Top Sliding Window Problems on LeetCode (Ranked by Difficulty)

If you search "sliding window LeetCode" right now, you'll find 200+ problems tagged with this pattern. That's overwhelming. Which ones should you actually solve? Which teach the core skills vs. obscure edge cases?

I've been there. Early in my interview prep, I randomly picked sliding window problems and wasted hours on variants that taught me nothing new. I wish someone had told me: not all LeetCode problems are created equal.

This guide gives you a ranked, curated list of sliding window problems—ordered by difficulty and learning value. You'll know exactly which problems to solve, in what order, and what skill each one builds.

Here's what you'll get:

• Characteristics of a "good" sliding window problem
• Easy-level problems (1–5): Build foundational skills
• Medium-level problems (6–15): Master variable-size windows and constraints
• Hard-level problems (16+): Tackle multi-constraint and optimization challenges
• A study plan to track progress and avoid traps

Let's turn that intimidating list into a structured training roadmap.

TL;DR — Your Sliding Window Study Roadmap

• Easy problems (1-5): Build template muscle memory, aim for <10 min per problem
• Medium problems (6-15): Master constraints and variations, aim for <25 min
• Hard problems (16+): Learn monotonic deques and multi-constraint handling
• Most important problem: Minimum Window Substring (#76) - the comprehensive test
• Common trap: Jumping to hard problems before mastering easy/medium
• Practice schedule: 1-2 problems daily for 4 weeks, consistency beats volume
• Use the template: Don't freestyle—follow the proven structure

What Makes a Good Sliding Window Problem?

Not every "subarray" problem is worth your time. The best sliding window problems teach you transferable skills—pattern recognition, invariant maintenance, and constraint handling.

Characteristics (Contiguous Elements, Expand/Shrink)

A good sliding window problem has these properties:

1. Contiguous requirement: You're working with subarrays or substrings, not subsequences.
2. Monotonic constraints: Adding or removing an element has a predictable effect on validity.
3. Efficient state updates: You can maintain window state in $O(1)$ or $O(\log K)$ per operation.
4. Clear optimization goal: Find longest, shortest, maximum, minimum, or count.

Red Flags (These aren't sliding window):

• "Find all subsequences..." (that's DP or backtracking)
• "Subarray sum equals K with negative numbers" (use prefix sums)
• "Find pairs/triplets..." (that's two pointers with sorting)
• Learn more in our guide on when NOT to use sliding window

Problem Tags, Time Complexity Clues

On LeetCode, look for these signals:

Tags: "Sliding Window," "Two Pointers," "Hash Table," "String"
Constraints: "Find the longest/shortest subarray/substring where..."
Time Limit: If the naive solution is $O(N^2)$ and $N\le 10^5$, sliding window often reduces it to $O(N)$

Pro Tip: If a problem says "contiguous" and has a monotonic condition (like "at most K distinct characters"), it's almost certainly sliding window.

Easy-Level Problems (1–5)

These problems introduce the core template without gotchas. If you're new to sliding window, start here. Don't skip them—they build muscle memory.

Problem 1: Maximum Average Subarray I (LeetCode 643)

Problem: Given an array and integer k, find the maximum average of any subarray of length k.

What You Learn:

• Fixed-size sliding window basics
• How to slide efficiently (add one, remove one)
• Avoiding repeated sum calculations

Key Insight: This is the "Hello World" of sliding window. If you can't solve this in 5 minutes, review the fixed vs dynamic window guide first.

Template Fit: Fixed-size window (k is given) - use the template

def findMaxAverage(nums: List[int], k: int) -> float:
    window_sum = sum(nums[:k])
    max_sum = window_sum
    
    for i in range(k, len(nums)):
        window_sum += nums[i] - nums[i - k]
        max_sum = max(max_sum, window_sum)
    
    return max_sum / k

Problem 2: Longest Substring Without Repeating Characters (LeetCode 3)

Problem: Find the length of the longest substring without repeating characters.

What You Learn:

• Variable-size window with a Set
• When to shrink (duplicate detected)
• Tracking maximum window size

Key Insight: This problem teaches you the core variable-size pattern. The shrink condition is simple: "while duplicate exists." See detailed walkthrough in sliding window intuition guide.

