Introduction

• Sliding Window is a technique used to solve problems on arrays or strings.
• It is a form of the Two Pointers Technique.
• We create a window (a small part of the array/string) and move it step-by-step.
• Image
• This window can be:
  • Fixed Size: The window size remains constant.
  • Variable Size: The window size can expand or contract based on conditions.
• It helps reduce time complexity from O(n²) to O(n) in many problems.

• Sliding Window Technique is also used in real-world projects.
  • For example, if you want to calculate how many users visited your website in the last 3 days or the last 24 hours, you can keep a moving window of data and update the count as the window shifts. This helps you get real-time analytics without recalculating everything again.
  • Below is the program for this example.

Programs:-

• Program 1: Calculate the number of user visits in the last 3 days.

  public class MainApp1
  {
      public static void main(String[] args)
      {
          int[] users = {100,80,40,70,120,90,60};
          int days = 3;
  
          int total=0;
          for(int i=0; i<days; i++)
          {
              total = total + users[i];
          }
  
          System.out.println("Total number of users visited in last 3 days");
          System.out.println(total);
  
          for(int i=1; i<=users.length-days; i++)
          {
              total = total - users[i-1] + users[i+days-1];
              System.out.println(total);
          }
      }
  }

  Output:

  Total number of users visited in last 3 days
  220
  190
  230
  280
  270

• Program 2: Maximum sum of a subarray of size k.

  public class MainApp2
  {
      public static void main(String[] args)
      {
          int[] arr = {100,80,40,70,120,90,60};
          int size=3;
  
          int total=0;
          for(int i=0; i<size; i++)
          {
              total = total + arr[i];
          }
  
          int max=total;
          for(int i=1; i<=arr.length-size; i++)
          {
              total = total - arr[i-1] + arr[i+size-1];
              max = Math.max(total, max);
          }
  
          System.out.println("Maximum Sum Subarray : "+max);
      }
  }

  Output:

  Maximum Sum Subarray : 280

Points to Note:

• Sliding window works only when:
  • Window size is fixed OR variable but valid
  • Elements are added/removed from window
  • Mostly used for subarrays with constraints (length, sum ≤ K, etc.)

Sliding Window Technique Task:

Try to solve the following problems using Sliding Window Technique:

1. WAP to Find the Sliding Window Maximum
2. WAP to Find the Subarrays with K Different Integers