This is my reading notes for Chapter 13 in book “System Design Interview – An insider’s guide (Vol. 1)”.
Chapter Overview: This chapter focuses on designing a search autocomplete system—a critical feature used in search engines, e-commerce platforms, and other applications to enhance user experience by suggesting relevant search terms as the user types. The chapter guides readers through defining requirements, performing estimations, designing a high-level architecture, and diving into detailed implementation considerations.
Key Requirements:
- Fast Response Time:
- Goal: The system should provide autocomplete suggestions within 100 milliseconds to ensure a smooth and responsive user experience.
- Explanation: If the system takes too long, it disrupts the user’s flow and may lead to frustration. For high traffic systems, every millisecond counts, so optimization is critical.
- Example: When a user types “how to cook” into a search bar, they should instantly see suggestions like “how to cook rice” or “how to cook pasta.”
- Relevance:
- Goal: The suggestions must be relevant to what the user is likely to search for, based on the prefix they have typed.
- Explanation: Relevance is often determined by analyzing historical search data, user behavior, and trends. The system must be able to rank these suggestions effectively.
- Example: For the prefix “pyt,” relevant suggestions might include “python,” “python tutorial,” and “python string methods” if the system has detected a trend in users searching for Python-related terms.
- Sorted Results:
- Goal: Suggestions should be sorted based on criteria such as popularity, recency, or a combination of factors.
- Explanation: Sorting helps users find the most commonly sought-after information first, reducing the time they spend searching.
- Example: For the prefix “star,” the system might prioritize “Starbucks” over “Starfish” if “Starbucks” is more frequently searched.
- Scalability:
- Goal: The system should be able to handle millions of users, particularly during peak times, without degradation in performance.
- Explanation: As user bases grow, the system must be able to scale horizontally (by adding more servers) or vertically (by upgrading hardware) to manage the load.
- Example: An e-commerce site like Amazon must ensure that its autocomplete system can handle spikes in traffic during events like Black Friday.
- High Availability:
- Goal: The system should remain operational even if parts of it fail.
- Explanation: High availability is achieved through redundancy and failover mechanisms. If one server or data center goes down, others should seamlessly take over.
- Example: If a server handling autocomplete requests crashes, another server should immediately start handling those requests without users noticing any disruption.
Back-of-the-Envelope Estimation:
- Assumptions:
- 10 million daily active users (DAU).
- Each user performs around 10 searches per day.
- Each search generates approximately 20 autocomplete requests (e.g., for the query “apple,” requests would include “a,” “ap,” “app,” etc.).
- Storage requirement: Each query might require about 20 bytes (including metadata).
- Calculation:
- Queries per Second (QPS):
- Total queries = 10 million users * 10 searches/day * 20 requests per search = 2 billion requests/day.
- QPS = 2 billion requests/day / 86,400 seconds/day ≈ 23,000 QPS.
- Peak QPS: The peak load might be double during busy times, leading to around 46,000 QPS.
- Queries per Second (QPS):
- Storage Needs:
- Assuming 20 bytes per query, daily storage = 2 billion * 20 bytes = 40 GB/day.
- If queries are stored for a year, total storage ≈ 40 GB/day * 365 ≈ 14.6 TB/year.
High-Level Design:
- Data Gathering Service:
- Purpose: This service collects user queries in real-time and updates the frequency of each query in a data store.
- Components: A database (like a distributed NoSQL store) that tracks how often each query is made, and a processing service that aggregates this data.
- Example: When multiple users search for “new york times,” the system increments the frequency count for this phrase in the database.
- Query Service:
- Purpose: The query service quickly retrieves the top suggestions for the user as they type.
- Components: A trie (prefix tree) that stores query prefixes, with nodes containing references to the top queries starting with that prefix.
- Example: When a user types “mo,” the service might return “movies,” “mortgage rates,” and “motorcycles” as the top suggestions.
- Ranking and Sorting:
- Purpose: To sort the suggestions based on predefined criteria like popularity, recency, or personalization.
- Components: Ranking algorithms that consider query frequency, user behavior, and trends.
- Example: If “mortgage rates” is currently trending, it might appear higher in the suggestions for “mo” compared to “movies.”
- Sharding and Replication:
- Purpose: To ensure the system can scale and handle high traffic, the data is sharded (distributed) across multiple servers.
