📝 System Design Interview Classics · Data Flow
Search Autocomplete (Typeahead)
How search suggestions appear as you type: a prefix index built offline from past searches, served from memory, and refreshed regularly.
Drawing diagram…
What this diagram shows
- Suggestions come from a prefix trie held in memory
- The trie is rebuilt every hour from search logs
- Top suggestions per prefix are pre-computed
Prompt used
Data flow for search autocomplete: every search is logged to Kafka. An hourly Spark job counts queries, removes blocked terms and builds a trie with the top 10 suggestions for each prefix. The trie is stored in object storage and loaded by suggestion servers into memory. As a user types, the app calls the suggestion service, which answers from the in-memory trie; a CDN caches popular prefixes.
Mermaid code
flowchart LR U[User types] -->|Prefix| CDN[CDN Cache] CDN -->|Cache miss| SUG[Suggestion Service] TRIE[(In-memory Trie)] -->|Top 10 for prefix| SUG SUG -->|Suggestions| U S[Search Service] -->|Search queries| K[Kafka Search Log] K -->|Queries| SP[Hourly Spark Job] BL[(Blocked Terms)] -->|Filter list| SP SP -->|New trie| OS[(Object Storage)] OS -->|Load on refresh| TRIE
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