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');
  }

Tests:

test_filter
aged_18_filter = items.filter(filter_callback);
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]);} }
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[i]=items[i];} }

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):

LLMs can make mistakes. Check important info.

Let's break down what's being tested in the provided JSON benchmark:

**Benchmark Definition**

The benchmark is designed to compare three approaches for filtering an array of objects based on a specific condition.

1. **Array Filtering (filter_callback)**: This approach uses the built-in `filter()` method, which applies a callback function to each element in the array and returns a new array with only the elements that pass the test.
2. **Manual Looping**: The first individual test case ("test_loop") implements a manual loop to iterate through the array and push objects with an age of 18 into a new array.
3. **Pre-Allocated Array (test_loop_optimized)**: In this approach, a new array is pre-allocated with the same length as the original array, and then iterates through the original array using another loop to assign objects to the corresponding index in the pre-allocated array.

**Options Compared**

The three approaches are compared in terms of performance (number of executions per second).

**Pros and Cons**

1. **Array Filtering (filter_callback)**:
	* Pros: concise, efficient, and easy to maintain.
	* Cons: may not be suitable for very large arrays due to memory requirements.
2. **Manual Looping (test_loop)**:
	* Pros: straightforward, low-memory overhead.
	* Cons: can be slower than the other approaches, especially for larger arrays.
3. **Pre-Allocated Array (test_loop_optimized)**:
	* Pros: can be faster for very large arrays due to reduced memory allocation and deallocation overhead.
	* Cons: requires more memory upfront and may have a higher initial cost.

**Library**

None of the individual test cases use any external libraries, but they do rely on built-in JavaScript methods and features (e.g., `filter()`, `push()`).

**Special JS Features or Syntax**

There are no special JavaScript features or syntaxes used in these benchmark definitions. The focus is on comparing different programming approaches to achieve a specific goal.

**Other Considerations**

Some other factors that could affect the performance of these approaches include:

* Array size: Larger arrays may favor more efficient methods like `filter()` and `test_loop_optimized`.
* JavaScript engine optimizations: Different browsers or JavaScript engines may optimize their execution of these methods differently, affecting results.
* Hardware capabilities: The speed of filtering operations can be influenced by hardware factors like CPU clock speed, cache size, and memory bandwidth.

**Alternatives**

Other approaches to filter an array could include:

* Using a library like Lodash's `filter()` function
* Employing more advanced data structures, such as binary search or tree-based data structures
* Utilizing parallel processing techniques for very large arrays

Keep in mind that the choice of approach depends on specific requirements and constraints, such as performance, memory usage, or code complexity.

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):