Shortest Uncommon Substring in an Array

Shortest Uncommon Substring in an Array

What is this problem about?

The Shortest Uncommon Substring in an Array interview question gives you an array of strings. For each string, find the shortest substring that does NOT appear in any other string in the array. If no such substring exists for a string, return an empty string for it. This tests Trie-based substring frequency counting or hash map enumeration of all substrings.

Why is this asked in interviews?

Affirm, Airbnb, and Amazon ask this problem because it models uniqueness detection across a corpus — a core concept in search engine indexing (finding unique identifiers), DNA sequencing (finding unique primer sequences), and de-duplicating features in machine learning pipelines. It tests the ability to enumerate all substrings efficiently and cross-reference their occurrence across multiple strings.

Algorithmic pattern used

The pattern is hash map substring frequency with length-first search. For all strings in the array, enumerate every possible substring and track which strings contain it (or simply count occurrences globally). For each string s, iterate over substring lengths from 1 to len(s). For each length, check each starting position. If the substring appears only in s (not in any other string), it's a valid answer — return it (shortest first guarantees the minimum length answer). Use a global frequency map built from all strings' substrings for O(1) lookup.

Example explanation

words = ["apple", "apply", "ape"].

All substrings of "apple": a, p, p, l, e, ap, pp, pl, le, ... "apple", "apple". Substrings that appear only in "apple": "ppl", "pple", "apple"... Check "pp": also in "apply"? No → "pp" appears only in "apple". Length 2. But "l" appears only in "apple" and "apply"... "le" only in "apple"! Length 2.

Shortest for "apple": "le" (length 2, appears only in "apple").

Common mistakes candidates make

• Not checking ALL strings — a substring must not appear in ANY other string, not just the adjacent ones.
• Building the frequency map only for exact string matches — must enumerate all SUBSTRINGS of each string.
• Not sorting by length before returning — return the shortest unique substring, not the first found.
• Memory overflow from storing all substrings of long strings — optimize by checking short substrings first and stopping early.

Interview preparation tip

For the Shortest Uncommon Substring in an Array coding problem, the hash table trie string interview pattern applies. The Trie approach is space-efficient: insert all substrings of all strings into a Trie, tracking which strings contributed each substring. For each string's Trie path, the shortest node with exactly one contributing string is the answer. Airbnb and Amazon interviewers appreciate the Trie approach for its structural elegance. Practice building substring-frequency maps and Tries from arrays of strings — these patterns appear in competitive string search problems throughout technical interviews.