Netflix·Software Engineer·Technical Phone Screen·Senior

Senior

Apr 2026

Summary

Netflix coding screen focused entirely on building an inverted index from scratch, including phrase search support. The question had more moving parts than I expected and the discussion about position lists caught me a bit flat-footed.

Questions Asked (1)

Given a collection of documents, design and implement an inverted index that supports both single-word search (return document IDs containing the word) and phrase search (return document IDs where a sequence of words appears consecutively and in order). Walk through tokenization, case-folding, your position-list structure, and the complexity of each operation.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

I got the single-word part out pretty fast, map from token to set of doc IDs, easy.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and assumptions, then describe the inverted index design with tokenization, case-folding, and position lists. Explain how single-word search uses the postings list directly, while phrase search intersects postings and checks positional adjacency. Finally, analyze time and space complexity for each operation and discuss trade-offs.

Pro tip: Mention that phrase search can be optimized by processing the least frequent term first to reduce the number of candidate documents, and that storing positions increases index size but enables phrase queries.

1. Clarify Requirements and Assumptions

Ask about document size, query patterns, expected latency, and whether updates are needed. State assumptions about tokenization (e.g., splitting on whitespace and punctuation) and case-folding (lowercasing).

2. Design the Inverted Index Structure

Describe the index as a hash map from terms to postings lists. Each posting contains a document ID and a list of positions where the term occurs. Explain tokenization and case-folding steps.

3. Implement Single-Word Search

For a single-word query, tokenize and case-fold the query, then look up the term in the index and return the document IDs from its postings list. Complexity: O(1) average lookup plus O(k) to return results, where k is the number of matching documents.

4. Implement Phrase Search

Tokenize and case-fold the phrase. Retrieve postings lists for each term. Intersect documents and check if positions are consecutive and in order. Complexity: O(sum of postings list lengths) for intersection, plus O(m * p) for position checks, where m is number of candidate docs and p is phrase length.

5. Analyze Complexity and Trade-offs

Discuss time and space complexity: index size O(total tokens), single-word search O(1) average, phrase search O(total postings for terms). Mention trade-offs: storing positions increases index size but enables phrase queries; skipping positions saves space but loses phrase capability.

Key Points to Mention

Tokenization: splitting on whitespace and punctuation, possibly stemming/lemmatization, and handling special characters.
Case-folding: converting all text to lowercase to ensure case-insensitive matching.
Position-list structure: each posting contains doc ID and list of positions; positions are 0-based or 1-based integers.
Single-word search: direct lookup in inverted index, returning doc IDs from postings list.
Phrase search: intersect postings lists and verify positional adjacency (e.g., position difference equals 1 for consecutive terms).
Complexity: single-word O(1) average lookup; phrase O(total postings for terms) for intersection and O(m * p) for position checks; space O(total tokens).
Optimization: process least frequent term first to reduce candidate set; use skip pointers for faster intersection.

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