What is the sliding window technique?

The sliding window technique is an algorithmic pattern that reduces nested loop complexity by maintaining a contiguous subarray or substring of variable or fixed size. Instead of recomputing the window from scratch at each step, you slide the window forward by adding one element on the right and removing one on the left. This converts many O(n^2) brute force solutions into O(n) solutions.

What are the two main types of sliding window problems?

The two main types are fixed-size windows (the window length k is given and constant) and variable-size windows (the window expands and contracts based on a constraint, such as sum less than or equal to a target, or no more than k distinct characters). Fixed-size windows are generally simpler. Variable-size windows require careful shrink logic and are more commonly asked at senior levels.

What data structures are commonly used inside a sliding window?

A hash map (for character or element frequency counts), a monotonic deque (for range maximum or minimum problems), a set (for uniqueness tracking), and simple integer counters are the most common. The choice of inner data structure distinguishes easy from hard sliding window problems. LeakCode reports show that hash map plus two-pointer combinations appear most frequently in FAANG interviews.

Which companies ask the most sliding window questions?

Based on real interview reports in LeakCode's database, Google, Meta, Amazon, and Microsoft ask sliding window questions most frequently. Google tends to combine sliding window with string manipulation. Amazon often embeds sliding window inside larger system-context problems during its coding rounds. Meta asks classic substring and subarray variants heavily at the E3 and E4 levels.

How does LeakCode help me prepare for sliding window interviews?

LeakCode aggregates 51,000+ real interview questions from sources like 1Point3Acres, Blind, LeetCode Discuss, and Glassdoor. You can filter by company to see which specific sliding window patterns each company emphasizes. This means you study the actual distribution of patterns your target company asks, not a generic problem list.

Sliding Window Interview Questions: Complete Guide (2026)

Which sliding window patterns FAANG companies actually ask, how to recognize when to apply the technique, and what the data from LeakCode's 51,000+ reports shows about frequency by company.

What sliding window interviews test

Sliding window problems test whether you can recognize linear-time opportunities hidden inside what looks like a quadratic problem. The pattern requires you to think in terms of maintaining invariants over a moving range, which directly tests loop reasoning, edge case handling, and data structure selection under time pressure.

Across LeakCode's database of 51,000+ real interview reports, sliding window questions appear at every level from phone screen to onsite. They are popular precisely because they have adjustable difficulty: a fixed-size window problem can be answered in minutes by a strong candidate, while a variable-size window problem with multiple constraints can separate L5 from L4 candidates.

Interviewers also use sliding window problems to evaluate communication quality. The technique requires candidates to verbalize the invariant they are maintaining, which reveals systematic thinking. Candidates who say "I will keep a window where the constraint holds and shrink from the left when it breaks" score higher than those who code silently.

The main sliding window question categories

Fixed-size window aggregation

Problems where k is fixed and you compute a running maximum, minimum, sum, or average as the window slides. The classic form is max sum subarray of size k. Typically resolved with a deque for range max/min and simple arithmetic for sum. These appear most often in phone screens and early onsite rounds as warmup problems.

Variable-size window with a sum or count constraint

Problems where you find the smallest or largest subarray satisfying a numeric constraint such as sum at least k or product less than k. Solved with a two-pointer expand-then-shrink loop. The shrink condition and the answer update order are the common sources of off-by-one errors.

Substring problems with character frequency maps

Problems involving substrings with at most k distinct characters, permutation matching, or anagram detection. Require a hash map inside the window to track character counts. The number of distinct characters or the count of characters that satisfy the constraint is the invariant. Heavily asked at Google and Meta in string-focused rounds.

Longest or shortest substring with specific properties

Finding the longest substring without repeating characters, the longest substring with at most two distinct characters, or the minimum window containing all characters of a target. These are the most frequently reported sliding window variants in LeakCode's data for Meta and Amazon onsite rounds.

Sliding window combined with binary search or sorting

Harder problems where the window interacts with sorted structures or requires binary search to find optimal window size. These appear in hard-difficulty rounds and test whether candidates can compose techniques rather than apply them in isolation.

How to approach any sliding window problem

Before writing code, identify three things: (1) what defines a valid window (the constraint), (2) what you are optimizing over the window (max, min, count, existence), and (3) whether the window size is fixed or variable. Stating these aloud establishes the invariant and guides every subsequent decision.

1.Set up two pointers. Left and right start at index 0. Right expands the window on each outer loop iteration. Left contracts only when the window violates the constraint.
2.Maintain a state variable. A hash map, a counter, a running sum, or a deque. Update it as right expands, then revert it as left contracts.
3.Determine when to update the answer. For maximum window problems, update every time the window is valid. For minimum window problems, update only after shrinking as far as possible.
4.Check boundary conditions. Empty string, all-same-character string, window equal to array length. These edge cases appear in every interviewer rubric.

Companies that frequently ask sliding window questions

Browse real sliding window interview questions from these companies on LeakCode:

Google Meta Amazon Microsoft Apple Uber LinkedIn Adobe

Search Sliding Window on LeakCode

LeakCode has 51,000+ real interview questions from 2,000+ companies. Filter by company to see exactly which sliding window patterns your target company has asked recently.

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How LeakCode helps with sliding window prep

LeakCode indexes real questions reported by candidates across 1Point3Acres, Blind, LeetCode Discuss, Glassdoor, and Reddit. Every question is tagged by topic and round type, so you can isolate sliding window questions from a specific company's coding rounds rather than browsing a generic list.

The difference matters because Google leans toward harder substring variants while Amazon frequently embeds sliding window inside multi-step problems. LeakCode's data makes that distribution visible.

Fixed window vs variable window

Fixed-size sliding window: maintain a window of exactly k elements; on each step, add the new element on the right and drop the leftmost. Used for problems like "maximum sum of any k consecutive elements" or "average of every k elements." The implementation is mechanical and runs in O(n) time with O(1) extra space.

Variable-size sliding window: maintain a window that grows or shrinks based on a constraint. Right pointer expands the window by adding elements; left pointer contracts the window when the constraint is violated. Used for problems like "longest substring without repeating characters," "minimum window substring," and "longest repeating character replacement." The key invariant: after each iteration, the window between left and right is a valid candidate, and you track the best valid candidate seen.

Sliding window with a frequency map

Many sliding window problems require a frequency map (dictionary or array of size 26 for letters). The map tracks how many times each character appears in the current window. When the window constraint involves character counts (e.g., "longest substring with at most k distinct characters" or "minimum window containing all chars of T"), the frequency map is your primary data structure.

The most common bug under pressure: forgetting to decrement the frequency map when the left pointer advances. Strong candidates write the decrement before the left pointer increment, in the same code block, so the operations always pair correctly. Reports on LeakCode show this single discipline catches a large fraction of sliding-window bugs at L4 rounds.