Sliding Window Interview Questions & Tips for Senior Engineers
Sliding Window Interview Questions & Tips
By Jai Pandya | Last updated: November 22, 2024
The sliding window technique is a powerful and frequently used algorithmic technique in coding interviews. It offers an elegant way to solve a broad array of problems, often related to subarrays or substrings, with maximum efficiency. This article aims to provide a deep dive into this technique, highlighting when it's beneficial, common pitfalls, and how to demonstrate proficiency in interviews.
What is a Sliding Window?
The sliding window technique provides an efficient method for handling subarrays and substrings. It is a specialized subtype in the broader umbrella of Two Pointer problems. This approach involves creating a 'window' that slides over the data to compute a desired result. It uses the result of the previous window position to calculate the result of the position of the window. Sliding Window is potentially one of the most common patterns you'll come across during a coding interview to solve array or string-related questions.
Picture yourself aboard a train, where the view from your window continually changes. You can see a part of the scenery outside, but as the train advances, the portion of the scene you can observe changes. You're not moving within the train; instead, the train (and thus the window) is moving over the landscape.
This is precisely how the sliding window technique works in algorithmic problems. Consider the 'landscape' as your array or list of data. The 'train window' is your 'sliding window', which moves over the data, taking a subset of it into consideration at any given moment. As the window slides over the data, it helps to focus on a specific portion, analyze it, and find particular properties of the elements in that section.
The brilliance of the sliding window technique comes from its strategy of decomposing a larger problem into smaller, manageable sub-problems.
For instance, consider the problem of finding subarray sums of size k for an array. A straightforward interpretation might view it as n separate problems of adding up k elements. However, the sliding window technique reframes this into a single, flowing problem. As the window of size k slides over the array, the sum of the k elements is continually adjusted by adding the new incoming element and subtracting the outgoing one. In this way, a larger problem—is decomposed into a simpler, ongoing task —updating a single sum as the window slides. This approach significantly reduces redundant computations and enhances efficiency.
Companies That Ask Sliding Window Questions
When to Use Sliding Window
The Sliding Window technique is a great fit for problems where you're working with linear data structures like arrays or strings, and you need to find something specific within a subarray or substring. This "something specific" could be a maximum or minimum value, a target sum, or a specific pattern. Essentially, if your problem involves sequentially scanning through the data and the task is focused on contiguous portions of this data, the Sliding Window technique can often provide an efficient solution.
It's important to note here the distinction between subarrays and subsequences. Subarrays and substrings refer to contiguous segments of the original data. For example, in the array [1, 2, 3, 4, 5], [1, 2, 3] and [4, 5] are subarrays—notice how the elements are adjacent to each other. On the other hand, subsequences can contain elements that are not contiguous. In the same array [1, 3, 5] is a subsequence—the elements are not adjacent but they maintain the original order. To further clarify, the Sliding Window technique lends itself well to the "Longest Common Substring" problem, but it is not suitable for the "Longest Common Subsequence" problem due to the non-contiguous nature of subsequences.
How to Use Sliding Window in an Interview
There are two main types of sliding window problems: fixed-size and variable-size sliding windows. The distinction lies in whether the window's size remains constant as it slides or changes based on certain conditions. In this section, we'll see how to work with both types.
Fixed-Size Sliding Window
In a fixed-size sliding window problem, we maintain a window of a fixed-size 'k' that slides through the data structure.
Approach
- First, we compute the desired result (like sum, average, count) for the initial size 'k' window.
- Then, we slide the window one element at a time. For every slide, we adjust our result by adding a new element and removing the last element of the previous window.
- While sliding the window, we keep track of the desired outcome (like maximum sum, longest sequence, and minimum average).
This approach can be seen in many fixed-size sliding window problems.
Example
Given an integer array nums, find a contiguous subarray whose length is 'k' with the maximum/minimum average value. Also, output the maximum/minimum average value.
Solution
We use a fixed-size sliding window to tackle this problem. After initializing variables for window start and window sum, we calculate the sum of the initial window size 'k'. Moving the window by one element at each step, we adjust our window sum by subtracting the outgoing element and adding the incoming element. We continuously calculate the average and track the maximum average found so far.
def find_max_average(nums, k):
window_start, window_sum = 0, 0.0
max_avg = float('-inf')
for window_end in range(len(nums)):
# Add the incoming element to the window sum
window_sum += nums[window_end]
# If we've hit the window size, start sliding
if window_end >= k - 1:
# Calculate average of current window and compare with max_avg
max_avg = max(max_avg, window_sum / k)
# Subtract the outgoing element from window sum
window_sum -= nums[window_start]
# Slide the window
window_start += 1
return max_avg
Variable-Size Sliding Window
In variable-size sliding window problems, the window's size changes based on certain conditions.
Approach
- Start with a window that includes the first element.
- Expand the window until it no longer satisfies the problem's condition.
- Contract the window from the left, continuously checking if it satisfies the condition.
- Repeat expanding and contracting the window while keeping track of the minimum/maximum size or other desired outcomes.
This approach can be generalized for a variety of variable-size sliding window problems.
Example
Longest Substring Without Repeating Characters: Given a string, find the length of the longest substring without repeating characters.
Solution
This is a variable-size sliding window problem. We maintain a dictionary to keep track of the characters and their latest indices in the window. We expand the window from the right, adding the rightmost character to the window. If this character is already in the dictionary (which means it's a repeated character), we slide the window start to the right of the previous occurrence of the character. This way, we ensure the window always contains unique characters. Meanwhile, we continuously calculate and track the maximum length of substrings we've found so far.
def length_of_longest_substring(s):
window_start, max_length = 0, 0
char_index_map = {}
for window_end in range(len(s)):
right_char = s[window_end]
if right_char in char_index_map: # If the character is repeated in the window
# Slide the start of the window to the right of
# the previous occurrence of the right_char
window_start = max(window_start, char_index_map[right_char] + 1)
char_index_map[right_char] = window_end # Store the current index of the character
# Compute the current window size and compare with max_length
max_length = max(max_length, window_end - window_start + 1)
return max_length
Additional Problems
Common Mistakes in Interviews Featuring Sliding Window
Understanding common pitfalls can significantly improve your performance in interviews. Here are some to watch out for when employing the Sliding Window technique:
Off-by-One Errors
Off-by-one errors, a common stumbling block in programming, occur when an element is missed or processed more than necessary due to an incorrect condition in the loop or a wrongly set boundary. In the context of the sliding window technique, this often involves miscalculating the window's start or end indices.
Jumping Directly to the Optimized Solution
A common mistake during interviews is jumping directly to the most optimized solution. While it's important to aim for optimization, it's equally crucial to demonstrate your problem-solving journey to the interviewer.
Overlooking Edge Cases and Not Asking Clarifying Questions
While dealing with sliding window problems, it's essential to consider edge cases that could lead to bugs or incorrect answers.
Not Testing Enough
The insights from understanding edge cases and asking clarifying questions should inform the tests you use to validate your code.
Writing Messy Code
Clear, well-structured code signals your expertise. When applying the sliding window technique, your code should accurately represent the “window”.
Neglecting Complexity
It's crucial to discuss the time and space complexity of your solution.
Overlooking Improvements
Lastly, discuss how your solution could be further optimized or adapted to different problems.
Common Sliding Window Interview Questions
MEDIUM
Data Structures and Algorithms
Fruit into Baskets
HARD
Data Structures and Algorithms
Minimum Window Substring
MEDIUM
Data Structures and Algorithms
Permutation in String
MEDIUM
Data Structures and Algorithms