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_looptest_looptest_looptest_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:

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

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Let's break down the provided benchmark and explain what's being tested.

**Benchmark Definition:**

The benchmark is designed to compare two approaches for filtering an array of objects in JavaScript:

1. **`filter_callback` approach**: This method uses a callback function `filter_callback(item)` to filter the array. The callback function returns `true` if the object's `age` property equals `'18'`.
2. **`loop` approach**: This method uses a traditional `for` loop to iterate through the array and push objects with an age of `'18'` into a new array `aged_18_loop`.
3. **Optimized `loop` approach**: This is similar to the previous `loop` approach but optimized for performance by pre-allocating an array of the same length as the input array `items.length`. The loop then uses this pre-allocated array to store the filtered objects.

**Options being compared:**

The benchmark is comparing three options:

1. **`filter_callback` approach**: A callback function-based filtering method.
2. **Traditional `loop` approach**: A traditional `for` loop-based filtering method.
3. **Optimized `loop` approach**: An optimized version of the traditional `loop` approach, which pre-allocates an array to improve performance.

**Pros and Cons:**

Here's a brief summary of each approach:

1. **`filter_callback` approach**:
	* Pros:
		+ Easy to implement and read.
		+ Can be easily parallelized for better performance.
	* Cons:
		+ May not be as efficient as other methods due to the overhead of function calls.
2. **Traditional `loop` approach**:
	* Pros:
		+ Simple and easy to understand.
	* Cons:
		+ Inefficient, especially for large datasets, due to the need to iterate through each element individually.
3. **Optimized `loop` approach**:
	* Pros:
		+ Significantly faster than traditional looping due to pre-allocating an array.
	* Cons:
		+ More complex and harder to read due to the use of pre-allocated arrays.

**Library:**

The benchmark uses the built-in JavaScript `Array.prototype.filter()` method, which is a high-performance filtering method that's optimized for modern browsers. The `filter_callback` approach manually implements this method using a callback function.

**Special JS feature or syntax:**

There are no special features or syntaxes used in this benchmark beyond standard JavaScript features and syntax.

**Other alternatives:**

If you're looking for alternative methods to filter an array, some other options include:

1. `Array.prototype.map()` with a conditional transformation function.
2. `Array.prototype.every()` with a conditional predicate function.
3. Using a library like Lodash or Ramda, which provide more advanced filtering and mapping functions.

However, the `filter_callback` approach is not necessarily an alternative to these methods but rather a specific implementation of filtering using a callback function.

Related benchmarks:

Array_Filter_VS_loop (version: 0)

Comparing performance of: test_filter vs test_loop vs test_loop_optimized

Created: 8 years ago by: Guest

Jump to the latest result

test_filter

test_loop

test_loop_optimized

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

No previous run results

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