Question 1

How does a trie data structure enable fast prefix search for autocomplete?

Accepted Answer

A trie (prefix tree) stores strings as character-by-character paths from the root node. Each node represents one character; traversing from root to a leaf spells out a complete string. Prefix search: start at the root and follow edges matching each character of the query prefix. The subtree below the final matched node contains all strings with that prefix. For autocomplete, the naive approach collects all strings in the subtree — O(k) where k is the number of matches. This is too slow for common prefixes ('a' might match millions of strings). Optimization: store the top-K completions (by popularity score) at every trie node, precomputed. A query for any prefix returns its precomputed top-K immediately — O(prefix_length) time. When a string's score updates, the change propagates up from the leaf to the root, updating the top-K list at each ancestor. This makes queries O(L) where L is the prefix length, with updates O(L * K log K) to re-sort K candidates at L ancestor nodes.

Question 2

How does Redis ZRANGEBYLEX implement autocomplete for smaller datasets?

Accepted Answer

Redis sorted sets with lexicographic ordering provide a simpler autocomplete for datasets up to ~10M strings. Insert strings with score 0 (score is irrelevant for lex ordering): ZADD completions 0 'apple'; ZADD completions 0 'application'; ZADD completions 0 'apply'. Prefix query: ZRANGEBYLEX completions '[app' '[appxff' — the '[' prefix indicates inclusive range start; 'xff' (max byte) as suffix matches any continuation. This returns 'apple', 'application', 'apply' in lexicographic order. For ranking by popularity: maintain a separate ZSET with popularity scores, or append the score as a zero-padded prefix to the string so lexicographic order matches popularity order. The main limitation: ZRANGEBYLEX returns all matches — you must limit client-side or use ZRANGEBYLEX ... LIMIT 0 10. Works well for sub-100ms autocomplete on up to 10M strings with a single Redis node.

Question 3

How do you rank and personalize autocomplete results?

Accepted Answer

Autocomplete ranking combines multiple signals: (1) Global popularity: click-through rate from autocomplete results, raw search frequency, conversion rate (did the user find what they wanted?). Computed in a daily batch job from search logs. (2) Trending: queries surging in the last 1-24 hours from a streaming pipeline (Kafka → Flink → trie score update). Breaking news terms appear in autocomplete within minutes. (3) Personalization: re-rank the top-20 global candidates using user-specific signals — the user's own search history, language, location. A user in France typing 'par' should see 'Paris' ranked higher than 'parking'. Personalization happens at query time (not in the trie) — fetch the top-20 global candidates from the trie, then apply a lightweight re-ranking model using user signals. (4) Query context: infer intent from the session — if the user has been searching for Python tutorials, 'list' should autocomplete to 'list comprehension Python', not 'grocery list'.

Question 4

What caching strategies reduce latency for an autocomplete service?

Accepted Answer

Autocomplete queries are highly repetitive — the same popular prefixes are queried millions of times. Caching strategy: (1) CDN edge cache: GET /autocomplete?q=app returns the same result for all users (before personalization). Cache at CDN edge nodes with a 60-second TTL. Sub-millisecond response for cached prefixes; the CDN handles 95%+ of traffic. (2) Redis cache: for prefixes not in CDN, a Redis lookup returns the top-K results in 1-2ms. TTL of 60 seconds. The trie service only handles cache misses. (3) Client-side debouncing: don't send a request on every keystroke — wait 50-100ms after the user stops typing. A user typing 'apple' at normal speed generates 5 keystrokes in 200ms; debouncing at 100ms means only 1-2 requests are sent. This reduces total request volume by 60-70%. (4) Client-side prefix cache: cache recent prefix→results in the browser. 'app' results are cached; typing 'appl' can be filtered client-side before the 100ms debounce fires — shows immediate results while the network request is in flight.

Typeahead / Autocomplete Service: Low-Level Design

Trie-Based Prefix Indexing

Alternative: Sorted Set Prefix Search in Redis

Ranking and Personalization

Serving Architecture

Index Updates