Data structure for fast full text search

Question

A trie seems like it would work for small strings, but not for large documents, so not sure (1-100's of pages of text). Maybe it is possible to combine an inverted index with a suffix tree to get the best of both worlds. Or perhaps using a b-tree with words stored as nodes, and a trie for each node. Not sure. Wondering what a good data structure would be (b-tree, linked-list, etc.).

I'm thinking of searching documents such as regular books, web pages, and source code, so the idea of storing just words in an inverted index doesn't seem quite right. Would be helpful to know if you need alternative solutions for each or if there is a general one that works for them all, or a combination of them.

Paralipomena

Have a look here, it discusses some basics, but also list a number of open-source solutions (lucene, solr, ...) – Galengalena

In case you want to support approximate search (typos, etc), the standard approach is probably n-grams. Here is the Google n-gram viewer. – Galengalena

Answer 1

You do need an inverted index at the end of the day for interleaving matching results from each of your query terms but an inverted index can be built either from Trie or a Hash Map. A trie would allow fuzzy look-ups, while an hash map based inverted-index would only allow an exact look up of a token.

To optimize for memory usage, you can use memory optimized versions of Trie like Radix Tree or Adaptive Radix Tree (ART). I've had great success using ART for an open source fuzzy search engine project I've been working on: https://github.com/typesense/typesense

With Typesense, I was able to index about 1 million Hacker News titles in about 165 MB of RAM (uncompressed size on disk was 85 MB). You can probably squeeze it in even further if your use case is more specific and don't need some metadata fields I added to the data structure.

Glede