Benchmark: Array_Filter_VS_loop

Script Preparation code:

  var items = [];
  var names = ['george', 'nick', 'jack', 'herodotus', 'joanne'];
  for (var i = 0; i < 2000; i++) {
   var obj = {
    name: names[i % 5] + '-' + i,
    age: i % 10 + 15
   };
   items.push(obj);
  }
  
  function filter_callback(item) {
   return (item.age == '18');
  }
  
  function test_filter() {
   var aged_18_filter = items.filter(filter_callback);
  }
  
  function test_loop() {
   var aged_18_loop = [];
   for (var i = 0; i < items.length; i++) {
    if (items[i].age == '18') aged_18_loop.push(items[i]);
   }
  }

 function test_loop_optimized() {
 var length=items.length
   var aged_18_loop = new Array(length);
   for (var i = 0; i < length; i++) {
    if (items[i].age == '18') aged_18_loop=items[i];
   }
  }

Tests:

test_filter
test_filter()
test_loop
test_loop()
test_loop_optimized
test_loop_optimized()

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
test_filter
test_loop
test_loop_optimized

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 7 years ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/60.0.3112.113 Safari/537.36

Browser/OS: Chrome 60 on Windows

View result in a separate tab

Test name	Executions per second
test_filter	19559.7 Ops/sec
test_loop	1955.3 Ops/sec
test_loop_optimized	3585.2 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's break down the benchmark and its test cases.

**Benchmark Definition**

The benchmark is designed to measure the performance of two approaches: using a filter callback function (`filter_callback`) and using a traditional `for` loop (`test_loop`).

The script preparation code generates an array of 2000 objects, each with a unique name and age. The `filter_callback` function takes an object as input and returns `true` if the object's age is equal to `'18'`. The `test_filter` function uses this callback to filter the array.

The alternative approach, `test_loop_optimized`, attempts to optimize the traditional loop by pre-allocating an array of the same length as the original array. However, this approach still uses a traditional `for` loop and assigns each element individually, which is not an optimized solution.

**Options Compared**

* **Filter callback**: This approach uses the `Array.prototype.filter()` method with a callback function to filter the array.
* **Traditional `for` loop**: This approach uses a traditional `for` loop to iterate over the array and push elements into a new array if they meet the condition.
* **Optimized traditional `for` loop**: This approach attempts to optimize the traditional loop by pre-allocating an array of the same length as the original array.

**Pros and Cons**

* **Filter callback**:
	+ Pros: concise, easy to read, and maintainable code. The `Array.prototype.filter()` method is a built-in method that is widely supported.
	+ Cons: may incur additional overhead due to the creation of a new array and the use of function calls.
* **Traditional `for` loop**:
	+ Pros: can be more control-oriented and flexible, especially when working with complex data structures. However, this approach requires explicit memory management.
	+ Cons: can be verbose and harder to read for larger arrays or complex logic.
* **Optimized traditional `for` loop**: This approach is not truly optimized and still uses a traditional loop.

**Library and Special JS Features**

The benchmark uses the following library:

* None. The benchmark code does not rely on any external libraries.

However, it's worth noting that the `Array.prototype.filter()` method is a built-in method of JavaScript arrays, but it may be supported differently in older browsers or environments.

Related benchmarks:

Array_Filter_VS_loop (version: 0)

Comparing performance of: test_filter vs test_loop vs test_loop_optimized

Created: 7 years ago by: Guest

Jump to the latest result

test_filter

test_loop

test_loop_optimized

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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