Writing a full-text search engine in TypeScript

Can TypeScript power a search engine that queries a million titles in 41 microseconds? Discover the data structures and algorithms that make it possible.

#1about 2 minutes

Why build a full-text search engine from scratch

Building a search engine from scratch is the best way to understand the underlying data structures and algorithms that power it.

#2about 2 minutes

An overview of existing full-text search solutions

Full-text search uses text indexes to quickly find terms, with established solutions like Elasticsearch, Algolia, and newer ones like Meilisearch.

#3about 6 minutes

Preparing text data with tokenization and stemming

Raw text is processed through tokenization, lowercasing, stop-word removal, and stemming to create a clean set of searchable terms.

#4about 6 minutes

Using hash maps to create an inverted index

An inverted index, implemented with a hash map, provides constant-time (O(1)) lookups by mapping search tokens directly to the documents that contain them.

#5about 8 minutes

Optimizing storage space with prefix trees (tries)

Prefix trees, or tries, optimize memory usage by storing common prefixes of words only once, avoiding redundant data storage.

#6about 9 minutes

Implementing typo tolerance with Levenshtein distance

The Levenshtein distance algorithm uses dynamic programming to calculate the "edit distance" between two strings, enabling effective typo tolerance in search queries.

#7about 2 minutes

Introducing Lyra, a fast TypeScript search engine

Lyra is a new, open-source full-text search engine written in TypeScript that achieves microsecond search times by leveraging efficient data structures.

#8about 3 minutes