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Implement and compare round-robin and consistent hashing

Last updated: Mar 29, 2026

Quick Overview

This question evaluates a candidate's ability to implement and reason about load balancing algorithms (round-robin and consistent hashing), covering competencies in data structures, hashing, concurrency, correctness, and unit testing.

  • Medium
  • DoorDash
  • Coding & Algorithms
  • Software Engineer

Implement and compare round-robin and consistent hashing

Company: DoorDash

Role: Software Engineer

Category: Coding & Algorithms

Difficulty: Medium

Interview Round: Onsite

Implement a round-robin load balancer that selects among service nodes, skipping unavailable nodes and wrapping around correctly. Fix typical pitfalls: avoid typos in status constants, ensure the current-index state persists correctly across calls (thread-safe, no unintended resets), and guarantee the algorithm never returns an unavailable node; add comprehensive unit tests to catch these cases. Then implement a consistent-hashing load balancer with virtual nodes; support node additions/removals with minimal key movement and demonstrate how you locate the next available node when the initial target is down. Compare the two approaches in terms of time/space complexity, rebalancing behavior, cache affinity, failure handling, hotspot risk, and typical production use cases.

Quick Answer: This question evaluates a candidate's ability to implement and reason about load balancing algorithms (round-robin and consistent hashing), covering competencies in data structures, hashing, concurrency, correctness, and unit testing.

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DoorDash logo
DoorDash
Sep 6, 2025, 12:00 AM
Software Engineer
Onsite
Coding & Algorithms
9
0

Implement a round-robin load balancer that selects among service nodes, skipping unavailable nodes and wrapping around correctly. Fix typical pitfalls: avoid typos in status constants, ensure the current-index state persists correctly across calls (thread-safe, no unintended resets), and guarantee the algorithm never returns an unavailable node; add comprehensive unit tests to catch these cases. Then implement a consistent-hashing load balancer with virtual nodes; support node additions/removals with minimal key movement and demonstrate how you locate the next available node when the initial target is down. Compare the two approaches in terms of time/space complexity, rebalancing behavior, cache affinity, failure handling, hotspot risk, and typical production use cases.

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