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Implement autocomplete with top-K suggestions

Last updated: Mar 29, 2026

Quick Overview

This question evaluates knowledge of data structures and algorithms for real-time autocomplete systems, including prefix indexing, frequency-based ranking and considerations for case-insensitivity and Unicode, as well as update, deletion, and streaming semantics.

  • medium
  • Amazon
  • Coding & Algorithms
  • Software Engineer

Implement autocomplete with top-K suggestions

Company: Amazon

Role: Software Engineer

Category: Coding & Algorithms

Difficulty: medium

Interview Round: Onsite

Implement an autocomplete component that, given a history of query strings with frequencies, returns the top K suggestions for an input prefix in real time. Support updates when a query is entered again (frequency increases) and insertion of new queries. Specify tie-breaking (e.g., higher frequency, then lexicographic), case-insensitivity and Unicode handling, and limits on K. Describe and implement the core data structures (e.g., trie with per-node top-K min-heaps or balanced trees), and analyze time/space complexity for insert, update, and query. Discuss how to handle deletions, streaming updates, and large-scale memory usage.

Quick Answer: This question evaluates knowledge of data structures and algorithms for real-time autocomplete systems, including prefix indexing, frequency-based ranking and considerations for case-insensitivity and Unicode, as well as update, deletion, and streaming semantics.

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|Home/Coding & Algorithms/Amazon

Implement autocomplete with top-K suggestions

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Amazon
Sep 6, 2025, 12:00 AM
mediumSoftware EngineerOnsiteCoding & Algorithms
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0

Implement an autocomplete component that, given a history of query strings with frequencies, returns the top K suggestions for an input prefix in real time. Support updates when a query is entered again (frequency increases) and insertion of new queries. Specify tie-breaking (e.g., higher frequency, then lexicographic), case-insensitivity and Unicode handling, and limits on K. Describe and implement the core data structures (e.g., trie with per-node top-K min-heaps or balanced trees), and analyze time/space complexity for insert, update, and query. Discuss how to handle deletions, streaming updates, and large-scale memory usage.

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