Autocomplete
The Problem & When to Use a Trie
What does autocomplete need beyond plain prefix search to feel useful?
From Prefix Node to Candidates
How does locating the prefix node set up the search for completions to suggest?
Ranking by Frequency or Weight
How do you decide which completions matter most when many share the prefix?
Storing Weights in the Trie
Where do popularity scores live, and how are they updated as usage changes?
Top-k Retrieval
How does a bounded heap surface the best \( k \) completions without sorting everything?
Heap vs. Full Sort
Why is a size-\( k \) heap over candidates cheaper than collecting and sorting them all?
Precomputation vs. On-Demand
When should each node cache its top suggestions versus computing them per query?
Caching Top Suggestions Per Node
What’s the memory-versus-latency tradeoff of storing precomputed top-k at every node?
Handling Typos & Fuzzy Matches
How might edit-distance tolerance extend strict prefix matching, and what does it cost?
Latency & Scale Trade-offs
What constraints — keystroke latency, dataset size, freshness — shape a production system?
Time Complexity
Where does a single autocomplete query spend its time?
Descent Plus Candidate Gathering
Why is reaching the prefix node \( O(L) \), and what makes gathering candidates depend on subtree size?
Top-k Selection
Why does maintaining a size-\( k \) heap add a \( \log k \) factor per candidate, and how does caching drop this to \( O(k) \)?
Space Complexity
What memory does ranked autocomplete cost?
Weights and the Trie
Why does storing a weight per node add only a constant per node over a plain trie?
Precomputed Top-k Caches
Why does caching \( k \) suggestions at every node multiply space by \( k \), and when is that worth it?
Real Uses
Where do you see this — search bars, IDEs, messaging apps, command palettes?
Pitfalls
Why do stale caches, ignoring frequency, and unbounded result sets degrade the experience?
Implementation Walkthrough
How do ranking and retrieval fit together in code?
Setup: Weighted Nodes
How do you extend the trie node to carry a frequency or score, updated on insert?
Gathering Candidates Below the Prefix
How does the traversal collect (word, weight) pairs from the prefix subtree?
Selecting the Top k
How does a min-heap of size \( k \) keep only the best completions as you stream candidates?
Optional: Reading a Cached Top-k
How would a per-node cache let you skip the subtree walk entirely?
Implementation
// implement from scratch here
Summary
Recap the whole topic in a few lines — the core idea, the key complexities and trade-offs, and when to reach for it. The cheat-sheet you’d skim before an exam or interview.
My Notes
Fill this in as you learn