- Components: A distributed database that shards data based on the prefix (e.g., the first letter or first two letters of the query).
- Example: Queries starting with “a” might be stored on one server, while those starting with “b” are on another. This prevents any one server from being overwhelmed.
Design Deep Dive:
- Trie Data Structure:
- Purpose: The trie is used to efficiently store and retrieve query prefixes. It supports fast lookup times, which are crucial for meeting the 100 ms response time.
- Optimization: Each node in the trie not only stores the current character but also maintains a list of top search queries that share that prefix.
- Example: For the prefix “ap,” the trie node might store references to “apple,” “apartment,” and “application,” ranked by their popularity.
- Real-Time Data Update:
- Purpose: To keep the trie updated with the latest search trends without causing latency issues.
- Strategy: Instead of updating the trie for every single search in real-time, the system batches updates and processes them at regular intervals (e.g., hourly or daily).
- Example: If there’s a sudden surge in searches for “apocalypse,” the trie might be updated at the end of the day to include this in the top suggestions for “ap.”
- Handling Large Data Sets:
- Sharding: To manage large amounts of data, the trie is split across multiple servers. Initially, sharding might be based on the first character of the query, but this can lead to imbalances.
- Advanced Sharding: Analyze historical query data to distribute load more evenly. For instance, “s” might be broken down further into “sa,” “sb,” etc., to prevent any single shard from becoming too large.
- Example: If “s” is a popular starting letter, the system might shard based on the first two letters, distributing “sa,” “sb,” and “sc” queries to different servers.
- Caching Popular Queries:
- Purpose: Caching frequently searched queries ensures faster response times by reducing the need to repeatedly query the trie.
- Strategy: Implement an in-memory cache (like Redis) to store the top N queries for each popular prefix.
- Example: For the prefix “re,” the cache might store “restaurants near me,” “red dress,” and “recipe ideas” to quickly serve these popular suggestions without accessing the trie.
Scaling the Storage:
- Problem: As the trie grows, it may no longer fit on a single server, leading to the need for distributed storage.
- Solution:
- Sharding: Start by sharding based on the first character or the first two characters. If the data is still too large, implement a more granular sharding strategy.
- Replication: Use data replication to ensure that even if one shard fails, another can take over without downtime.
- Example: Sharding by prefix (“a” on server 1, “b” on server 2, etc.) might eventually evolve to sharding by first two characters (“aa-ab” on server 1, “ac-ad” on server 2) to balance load and storage.
Optimizing the System for Real-Time Trends:
- Problem: During significant events (e.g., breaking news, viral content), the system must adapt quickly to new trends.
- Solution:
- Real-Time Data Processing: Use streaming data processing systems like Apache Kafka to detect trending queries in real-time and update the trie or cache accordingly.
- Trending Queries: Introduce a mechanism to detect and prioritize these in autocomplete suggestions dynamically.
- Example: If a new movie “Inception 2” is announced, the system should immediately start suggesting “Inception 2” as a top result after the user types “inc.”
Wrap-Up and Additional Considerations:
- Multi-Language Support:
- Problem: Supporting multiple languages can complicate the design, especially with languages that use different character sets.
- Solution: Store Unicode characters in trie nodes and ensure the system can handle different language-specific sorting and ranking rules.
- Example: For a multi-lingual site, the system might need to suggest both “sushi” and “寿司” (Japanese for sushi) depending on the user’s language preference.
- Monitoring and Analytics:
- Problem: Without proper monitoring, it’s hard to know if the system is performing optimally.
- Solution: Implement logging and analytics to track query performance, cache hit rates, and user engagement with suggestions.
- Example: Monitoring tools could alert the team if response times exceed 100 ms, prompting an investigation into potential bottlenecks.
- Error Handling and Failover:
- Problem: The system needs to be resilient to failures, such as a server going offline.
- Solution: Implement failover strategies where requests are automatically routed to a backup server if the primary one fails.
- Example: If a shard responsible for “a” queries fails, another server should immediately take over, ensuring users still receive suggestions without interruption.
The notes I took provide a comprehensive overview of the key concepts, requirements, and design strategies discussed in Chapter 13. The examples are tailored to help illustrate how the design principles apply in real-world scenarios, making it easier to grasp the complexities involved in designing a robust and scalable search autocomplete system.