January 16, 2022 6:00 PM PST
Topic: Typeahead Suggestion
Level: L5 (Senior)
Duration: 45 minutes
Drawing Tool Used: Excalibur
Overview
The meeting focused on the design and requirements for a typeahead suggestion system, discussing both functional and non-functional requirements, system design considerations, and optimization strategies. The discussion included scalability, availability, and the integration of machine learning for enhanced suggestions.
Requirements
Functional Requirements
- Implement Google-like autocomplete suggestions.
- Number of suggestions: 10.
- Track usage frequency over a period of 10 days.
- Focus on the US market and English language.
- Allow user customization based on location.
Non-Functional Requirements
- Low latency.
- High queries per second (QPS): 20 characters leading to 20 API calls.
- Scalability, availability, and reliability.
Estimation
- Daily queries: 5 billion, translating to 60,000 queries per second.
- Storage requirements:
- 1 billion new queries per day.
- 20 bytes per query (20 characters, ASCII).
- Total data: 20 GB daily, leading to 200 GB over 10 days, which can be stored in memory.
System Design
External APIs
- API:
getSuggestions(String) -> List<String> (size: 10)
Optimization Strategies
- Precompute the top 10 frequent queries for trie nodes to reduce latency, though this increases space consumption.
- Limit the depth of the trie to optimize performance.
- Implement server-side and client-side caching, potentially using Redis.
Scalability
- Addressed the scalability of the trie structure, which can lead to trillions of records.
- Suggested partitioning based on ranges and dynamic rebalancing.
Availability
- Load balancers to manage server health.
- Use of a Redis cluster for caching and server database replication.
Machine Learning Integration
- Incorporate machine learning for recommendations based on search history, location, and demographics.
- Discussed the need for real-time updates to the trie, potentially using batch processes and triggers for database maintenance.
Update Mechanisms
- Suggested a batch update process for the trie every 10 days.
- Discussed using moving averages to weigh recent data more heavily.
- Proposed methods for blacklisting keywords through application server filters.
Feedback and Discussion
- The interview was well-structured with solid answers and clear presentation.
- Emphasized the importance of localization and user flow in the design.
- Discussed trade-offs between using a trie versus a hashtable for caching and performance.
Technical Considerations
- Trie structure vs. hashtable for prefix storage.
- Considerations for caching strategies, including LRU (Least Recently Used) policies.
- Discussed the implications of using NoSQL databases for frequency tracking and real-time data processing.
Additional Notes
- Explored the potential for maintaining two systems for trending words and search queries.
- Discussed the use of streaming data processing frameworks like Kafka and Flink for real-time updates.
- Emphasized the need for a robust caching strategy to manage high-frequency queries effectively.
This meeting provided a comprehensive overview of the typeahead suggestion system's design, addressing both technical and operational challenges while considering future scalability and optimization strategies.