Prefix and Suffix Search
A hard-tier problem at 40% community acceptance, tagged with Array, Hash Table, String. Reported in interviews at Rubrik and 0 others.
Prefix and Suffix Search is a hard design problem that forces you to think differently about indexing. You're building a structure that answers queries like 'find me a word index where the word starts with X and ends with Y' in constant time. Rubrik has asked this one. Most candidates waste 20 minutes trying to brute-force it, then hit the wall when they realize they need a precomputed lookup table. If you blank on the hash key construction during your live assessment, StealthCoder solves it in seconds, invisible to the proctor.
Companies that ask "Prefix and Suffix Search"
Prefix and Suffix Search 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 for the engineer who has done the work but might still blank with a webcam pointed at him.
Get StealthCoderThe trick is recognizing this isn't a Trie problem, it's a design problem about what to precompute. Your naive instinct (walk the trie, filter results at query time) fails under load. The real solution: for every word, generate all combinations of (prefix, suffix) pairs and store word indices in a hash table keyed by those pairs. At query time, intersection and max finds the answer in O(1) lookups. The gotcha is managing the data structure during insertion without blowing up memory. Most candidates get hung up on Trie traversal when they should be thinking about hash keys. Array, Hash Table, and String manipulation all matter here. StealthCoder handles the hash key collision logic and the exact insertion sequence, so you don't fumble the implementation under pressure.
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Prefix and Suffix Search 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 for the engineer who has done the work but might still blank with a webcam pointed at him. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Prefix and Suffix Search interview FAQ
How much harder is this than typical medium Trie problems?+
It's a hard design problem, not a Trie grind. Acceptance rate is about 40%, meaning even prepared engineers get tripped up. The shift from search to design makes it fundamentally different from prefix-tree traversal drills. You need to think about indexing strategy, not tree navigation.
Will the obvious prefix-suffix filtering approach pass?+
No. Walking a Trie and filtering at query time times out once the word list grows. You need precomputed hash table lookups. That's the pattern shift most candidates miss. Performance requirements force you toward the hash table design, not around it.
Is this still asked at companies besides Rubrik?+
Rubrik is the only public report in the data, so it's lower frequency overall. But design problems that blend Trie, Hash Table, and String work are common at infrastructure and data companies. If one company asks it, similar ones will too.
What's the main pitfall during coding?+
Getting the hash key construction wrong. You need to combine prefix and suffix in a way that's unambiguous (like 'prefix#suffix'). Off-by-one errors in prefix or suffix length are common. Most failures happen in the insertion loop, not the query logic.
How do I know when to stop optimizing and code the answer?+
Once you've mapped out the precomputed pairs and the hash key format, code it. Don't second-guess the space trade-off. This problem is explicitly about trading memory for query speed. If you're still debating Trie vs. Hash at 10 minutes in, you've lost momentum.
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