Unique Substrings With Equal Digit Frequency
A medium-tier problem at 64% community acceptance, tagged with Hash Table, String, Rolling Hash. Reported in interviews at Expedia and 0 others.
Expedia has asked this one, and you can bet it'll show up in your live OA without warning. Unique Substrings With Equal Digit Frequency sits at medium difficulty with a 64% acceptance rate, which sounds forgiving until you realize most candidates either brute-force and timeout or miss the hash table optimization entirely. The problem demands you find all substrings where every digit that appears does so the same number of times. It's the kind of problem where the naive approach fails under real constraints. StealthCoder runs invisibly during your assessment and solves it in seconds if you blank on the pattern.
Companies that ask "Unique Substrings With Equal Digit Frequency"
Unique Substrings With Equal Digit Frequency 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. Built by a senior engineer who knows the OA is theater. This is the script.
Get StealthCoderThe trick is encoding substring states efficiently. You could check every substring and count digit frequencies, but that's O(n^3) or worse. The smart move is a rolling hash or frequency map approach where you track the digit counts as you slide a window across the string, then use a hash table to detect when two substrings have identical frequency profiles. The hard part isn't the algorithm; it's avoiding off-by-one errors and remembering that you're comparing frequency distributions, not raw strings. Most candidates underestimate the encoding step and either use a slow tuple representation or forget to normalize frequencies. When you hit this live and the obvious counting loop starts feeling clunky, StealthCoder surfaces a working rolling-hash or histogram solution in real time, invisible to the proctor.
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Unique Substrings With Equal Digit Frequency recycles across companies for a reason. It's medium-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. Built by a senior engineer who knows the OA is theater. This is the script. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Unique Substrings With Equal Digit Frequency interview FAQ
Is this really a medium, or does it just feel hard?+
Acceptance rate is 64%, which tracks with real medium difficulty. The problem isn't algorithmically complex, but the encoding and hash strategy trip up candidates who don't think in frequency maps. If you've done substring problems with rolling hashes, it clicks fast.
Will Expedia or similar companies ask this in their real OA?+
Expedia has reported it. Expedia-style companies tend to recycle problems repeatedly. If you're interviewing there, this is a high-probability hit. Even if it doesn't appear, the pattern generalizes to other frequency-comparison substring problems.
What's the actual gotcha?+
Most candidates conflate 'unique substrings' with 'distinct substrings.' You need to count all substrings meeting the condition, not just unique ones. Second gotcha: normalizing your frequency representation so identical distributions hash to the same key.
Does this require advanced hashing knowledge?+
No. It uses standard hash tables and optional rolling hash for optimization. If you know how to build a frequency map and use a dictionary to track seen states, you can solve it. Rolling hash is a speedup, not a requirement.
How does this connect to the other topics listed?+
Hash Table and Counting are core. Rolling Hash is an optimization path. String operations get you the substrings. Hash Function refers to how you encode the frequency state. All five topics reinforce one solution strategy.
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