Practice/Meta/Design Top K Trends Service

Design Top K Trends Service

Product DesignOptional

Problem Statement

Design a global leaderboard system for a mobile gaming platform that tracks and displays player rankings in real-time. The system must handle millions of concurrent players across different games, each with their own scoring mechanics. Players should be able to view their current rank, see the top N players globally or within their region, and receive near-instant updates when their position changes. The system needs to support multiple leaderboard types (global, regional, friends-only) and handle both casual games with frequent score updates and competitive tournaments with strict accuracy requirements.

Your solution should account for the massive write volume from score submissions, the read-heavy nature of leaderboard queries, and the challenge of maintaining consistency across distributed data centers while providing low-latency responses to users worldwide.

Key Requirements

Functional

Score Submission -- players can submit scores after completing game sessions, with validation to prevent cheating
Real-Time Ranking -- system calculates and displays current player rank among all participants within seconds
Top-N Queries -- users can retrieve top K players globally, regionally, or from their friend list
Historical Snapshots -- maintain leaderboard state at specific points in time for tournaments and seasons
Multiple Leaderboard Types -- support different ranking algorithms (highest score, fastest time, cumulative points)

Non-Functional

Scalability -- handle 10 million concurrent players with 50,000 score updates per second during peak hours
Reliability -- ensure 99.95% uptime with no data loss for score submissions
Latency -- score updates reflected in rankings within 5 seconds; leaderboard queries respond in under 200ms
Consistency -- eventual consistency acceptable for global rankings; strong consistency required for tournament final standings

What Interviewers Focus On

Based on real interview experiences, these are the areas interviewers probe most deeply:

1. Data Structure Selection for Ranking

The choice of data structure fundamentally determines system performance and scalability. Interviewers want to see you reason through tradeoffs between different approaches for maintaining sorted rankings.

Hints to consider:

Sorted sets in Redis provide O(log N) insertion but may not scale to hundreds of millions of players
Approximate algorithms like Count-Min Sketch can trade accuracy for memory efficiency
Sharding strategies for distributed sorted structures and how to merge results
Consider bucketing players into tiers (e.g., top 1000, next 10K, everyone else) with different storage strategies

2. Write Amplification and Hot Key Problems

Score updates create significant write pressure, especially for popular games where many players compete simultaneously. The system must handle both the volume and the hot key problem when updating a single leaderboard.

Hints to consider:

Batch score updates and process them asynchronously to reduce write load
Use write-behind caching patterns to buffer updates before persisting
Implement rate limiting per player to prevent abuse and excessive writes
Consider separating the write path (score ingestion) from the read path (leaderboard queries) entirely

3. Handling Global vs Regional vs Social Leaderboards

Different leaderboard scopes require different architectural approaches. A friends-only leaderboard has different access patterns and scale constraints than a global one.

Hints to consider:

Pre-compute friend leaderboards or compute them on-demand based on friend list size
Regional leaderboards can be independently maintained and don't require global coordination
Use a lambda architecture approach with both real-time and batch-computed views
Consider the fanout problem when a player's score changes and multiple leaderboards must update

4. Anti-Cheat and Score Validation

Ensuring legitimate scores is critical for player trust, but validation adds latency and complexity to the submission pipeline.

Hints to consider:

Server-side game session validation vs client-reported scores
Statistical anomaly detection to flag suspicious score patterns
Rate limiting and duplicate submission detection
Delayed visibility of scores while validation occurs in the background

Practice/Meta/Design Top K Trends Service

Design Top K Trends Service

Product DesignOptional

Problem Statement

Key Requirements

Functional

Score Submission -- players can submit scores after completing game sessions, with validation to prevent cheating
Real-Time Ranking -- system calculates and displays current player rank among all participants within seconds
Top-N Queries -- users can retrieve top K players globally, regionally, or from their friend list
Historical Snapshots -- maintain leaderboard state at specific points in time for tournaments and seasons
Multiple Leaderboard Types -- support different ranking algorithms (highest score, fastest time, cumulative points)

Non-Functional

Scalability -- handle 10 million concurrent players with 50,000 score updates per second during peak hours
Reliability -- ensure 99.95% uptime with no data loss for score submissions
Latency -- score updates reflected in rankings within 5 seconds; leaderboard queries respond in under 200ms
Consistency -- eventual consistency acceptable for global rankings; strong consistency required for tournament final standings

What Interviewers Focus On

Based on real interview experiences, these are the areas interviewers probe most deeply:

1. Data Structure Selection for Ranking

Hints to consider:

Sorted sets in Redis provide O(log N) insertion but may not scale to hundreds of millions of players
Approximate algorithms like Count-Min Sketch can trade accuracy for memory efficiency
Sharding strategies for distributed sorted structures and how to merge results
Consider bucketing players into tiers (e.g., top 1000, next 10K, everyone else) with different storage strategies

2. Write Amplification and Hot Key Problems

Hints to consider:

Batch score updates and process them asynchronously to reduce write load
Use write-behind caching patterns to buffer updates before persisting
Implement rate limiting per player to prevent abuse and excessive writes
Consider separating the write path (score ingestion) from the read path (leaderboard queries) entirely

3. Handling Global vs Regional vs Social Leaderboards

Different leaderboard scopes require different architectural approaches. A friends-only leaderboard has different access patterns and scale constraints than a global one.

Hints to consider:

Pre-compute friend leaderboards or compute them on-demand based on friend list size
Regional leaderboards can be independently maintained and don't require global coordination
Use a lambda architecture approach with both real-time and batch-computed views
Consider the fanout problem when a player's score changes and multiple leaderboards must update

4. Anti-Cheat and Score Validation

Ensuring legitimate scores is critical for player trust, but validation adds latency and complexity to the submission pipeline.

Hints to consider:

Server-side game session validation vs client-reported scores
Statistical anomaly detection to flag suspicious score patterns
Rate limiting and duplicate submission detection
Delayed visibility of scores while validation occurs in the background