntax**
There are no special JavaScript features or syntax used in this benchmark definition.

**Other Considerations**

When choosing between filters and for loops, consider the following factors:

* Dataset size: For very large datasets, iterative approaches might be faster.
* Performance: If speed is critical, a for loop might be a better choice.
* Code readability: Filters are generally more concise and easier to read than for loops.

**Alternatives**

Other alternatives for filtering arrays include:

1. `some()` method
2. Regular expressions (regex)
3. Array.prototype.reduce()
4. Custom iterative approaches using array indices

These alternatives might offer different trade-offs in terms of performance, readability, or conciseness, and might be worth exploring depending on the specific use case.

Latest run results:

Run details: (Test run date: 10 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
filters	230364.3 Ops/sec
for loop	261420.5 Ops/sec

Related benchmarks:

filters vs for loop (version: 0)

Comparing performance of: filters vs for loop

Created: 10 months ago by: Registered User

Jump to the latest result

filters

for loop

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):