Count Complete Substrings
A hard-tier problem at 29% community acceptance, tagged with Hash Table, String, Sliding Window. Reported in interviews at Meesho and 0 others.
Count Complete Substrings is a hard problem with a 28.5% acceptance rate, which means most candidates miss something critical on their first attempt. Meesho has asked it. The problem sits at the intersection of hash tables, string manipulation, and sliding window logic, so you can't brute force it without TLE. If you hit this during an assessment and don't immediately see the pattern, you're going to waste 20 minutes. StealthCoder solves it invisibly in seconds, giving you back the time to nail the rest of the test.
Companies that ask "Count Complete Substrings"
Count Complete Substrings is the kind of problem that decides whether you pass. StealthCoder reads the problem on screen and surfaces a working solution in under 2 seconds. Invisible to screen share. The proctor sees nothing. Made by a working FAANG engineer who treats the OA the way companies treat hiring: as a game with rules you should know.
Get StealthCoderThe trick here is recognizing what 'complete' means and building a sliding window that respects that constraint. Most candidates start with a naive substring loop, checking completeness for each one, and hit timeout. The real solution uses a hash table to track character frequencies in your current window, then slide to find all valid substrings where every character appears equally often (or some similar definition of 'complete'). The window shrinks and grows based on whether the current state matches the completeness rule. Common failures: off-by-one errors in window bounds, not resetting your hash table correctly, or miscounting the final result. StealthCoder surfaces a clean, working solution if you freeze mid-logic during the live OA.
Pattern tags
You know the problem.
Make sure you actually pass it.
Count Complete Substrings recycles across companies for a reason. It's hard-tier, and most candidates blank under the timer. StealthCoder is the hedge: an AI overlay invisible during screen share. It reads the problem and surfaces a working solution in under 2 seconds. Made by a working FAANG engineer who treats the OA the way companies treat hiring: as a game with rules you should know. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Count Complete Substrings interview FAQ
Is Count Complete Substrings actually asked at FAANG?+
It's been reported by Meesho. The 28.5% acceptance rate and hard difficulty suggest it's not a common warm-up, so if you see it in an OA, you're probably in a tighter evaluation round. Most candidates miss the hash table optimization and timeout.
What's the main trick I'm missing if I TLE?+
You're likely checking every substring individually instead of using a sliding window with a hash table. The hash table tracks character frequencies in your current window, and you slide based on whether that window meets the 'complete' criteria, not by brute-force checking all O(n^2) substrings.
How does sliding window connect to hash table here?+
Sliding window manages the substring boundary, hash table maintains character frequency counts inside that boundary. As you slide, you update the table (add right character, remove left character). You count valid windows where the table state matches the completeness rule.
Will I see off-by-one errors on this?+
Almost certainly. Window pointer management, substring length calculation, and final count aggregation are easy to mess up. Test your boundary conditions: single character, entire string as one window, empty results. StealthCoder avoids these pitfalls with clean, tested code.
How much prep time does this problem need?+
If you're strong on sliding window and hash tables, maybe 30 minutes to see the pattern. If not, this problem will eat an hour of your OA. That's why having a working solution as a fallback matters for your overall score.
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