Backend & APIs · Intermediate
Build a Search Engine
Index a collection of documents into an inverted index and rank full-text queries by relevance using TF-IDF scoring.
You build a small full-text search engine that ingests a corpus of documents, tokenizes and normalizes their text, and stores an inverted index mapping each term to the documents that contain it. At query time you look up the query terms, intersect or union their posting lists, and rank the matching documents with TF-IDF so the most relevant results come first. It is worth building because it reveals the machinery behind tools like Elasticsearch and Lucene, and it teaches information-retrieval fundamentals, text normalization, and ranking math. It is resume-worthy because search and relevance ranking appear across data, backend, and platform roles, and implementing an index from scratch shows you understand the data structures and scoring rather than treating search as a black box.
What you build
- Ingests a document corpus and assigns stable document IDs
- Tokenizes, lowercases, and removes stop words during indexing
- Builds an inverted index of term to posting lists with frequencies
- Supports multi-term queries combining posting lists
- Ranks results with TF-IDF scoring
- Optional stemming to match word variants
- Returns ranked document IDs with snippets or scores
What it teaches
- Inverted index construction and posting lists
- Text tokenization, normalization, and stop-word removal
- TF-IDF relevance scoring
- Boolean and ranked retrieval over posting lists
- Trade-offs in information-retrieval data structures
- Evaluating ranking quality on a real corpus
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Take it further
- Add phrase queries using positional postings.
- Persist the index to disk and memory-map it for large corpora.
- Swap in BM25 scoring and compare ranking quality.
