Tier 6 Capstone: Building a Search System

Step back and look at everything this tier covered: sorting algorithms that put things in order efficiently, binary search trees that keep data ordered and searchable as it changes, tree traversals that visit that data in useful sequences, tries that make prefix matching essentially free, and union-find for tracking groups. A real search-engine autocomplete feature - the kind that suggests results as you type into a search box - is not powered by any one of these in isolation. It is what you get when you combine them with one more idea: matching alone is not enough, you also need to rank.

• -Fast prefix matching - given what the user has typed so far, find every candidate word that starts with it. This is exactly the trie from two modules ago: walk to the node for the prefix, then explore everything beneath it.
• -A notion of relevance - "every word starting with 'th'" could be thousands of matches. Real autocomplete tracks how often each word has been searched (or clicked, or purchased) and uses that frequency as a relevance score.
• -Efficient top-k selection - once you have candidates and their scores, you need the k highest-scoring ones, not a full sort of everything. This is precisely the job a heap is good at (Tier 4), and Python's heapq module exposes it directly via nlargest.

Put together, that is: a trie for fast prefix lookup, a frequency map for ranking, and a heap-backed top-k selection to avoid sorting more than necessary. None of the three pieces is new - the insight is in how they compose.

This is a simplified sketch, not a production search engine - real systems shard this across machines, update frequencies in real time, and blend in recency and personalization signals too. But the core shape does not change: match fast (trie), rank by something meaningful (frequency, or a fancier relevance score), and select the top few efficiently (heap) instead of sorting everything just to throw most of it away.