Common Mistake: Not removing characters from the set when shrinking. Avoid this with our common mistakes guide.

Problem 3: Max Consecutive Ones III (LeetCode 1004)

Problem: Given a binary array and integer k, find the maximum number of consecutive 1's if you can flip at most k zeros.

What You Learn:

• Reframing: "at most K flips" = "at most K zeros in the window"
• Counting-based validity check
• Variable-size window with a counter

Key Insight: Many sliding window problems are disguised. This looks like a "flip" problem, but it's really "longest subarray with at most K zeros."

Problem 4: Minimum Size Subarray Sum (LeetCode 209)

Problem: Find the minimum length of a subarray whose sum is at least target.

What You Learn:

• Minimization (instead of maximization)
• Shrinking aggressively to find the smallest valid window
• Handling "no valid answer" cases

Key Insight: The shrink loop should be while (sum >= target) , not while (sum > target) . This catches the exact boundary.

Problem 5: Longest Repeating Character Replacement (LeetCode 424)

Problem: Find the length of the longest substring containing the same letter you can get after changing at most k characters.

What You Learn:

• Tracking the most frequent character
• Calculating "cost" of making the window valid
• The formula: window_size - max_freq \leq k

Key Insight: You don't need to track all character frequencies perfectly—just the max frequency. This is a classic trick to avoid $O(26)$ scans.

Medium-Level Problems (6–15)

Once you've internalized the template with the easy problems, these medium problems add complexity: multiple constraints, advanced data structures, or tricky shrink logic.

Problem 6: Permutation in String (LeetCode 567)

What You Learn: Fixed-size window with character frequency matching.

Key Technique: Use a Map to track differences between target and current window. Shrink complexity by tracking a "match count" instead of comparing maps every iteration.

Problem 7: Find All Anagrams in a String (LeetCode 438)

What You Learn: Similar to #6, but you collect all valid windows, not just check existence.

Key Technique: When the window is valid, record left index.

Problem 8: Longest Substring with At Most K Distinct Characters (LeetCode 340, Premium)

What You Learn: Using a Map to count distinct elements. Shrink when map.size() > k.

Key Technique: This is the canonical variable-size + hash map problem. If you master this, you've mastered 80% of sliding window.

Problem 9: Subarrays with K Different Integers (LeetCode 992)

What You Learn: "Exactly K" is hard. Use the trick: exactlyK(k) = atMostK(k) - atMostK(k - 1) .

Key Technique: Write a helper function atMostK and call it twice.

Problem 10: Minimum Window Substring (LeetCode 76)

What You Learn: This is the final boss of medium sliding window. You need:

• A Map to track required characters
• A counter to know when the window is valid
• Minimization logic (shrink greedily)

Key Technique: Maintain need (required char counts) and have (current matches). Expand until have == need, then shrink to minimize.

def minWindow(s: str, t: str) -> str:
    if not t or not s:
        return ""
    
    need = Counter(t)
    have = {}
    required = len(need)
    formed = 0
    
    left = 0
    min_len = float('inf')
    min_window = (0, 0)
    
    for right in range(len(s)):
        char = s[right]
        have[char] = have.get(char, 0) + 1
        
        if char in need and have[char] == need[char]:
            formed += 1
        
        while formed == required:
            if right - left + 1 < min_len:
                min_len = right - left + 1
                min_window = (left, right)
            
            char = s[left]
            have[char] -= 1
            if char in need and have[char] < need[char]:
                formed -= 1
            left += 1
    
    l, r = min_window
    return s[l:r+1] if min_len != float('inf') else ""

Problems 11–15: Additional Medium Challenges

11. Fruit Into Baskets (LeetCode 904): At most 2 distinct types (disguised "K distinct")
12. Longest Turbulent Subarray (LeetCode 978): State machine + sliding window
13. Grumpy Bookstore Owner (LeetCode 1052): Fixed window with conditional counting
14. Count Number of Nice Subarrays (LeetCode 1248): Transform to "at most K" trick
15. Replace the Substring for Balanced String (LeetCode 1234): Inverse logic (valid window = outside is balanced)

Hard-Level Problems (16+)

These problems combine sliding window with other patterns or have subtle correctness traps. Don't attempt these until you can solve mediums in under 20 minutes.

