Design a global multi-game leaderboard

Quick Overview

This question evaluates a candidate's ability to design a scalable, real-time distributed leaderboard service, testing skills in API design, data modeling, ranking algorithms, ingestion and read-path architecture, caching, sharding, consistency-versus-latency trade-offs, idempotency, anti-cheat measures, and disaster recovery.

Design a global multi-game leaderboard

Company: Disney

Role: Software Engineer

Category: System Design

Difficulty: hard

Interview Round: Onsite

Design a global, multi-game leaderboard (ladder) service where each game maintains its own rankings and player standings update immediately after a match ends. Define APIs for submitting match results and fetching leaderboards, the data model for players/games/scores, and the ranking algorithm choice (e.g., Elo/TrueSkill) with rationale. Detail the write path from result ingestion to rank computation and propagation, read path for low-latency queries, caching strategy, sharding/partitioning by game and region, consistency versus latency trade-offs for worldwide users, idempotency for duplicate result submissions, anti-cheat/fraud detection, backfill/recalculation flows, and fault tolerance/DR considerations.

Overview: This question evaluates a candidate's ability to design a scalable, real-time distributed leaderboard service, testing skills in API design, data modeling, ranking algorithms, ingestion and read-path architecture, caching, sharding, consistency-versus-latency trade-offs, idempotency, anti-cheat measures, and disaster recovery.

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Disney
Sep 6, 2025
hardSoftware EngineerOnsiteSystem Design
12
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Design a Global, Multi‑Game Leaderboard (Ladder) Service

You are designing a global leaderboard platform used by many different games and modes. Each game maintains its own rankings, and player standings should update immediately after a match ends.

Requirements:

  • Define APIs for:
    • Submitting match results (single or batch)
    • Fetching leaderboards (global/region/friends/mode/season)
    • Fetching a player's current rank
  • Propose a data model for players, games, matches, ratings, and leaderboards.
  • Choose a ranking algorithm (e.g., Elo, Glicko‑2, TrueSkill) and explain why.
  • Detail the write path from result ingestion to rank computation and propagation.
  • Detail the read path for low‑latency queries.
  • Propose a caching strategy.
  • Explain sharding/partitioning by game and region.
  • Discuss consistency versus latency trade‑offs for worldwide users.
  • Ensure idempotency for duplicate result submissions.
  • Outline anti‑cheat/fraud detection measures.
  • Describe backfill/recalculation flows (e.g., season reset, algorithm change, bans).
  • Provide fault tolerance and disaster recovery considerations.

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