← Instacart Interview Insights

Instacart·Software Engineer·Technical Phone Screen·Senior

Senior
Jul 2026

Summary

Instacart software engineer interview with a meaty data structures problem that had me thinking about tries for way longer than I expected. The complexity analysis portion caught me a bit flat-footed.

Questions Asked (1)

Q1

Design a data structure with an add(name) method and a find(prefix, suffix) method that returns all stored file names matching both a given prefix and suffix, in lexicographic order. Discuss time and space complexity and compare approaches like dual tries, suffix automata, and inverted indexes. Assume up to 100,000 insertions and frequent queries.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs
Author's notes

I went straight for the two-trie approach: one trie for prefixes, one for reversed strings to handle suffixes, then intersect the result sets.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then propose a dual trie approach (prefix trie and suffix trie) to efficiently find candidate sets for prefix and suffix, intersect them, and sort the results. Discuss time and space complexity, and compare with alternatives like suffix automata and inverted indexes, justifying your choice based on the given constraints.

Pro tip: Mention that you can optimize by storing file names in a sorted structure or using a trie with sorted children to avoid explicit sorting after intersection, and discuss trade-offs between preprocessing time and query time.

1. Clarify Requirements and Constraints

Ask about expected query patterns, whether updates are frequent, memory limits, and if lexicographic order is strictly required. Confirm that names are unique and can be arbitrary strings.

2. Propose a Dual Trie Approach

Build a prefix trie and a suffix trie (or a generalized suffix tree) storing file names. For a query, traverse the prefix trie to get all names with the prefix, traverse the suffix trie to get all names with the suffix, then intersect the two sets.

3. Analyze Time and Space Complexity

Insertion: O(L) per name, where L is average length. Query: O(P + S + K) where P and S are prefix/suffix lengths and K is the number of matches, plus sorting if needed. Space: O(N*L) for tries. Discuss optimizations like storing sorted lists at nodes.

4. Compare Alternative Approaches

Discuss suffix automata (good for substring queries but not prefix+suffix), inverted indexes (good for keyword search but not arbitrary prefix/suffix), and hybrid approaches. Highlight trade-offs in preprocessing, query time, and memory.

5. Conclude with a Recommendation

Recommend the dual trie approach for its balance of simplicity and efficiency given up to 100,000 insertions and frequent queries. Mention possible optimizations like caching frequent queries or using a trie with sorted children to return results in order without extra sorting.

Key Points to Mention

  • Time complexity of insertion and query for dual trie: O(L) insertion, O(P + S + K) query, where K is number of matches.
  • Space complexity: O(N*L) for tries, but can be reduced with compression (radix tree) or by storing only necessary nodes.
  • Intersection of candidate sets: use hash sets for O(min(|A|,|B|)) intersection, then sort results if needed.
  • Lexicographic order: can be achieved by storing file names in sorted order at trie nodes or by using a trie with sorted children, avoiding post-query sorting.
  • Comparison with suffix automata: suffix automata excel at substring queries but are overkill for prefix+suffix and have higher space overhead.
  • Inverted indexes: efficient for keyword-based search but not suitable for arbitrary prefix/suffix matching unless combined with other structures.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.