Benchmark: Loop with conditions VS Map narrowing chain

Script Preparation code:

var source = Array.from({
    length: 10000
}, (k, v) => k);
var result = {
    all: [],
    m6: [],
    found: false
};
var hit = 6*1345;

Tests:

Loop
for (var i = 0; i < source.length; i+=1) { if (source[i] % 2 === 0) { result.all.push(source[i]); if (source[i] % 6 === 0 ) { result.m6.push(source[i]); if(source[i] === found) result.found = true; } } }
Map
result.all = source.filter((item) => item%2===0); result.m6 = result.all.filter((item) => item%6===0); result.found = !!result.m6.find((item) => item === found);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Loop
Map

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 and its test cases.

**Benchmark Overview**

The benchmark is designed to compare two approaches for filtering an array of numbers: using a traditional `for` loop with conditions, and using the `map()` function in combination with `filter()`. The goal is to determine which approach is faster.

**Options Compared**

There are two options being compared:

1. **Traditional Loop**: This approach uses a `for` loop to iterate over the array, checking each element for specific conditions (e.g., if the element is even and divisible by 6). If the condition is met, it pushes the element to an array (`result.all`) and checks if it's equal to a predefined value (`found`).
2. **Map-Filter Approach**: This approach uses the `map()` function to create a new array with transformed elements (in this case, checking if each element is even), and then uses the `filter()` function to further filter the resulting array based on another condition (i.e., divisibility by 6). The final filtered array is assigned to `result.m6`, and a boolean value (`found`) is checked.

**Pros and Cons**

Here's a brief analysis of each approach:

* **Traditional Loop**: Pros:
	+ May be more readable for complex conditions.
	+ Can avoid unnecessary memory allocations.
Cons:
	+ Can be slower due to the overhead of incrementing an index variable.
	+ More prone to errors if the loop condition is incorrect.
* **Map-Filter Approach**: Pros:
	+ Generally faster, as it avoids the overhead of a loop and uses built-in array methods.
	+ Can take advantage of optimized implementations in modern browsers.
Cons:
	+ May be less readable for complex conditions.
	+ Requires more memory allocations (although this is typically negligible).

**Library/Function**

The `map()` function is used to create a new array with transformed elements. The `filter()` function is used to further filter the resulting array.

**Special JS Feature/Syntax**

There are no special JavaScript features or syntaxes being used in this benchmark, aside from the use of modern browser-specific features like `find()` and `filter()` methods.

**Other Alternatives**

If you're looking for alternative approaches to filtering arrays, consider:

* **Using a more complex loop condition**: Instead of using `map()` and `filter()`, you could implement a custom loop that checks multiple conditions at once.
* **Using a different data structure**: Depending on the specific use case, you might consider using a different data structure, such as an object or a trie, to store and filter the data.

Keep in mind that these alternatives may not be suitable for all situations, and the `map()`-`filter()` approach is often a good choice due to its efficiency and readability.

Latest run results:

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

User agent: Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:120.0) Gecko/20100101 Firefox/120.0

Browser/OS: Firefox 120 on Ubuntu

View result in a separate tab

Test name	Executions per second
Loop	152918.9 Ops/sec
Map	28647.0 Ops/sec

Related benchmarks:

Loop with conditions VS Map narrowing chain (version: 0)

Comparing performance of: Loop vs Map

Created: one year ago by: Guest

Jump to the latest result

Loop

Map

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):