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Implement consistent hashing ring

Last updated: Mar 29, 2026

Quick Overview

This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Implement consistent hashing ring states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  • medium
  • DoorDash
  • Coding & Algorithms
  • Software Engineer

Implement consistent hashing ring

Company: DoorDash

Role: Software Engineer

Category: Coding & Algorithms

Difficulty: medium

Interview Round: Technical Screen

Implement consistent hashing for routing keys to servers. Provide APIs addNode(nodeId, weight), removeNode(nodeId), and getNode(key). Use virtual nodes to balance load and ensure minimal key remapping when nodes join/leave. Explain hash choice, collision handling, and how you pick the number of virtual nodes. Analyze time/space complexity and compare with modulo hashing.

Quick Answer: This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Implement consistent hashing ring states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Solution

# Solution Alignment The prompt asks for an implementation-level answer. The safest way to present it is to define the state, maintain clear invariants, then walk through complexity and tests. ## Problem Restatement Implement consistent hashing for routing keys to servers. Provide APIs addNode(nodeId, weight), removeNode(nodeId), and getNode(key). Use virtual nodes to balance load and ensure minimal key remapping when nodes join/leave. Explain hash choice, collision handling, and how you pick the number of virtual nodes. Analyze time/space complexity and compare with modulo hashing. ## Recommended Approach Start with a brute-force baseline to confirm correctness, then identify the repeated work or ordering property that enables a better data structure such as a hash map, heap, stack, queue, two pointers, prefix sums, BFS/DFS, or dynamic programming. Write the implementation around a small invariant and test that invariant directly. ## Correctness The implementation should maintain an invariant after each loop or operation that directly matches the problem statement. At termination, that invariant implies the returned value has considered every valid candidate exactly once, or has preserved the required data-structure state after every API call. ## Complexity State the baseline complexity and the optimized complexity. For most interview constraints, justify why the optimized approach meets the expected input size. ## Edge Cases and Tests Empty and singleton inputs, duplicates, ties, invalid inputs, boundary values, and tests that exercise the main invariant.

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|Home/Coding & Algorithms/DoorDash

Implement consistent hashing ring

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DoorDash
Aug 9, 2025, 12:00 AM
mediumSoftware EngineerTechnical ScreenCoding & Algorithms
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Implement consistent hashing ring

Implement consistent hashing for routing keys to servers. Provide APIs addNode(nodeId, weight), removeNode(nodeId), and getNode(key). Use virtual nodes to balance load and ensure minimal key remapping when nodes join/leave. Explain hash choice, collision handling, and how you pick the number of virtual nodes. Analyze time/space complexity and compare with modulo hashing.

Constraints & Assumptions

  • Preserve the scope, facts, inputs, and requested outputs from the prompt above.
  • If the prompt leaves a detail unspecified, state a reasonable assumption before relying on it.
  • Keep the answer interview-ready: concise enough to present, but concrete enough to implement or evaluate.

Clarifying Questions to Ask

  • Clarify input sizes, value ranges, mutability, return format, and tie-breaking.
  • State the target time and space complexity before coding.
  • Call out edge cases such as empty inputs, duplicates, invalid values, overflow, and boundary sizes.

What a Strong Answer Covers

  • A clear algorithm with the right data structures and enough pseudocode or code-level detail to implement it.
  • A correctness argument that explains why the algorithm covers all required cases.
  • Time and space complexity, plus at least one alternative approach when relevant.
  • Focused tests for normal cases, edge cases, and failure modes.

Follow-up Questions

  • How would the approach change if the input were streaming or too large for memory?
  • What invariants would you assert in production code?
  • Which tests would catch off-by-one, duplicate, or tie-breaking bugs?

Submit Your Answer to Earn 20XP

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