Design Search Autocomplete

Question

Design a typeahead suggestion system for a search engine. Cover trie-based data structures, ranking by popularity and personalization, real-time updates, and serving suggestions with sub-50ms latency.

Codemia · Accepted Answer

- **Functional Requirements**:
  - Provide typeahead suggestions for user queries in real time.
  - Support multi-language queries and synonyms.
  - Rank suggestions based on popularity, personalization, and recent trends.
  - Allow users to select suggestions with keyboard or mouse input.
  - Support real-time updates to the suggestion list as new data is indexed.

- **Non-Functional Requirements**:
  - Latency: Serve suggestions with a response time of less than 50ms.
  - Scalability: Handle millions of concurrent users with increasing query volume.
  - Availability: Ensure 99.9% uptime for the suggestion service.
  - Consistency: Provide consistent suggestions across multiple sessions for the same user.

To estimate capacity, we can start with the following assumptions:
- **User Base**: Assume Google Search has approximately 3 billion queries per day.
- **Typeahead Usage**: Let's say 30% of these queries trigger typeahead suggestions, which translates to about 900 million queries per day.
- **Queries per Second**: 900 million queries / 86400 seconds = ~10,000 queries per second (QPS).
- **Latency Requirement**: With a target of sub-50ms latency, we must ensure our architecture can handle spikes, so we plan for up to 20,000 QPS to account for peaks.

Based on these calculations, we need our infrastructure to support approximately 20,000 QPS to handle traffic effectively.

The architecture will consist of the following components:
- **Frontend Application**: Responsible for capturing user input and displaying suggestions.
- **Load Balancer**: Distributes incoming requests to multiple backend servers to handle high QPS.
- **Suggestion Service**: A microservice that processes input queries and fetches suggestions from the data store.
- **Trie-Based Data Store**: A highly optimized trie data structure for retrieving suggestions efficiently.
- **Cache Layer**: Implement Redis or Memcached to cache frequently accessed suggestions for low-latency access.
- **Ranking Service**: A machine learning model or algorithm that scores suggestions based on popularity and user personalization.
- **Real-Time Update Service**: Ingests new data and updates the trie structure and cache in real-time.

**Technology Choices**:
- Frontend: React.js
- Load Balancer: Google Cloud Load Balancing
- Backend: Go or Java microservices
- Trie Data Structure: Custom implementation in-memory
- Cache: Redis
- ML Ranking: TensorFlow Serving or Google Cloud AI Platform

The data model will be designed to support efficient lookups and real-time updates:

- **Trie Node Structure**:
  - `character`: char
  - `isEndOfWord`: boolean
  - `children`: map of character to TrieNode
  - `suggestions`: list of suggestion objects (with fields for popularity, timestamp, etc.)

- **Suggestion Object**:
  - `query`: string
  - `popularity`: int (number of times the query has been searched)
  - `timestamp`: timestamp (last time the query was suggested)
  - `userPreferences`: list of user-specific factors for personalization

This model allows efficient query processing and allows real-time updates as new queries come in.

Several architectural decisions will entail trade-offs:
- **In-Memory vs. Disk-Based Storage**: Using an in-memory trie provides faster access but limits the size of the dataset. We must balance memory usage with the need for a rich set of suggestions.
- **Real-Time Updates vs. Latency**: Real-time updates can introduce latency due to the need to maintain the trie structure. We may need to implement a batching system for updates to ensure latency is kept low.
- **Personalization vs. Generalization**: Personalizing suggestions for individual users may lead to less popular suggestions being shown. We need to find the right balance to ensure users still see trending or popular queries.
- **Complexity of Ranking Algorithm**: While a complex ranking algorithm may yield better results, it can also increase response time. We must optimize for speed without sacrificing too much accuracy in suggestions.

Design Search Autocomplete

Google

How to Approach This

Sharpen Your Skills on Codemia

Sample Answer

Requirements

Capacity Estimation

Submit Your Answer

Google Software Engineer Interview Guide

Related Questions

Design a high-throughput Inventory Management System

Design a low-latency Rate Limiting System

Design a fault-tolerant Payment System

Design a fault-tolerant Messaging System

Design Walmart Product Search