Problem 16: Sliding Window Maximum (LeetCode 239)

What You Learn: When simple state updates aren't $O(1)$, use a monotonic deque.

Key Technique: Maintain a deque of indices in decreasing order of values. This lets you find the max in $O(1)$ even after removal.

Why It's Hard: The window logic is easy, but implementing the deque correctly is tricky.

Problem 17: Substring with Concatenation of All Words (LeetCode 30)

What You Learn: Sliding window on word boundaries, not characters.

Key Technique: Iterate over starting positions 0 to word_len - 1, then slide in word_len increments.

Problem 18: Longest Substring with At Most Two Distinct Characters (LeetCode 159, Premium)

What You Learn: Variant of "K distinct" with K = 2. Good for testing your generalized solution.

Problem 19: Sliding Window Median (LeetCode 480)

What You Learn: Maintaining median in a sliding window requires two heaps (max-heap and min-heap).

Why It's Hard: Balancing heaps after removal is non-trivial.

Problem 20: Minimum Number of K Consecutive Bit Flips (LeetCode 995)

What You Learn: This is a greedy + sliding window hybrid. Track flip state efficiently.

Key Technique: Use a counter to track cumulative flips instead of flipping the array.

How to Track Progress & Avoid Traps

Tagging Techniques, Tracking Practice

Use a Spreadsheet or Tool:
Track each problem with:

• Problem name and number
• Difficulty (Easy/Medium/Hard)
• First attempt (Success/Struggle/Failed)
• Key takeaway (one sentence)

Tag by Subpattern:

• Fixed-size window
• Variable-size with hash map
• Variable-size with counter
• Monotonic deque variant
• "At most K" trick

This helps you see patterns: "I struggle with 'at most K' problems—let me drill those."

LeetCopilot Tip: Use Study Mode to automatically track your attempts, highlight weak areas, and generate personalized review plans. It's like a spreadsheet that updates itself.

Avoid the Trap of Premature Optimization

The Trap: Jumping to hard problems too early because they "look cooler."

The Fix: Solve 3–5 easy problems first. Then 8–10 mediums. Only then touch hards. Mastery comes from repetition, not novelty.

Red Flag: If you can't explain why your sliding window shrinks, you're not ready for the next tier.

Takeaways & Study Plan

Week 1: Easy Problems (Foundation)

• Solve all 5 easy problems listed above
• Practice writing the template from memory
• Goal: Under 10 minutes per problem

Week 2: Medium Problems (Core Skills)

• Solve problems 6–10
• Focus on "Minimum Window Substring" (it's a rite of passage)
• Goal: Under 25 minutes per problem

Week 3: Medium Problems (Variations)

• Solve problems 11–15
• Identify which are "disguised" easy problems
• Goal: Recognize patterns instantly

Week 4: Hard Problems (Interview Edge)

• Attempt 3–5 hard problems
• Focus on monotonic deque (239) and multi-constraint (30)
• Goal: Understand the solution even if you don't code it perfectly

Daily Practice: 1–2 problems per day. Consistency beats volume.

FAQ

Q: Should I solve all 200+ sliding window problems on LeetCode?
A: No. Solve the 20–30 problems that teach unique skills. After that, you're just repeating patterns.

Q: How do I know when I've "mastered" sliding window?
A: You can: (1) identify the pattern in under 30 seconds, (2) write the template without syntax errors, (3) explain your shrink condition clearly.

Q: What if I get stuck on a problem for more than 1 hour?
A: Read the solution, understand it, then solve it again from scratch the next day. Struggling for 3 hours doesn't build skill—it builds frustration.

Q: Are premium problems worth it?
A: For sliding window, most premium problems are variants of free ones. If you already have premium, solve them. If not, don't buy it just for this pattern.

Q: Which problem is the most important?
A: Minimum Window Substring (LeetCode 76). It's the comprehensive test of all sliding window skills. If you can solve it confidently, you're ready for interviews.

Q: Should I memorize solutions?
A: No. Memorize the template structure, not specific solutions. The goal is to adapt the template to new constraints.