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Design LRU cache and pick k closest points

Last updated: Mar 29, 2026

Quick Overview

This question evaluates understanding of data structures and algorithmic techniques for designing an in-memory LRU cache (eviction policies, recency maintenance, and complexity analysis) and for selecting k closest points using sorting and selection/heap-based approaches.

  • Medium
  • Meta
  • Coding & Algorithms
  • Software Engineer

Design LRU cache and pick k closest points

Company: Meta

Role: Software Engineer

Category: Coding & Algorithms

Difficulty: Medium

Interview Round: Technical Screen

1) Design a fixed-capacity in-memory cache that evicts the least recently used key when full. Support get(key) and put(key, value) in O( 1) average time. Describe the data structures you would use, how recency is updated on get/put, how evictions occur, and analyze time and space complexity. 2) Given an array of 2D points and an integer k, return the k points closest to the origin by Euclidean distance. First outline a straightforward approach using sorting. Then discuss improvements using Quickselect and a max-heap, including their time and space complexity and when you would choose each approach.

Quick Answer: This question evaluates understanding of data structures and algorithmic techniques for designing an in-memory LRU cache (eviction policies, recency maintenance, and complexity analysis) and for selecting k closest points using sorting and selection/heap-based approaches.

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Meta
Jul 29, 2025, 12:00 AM
Software Engineer
Technical Screen
Coding & Algorithms
2
0
  1. Design a fixed-capacity in-memory cache that evicts the least recently used key when full. Support get(key) and put(key, value) in O(
  2. average time. Describe the data structures you would use, how recency is updated on get/put, how evictions occur, and analyze time and space complexity.
  3. Given an array of 2D points and an integer k, return the k points closest to the origin by Euclidean distance. First outline a straightforward approach using sorting. Then discuss improvements using Quickselect and a max-heap, including their time and space complexity and when you would choose each approach.

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