Design Top K ranking system
Problem Description
Design a real-time system to continuously identify and serve the Top K most frequent items from a high-volume, continuous stream of events. Each event references a unique item ID. The system must support low-latency queries for these Top K items.
Constraints and Assumptions
- Event Volume: Millions of events per second.
- Item Cardinality: High.
- K Value: Small, typically between 10 and 1,000.
- Query Types:
- Top K for various time windows (e.g., last 1 minute, 1 hour, 1 day).
- Top K potentially grouped by a key (e.g., per region, per tenant).
- Result Freshness: Near-real-time with bounded staleness.
Core Requirements
- Data Ingestion & Processing: Ingest a large, streaming dataset and continuously identify the Top K elements.
- Data Structures: Select appropriate data structures for both exact and approximate solutions.
- Scalability: The system must scale horizontally across shards and partitions.
- Event Handling: Manage insertions, deletions/expirations (critical for sliding windows), and out-of-order/late events.
- Query Performance: Support high-throughput, low-latency queries on the read path, incorporating caching and materialization strategies.
- System Qualities: Discuss consistency models, fault tolerance mechanisms, and operational considerations.
Deliverables
Provide a comprehensive system design, detailing:
- Key algorithms and data structures, including their complexity and accuracy considerations.
- Architectural trade-offs made during the design process.
AI interview prep
Core dilemma
Balancing the computational cost of maintaining accurate, real-time Top K counts across multiple dynamic time windows and groupings against the need for high-throughput ingestion and low-latency query serving, all while operating at massive scale with bounded staleness.
3 tradeoffs and 3 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
Design Top K ranking system
Problem Description
Design a real-time system to continuously identify and serve the Top K most frequent items from a high-volume, continuous stream of events. Each event references a unique item ID. The system must support low-latency queries for these Top K items.
Constraints and Assumptions
- Event Volume: Millions of events per second.
- Item Cardinality: High.
- K Value: Small, typically between 10 and 1,000.
- Query Types:
- Top K for various time windows (e.g., last 1 minute, 1 hour, 1 day).
- Top K potentially grouped by a key (e.g., per region, per tenant).
- Result Freshness: Near-real-time with bounded staleness.
Core Requirements
- Data Ingestion & Processing: Ingest a large, streaming dataset and continuously identify the Top K elements.
- Data Structures: Select appropriate data structures for both exact and approximate solutions.
- Scalability: The system must scale horizontally across shards and partitions.
- Event Handling: Manage insertions, deletions/expirations (critical for sliding windows), and out-of-order/late events.
- Query Performance: Support high-throughput, low-latency queries on the read path, incorporating caching and materialization strategies.
- System Qualities: Discuss consistency models, fault tolerance mechanisms, and operational considerations.
Deliverables
Provide a comprehensive system design, detailing:
- Key algorithms and data structures, including their complexity and accuracy considerations.
- Architectural trade-offs made during the design process.