1. Understanding the Problem

You have:

• An array with 1,000,000 elements (e.g., int[], double[], or String[])
• Sufficient main memory (RAM) to hold the array
• A need to sort efficiently

Since this array easily fits in memory (a million integers take only ~4 MB), you don’t need any external or distributed sorting approach.
All sorting can be done in-memory — which allows for O(n log n) performance and sub-second execution on modern CPUs.

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2. Java Sorting Algorithms Overview

Java provides several efficient in-memory sorting methods inside the java.util.Arrays class.
Let’s discuss the key ones:

A. Arrays.sort()

This is the standard sorting method.

For primitive types (int[], double[], etc.):

• Uses Dual-Pivot Quicksort (developed by Vladimir Yaroslavskiy)
• Sorts in-place (no extra array)
• Very fast on average
• Time complexity: O(n log n)
• Space complexity: O(log n) (recursion stack)

For objects (String[], Integer[], custom objects):

• Uses Timsort
• Stable (maintains relative order of equal elements)
• Works well for partially sorted data
• Time complexity: O(n log n)

Example:

int[] arr = new Random().ints(1_000_000, 0, 1_000_000).toArray();
Arrays.sort(arr);

This will complete in a few hundred milliseconds.

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B. Arrays.parallelSort()

Introduced in Java 8, this method improves performance by utilizing multiple CPU cores.

It internally:

1. Splits the array into subarrays.
2. Sorts them in parallel using multiple threads (via ForkJoinPool).
3. Merges the sorted parts efficiently.

When to use:

• Arrays with more than 100,000 elements.
• Systems with multi-core CPUs (almost all modern machines).
• Data that fits in memory.

Advantages:

• Can be 2x–3x faster than Arrays.sort() on large arrays.
• Automatically uses available cores.

Example:

int[] arr = new Random().ints(1_000_000, 0, 1_000_000).toArray();

long start = System.currentTimeMillis();
Arrays.parallelSort(arr);
long end = System.currentTimeMillis();

System.out.println("Parallel sort time: " + (end - start) + " ms");

On a quad-core CPU, you’ll likely see something around 100–200 ms sorting time.

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3. Comparison: Arrays.sort() vs Arrays.parallelSort()

Feature	Arrays.sort()	Arrays.parallelSort()
Algorithm (Primitives)	Dual-Pivot Quicksort	Parallel Merge Sort
Multithreaded	No (single-threaded)	Yes (uses multiple cores)
Stability	Stable for objects	Stable for objects
Memory Usage	Low	Slightly higher (temporary buffers)
Speed (for large arrays)	Fast	Faster on multi-core CPUs
Recommended For	Up to ~100k elements	1 million+ elements

Conclusion:
For a million elements, Arrays.parallelSort() is the best choice.

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4. Complete Example: Sorting a Million Records

Example with Primitive Array

import java.util.Arrays;
import java.util.Random;

public class MillionRecordSort {
    public static void main(String[] args) {
        // Generate 1 million random integers
        int[] arr = new Random().ints(1_000_000, 0, 1_000_000).toArray();

        // Sort using parallel sort
        long start = System.currentTimeMillis();
        Arrays.parallelSort(arr);
        long end = System.currentTimeMillis();

        System.out.println("Sorting completed in " + (end - start) + " ms");
        
        // Optional: verify sorted order
        boolean sorted = true;
        for (int i = 1; i < arr.length; i++) {
            if (arr[i - 1] > arr[i]) {
                sorted = false;
                break;
            }
        }
        System.out.println("Array sorted: " + sorted);
    }
}

Typical Output:

Sorting completed in 120 ms
Array sorted: true

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Example with Custom Objects

If you have an array of custom objects (e.g., Employee records):

class Employee {
    String name;
    int salary;
    
    Employee(String name, int salary) {
        this.name = name;
        this.salary = salary;
    }
}

public class SortEmployees {
    public static void main(String[] args) {
        Employee[] employees = new Employee[1_000_000];
        Random random = new Random();
        
        for (int i = 0; i < employees.length; i++) {
            employees[i] = new Employee("Emp" + i, random.nextInt(100000));
        }

        long start = System.currentTimeMillis();
        Arrays.parallelSort(employees, (a, b) -> Integer.compare(a.salary, b.salary));
        long end = System.currentTimeMillis();

        System.out.println("Sorting employees completed in " + (end - start) + " ms");
    }
}

This sorts employees by salary using a lambda comparator.
The underlying sort algorithm here is Parallel Timsort — stable and optimized for object arrays.

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5. When to Stick to Arrays.sort()

Although Arrays.parallelSort() is generally faster for large arrays, there are cases where Arrays.sort() may be better:

1. Very small arrays (< 100k elements)
   Overhead of multi-threading can outweigh parallel benefits.
2. Single-core systems
   No parallel benefit available.
3. Limited memory environments
   parallelSort() uses temporary arrays during merging.

In such cases, the regular Arrays.sort() using Dual-Pivot Quicksort performs extremely well.

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6. Practical Tips

1. Avoid writing your own sort algorithm.
   Java’s built-in sorting functions are heavily optimized and tuned over years of testing.
2. Use primitives instead of wrappers (int[] vs Integer[]) when possible.
   Primitives sort faster and consume less memory.
3. Parallel sorting scales with CPU cores.
   On 8-core systems, you’ll see substantial speed improvements.
4. For partially sorted data, object sorting with Timsort (Arrays.sort(Object[])) is especially efficient.
5. Benchmark both Arrays.sort() and Arrays.parallelSort() on your actual dataset.
   Data distribution sometimes affects which performs better.

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7. Summary

Scenario	Recommended Sort	Reason
Array fits in memory (like yours)	Arrays.parallelSort()	Fastest due to multi-threading
Single-threaded environment	Arrays.sort()	Avoids thread overhead
Sorting primitive data (int[], long[])	Dual-Pivot Quicksort (default)	In-place and efficient
Sorting objects with custom comparator	Parallel Timsort	Stable and parallelized
Data is already mostly sorted	Timsort (default for objects)	Detects runs, faster