Putting It All Together

You've built a real search engine:

What we didn't cover:

• Stemming / lemmatization: Porter stemmer, Snowball. Handles morphological variation.
• Synonyms: query expansion ("car" -> {"car", "automobile", "vehicle"}).
• Faceted search: navigate by category, price range, date.
• Highlighting: show snippets with query terms bolded.
• Pagination cursors: "page 2 of 1000" without recomputing.
• Sharding: split index across machines for scale.
• Relevance feedback: thumbs-up signals to improve ranking.
• Vector search: dense embeddings (BERT, OpenAI) for semantic matching. Hybrid systems combine sparse (BM25) and dense.
• Distributed merging: scatter query to N shards; merge top-K from each.

Real systems you should look at:

• Lucene (Java) — the workhorse. Used by Elasticsearch, Solr, OpenSearch.
• Tantivy (Rust) — Lucene-inspired, much faster on modern hardware.
• Bleve (Go) — pure-Go full-text engine.
• Whoosh (Python) — pure-Python full-text engine. Excellent for learning.
• Meilisearch / Typesense — newer "instant" engines focused on UX.

You've now built the foundations of every one of them.