Optimizing Memory Usage: Eliminating Unnecessary Object Allocation in JavaScript

JavaScript's dynamic nature and frequent object creation can lead to performance bottlenecks, especially in long-running applications or those dealing with large datasets. This challenge focuses on identifying and eliminating unnecessary object allocations within a given JavaScript code snippet, aiming to improve memory efficiency and overall performance. You'll be provided with a function and asked to refactor it to minimize object creation without altering its core functionality.

Problem Description

You are given a JavaScript function processData that takes an array of numbers as input and performs several operations on it. The current implementation creates multiple intermediate objects (arrays) during these operations, leading to unnecessary memory allocation and potential performance degradation. Your task is to refactor the processData function to achieve the same result – returning a new array containing the squares of even numbers from the input array, filtered to only include numbers greater than 10 – while minimizing the creation of new objects. Focus on in-place modifications and avoiding unnecessary array copies.

What needs to be achieved:

• The function must return a new array containing the squares of even numbers from the input array that are greater than 10.
• The original input array must remain unchanged.
• The refactored function should be as performant as possible, minimizing object allocations.

Key Requirements:

• Avoid creating unnecessary intermediate arrays.
• Prioritize in-place operations where possible.
• Maintain the original functionality of the function.

Expected Behavior:

The function should produce the same output as the original implementation, but with significantly reduced memory usage.

Edge Cases to Consider:

• Empty input array.
• Input array containing no even numbers.
• Input array containing no numbers greater than 10.
• Input array containing a mix of positive, negative, and zero values.

Examples

Example 1:

Input: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]
Output: [16, 36, 64, 100, 144, 196]
Explanation: The function filters for even numbers greater than 10 (12, 14, 16), squares them (144, 196, 256), and returns the resulting array.

Example 2:

Input: [1, 3, 5, 7, 9]
Output: []
Explanation: The input array contains no even numbers, so the function returns an empty array.

Example 3:

Input: [2, 4, 6, 8, 10]
Output: []
Explanation: All even numbers are less than or equal to 10, so the function returns an empty array.

Constraints

• The input array will contain only numbers (integers or floats).
• The input array can have a maximum length of 10,000 elements.
• The refactored function should demonstrate a noticeable reduction in object allocations compared to the original implementation (though precise measurement is not required for this challenge).
• The time complexity of the refactored function should be comparable to or better than the original implementation.

Notes

Consider using techniques like Array.prototype.filter() and Array.prototype.map() judiciously. Think about how you can chain these methods to avoid creating intermediate arrays. While Array.prototype.reduce() could be used, it might not be the most efficient approach for this specific problem. Focus on minimizing the number of times new arrays are created. The original implementation is provided below for reference (but you should not submit it). The goal is to improve upon it.

function processData(data) {
  const evenNumbers = data.filter(num => num % 2 === 0);
  const filteredNumbers = evenNumbers.filter(num => num > 10);
  const squaredNumbers = filteredNumbers.map(num => num * num);
  return squaredNumbers;
}