Fast Search Using PostgreSQL Trigram Indexes

I thought this was a great article, and trigram indexes are definitely an amazing tool to have in the arsenal.

I think PostgreSQL fulltext search (with tsvector, tsquery etc.) is given an unfairly bad rap though. I'm not sure that "full text search works best when the text vectors are stored in physical columns with an index" is true - in my experience there's no performance penalty to just indexing the tsvector expression - no need to worry about additional columns or triggers.

I also think the assertion that the key problem is that "full text search is that words are broken up according to the rules defined by the language of the text" is very context-dependent. In many situations that's the most awesome feature of fulltext search. Usually when I search for "cat" I'm not interested in results for catacombs or categories, but when I search for restaurants, results matching restaurant (singular) are relevant too.

I definitely see that for a use-case like GitLab's, where the data includes code, full text search's stemming would be a hindrance rather than a help.

Trigrams are a very straightforward solution to this but they have one major limitation: They break with misspellings / alternative spellings. For example the words "vodka" and "votka" share no trigrams yet are obviously very similar.

I recently worked on this problem for a project and came up with a more robust solution using pre-computed Levenshtein indexes. You've probably heard of Levenshtein distance as a measure of word similarity. It counts the number of single-character edits that are required to transform one string into another. For example "vodka" and "votka" have a Levenshtein distance of 1.

Using a related approach (single character edits) it's possible to build a so-called Levenshtein index.

The major issue with a vanilla Levenshtein index is that it's impractically large. There is a way to make a space / time trade-off however that makes it much smaller. The basic idea is that instead of generating all Levenshtein variations for a word you only use the set of variations created by deleting single characters for a word.

Since that doesn't match all possible variations we have to compensate somehow. For example with a full index "votka" would be in the set of variations for "vodka" which means if we had misspelled vodka when searching for it we'd still get a match. With our deletions-only index, however, we'd get "voka" as our closest match. To work around this, we generate the deletion-only variations of our search term as well. In this case our search term "vodka" would become "odka", "vdka", "voka", etc. and each of those terms would be matched against the index. That means our query is slower overall as it contains a number of sub-queries but in practice it's more than fast enough.

I wrote a meandering blog post about it here: http://lattejed.com/writing-an-embedded-full-text-search-eng...

There are some additional ideas in there such as how to make a Levenshtein index handle prefix matches better (for e.g., incremental search) that may be helpful if you need to implement something like this.

If you do read that I'd like to point out that I no longer recommend LevelDB. That doesn't change anything of importance in the post though.

A project I worked on used trigram indexes a few years ago to solve the autocomplete problem. They are AMAZING - they basically allow you to run LIKE queries with wildcards anywhere in the string (as opposed to suffix-only-wildcards) against an index.

An autocomplete search for e.g. "rub rai" becomes the following SQL query:

    select * from topics where name ilike "%rub%rai%";

Which, thanks to the magic of trigram indexes returns in just a few ms, even against hundreds of thousands of rows. Without trigram indexes, the same query would be a full scan and would be too slow to justify hooking up to a search-as-you-type UI.

    create extension pg_trgm;
    create index topic_name_gin on topics using gin (name gin_trgm_ops);

Hi! Author in question here, if anybody would like to know more about this particular feature I'll be happy to answer any questions. The changes discussed in this article are currently available on GitLab.com, 8.6 will be released on the 22nd as usual.

"Another problem with full text search is that words are broken up according to the rules defined by the language of the text"

You can use to_tsvector('simple', column_name) if you don't want a language-specific parse, or you can make your own.

"This means that searching for “yorick” or “peterse” will match the data, but searching for “yor” will not"

You can use prefix searching:

  select to_tsvector('simple', 'Yorick Peterse') @@ to_tsquery('simple', 'yor:*') --true

Heck you can even do suffix searching:

  select to_tsvector('simple', reverse('Yorick Peterse')) @@ to_tsquery('simple', reverse('rick') || ':*')

The real problem with PostgreSQL's text search is that it doesn't support BM 25 or TFIDF out of the box

IIRC Google also used trigrams for their Code search engine. This allowed them to make a first filter before running a regex search on the resulting documents.

Does gitlab support regex searches?

So how good do the trigram indexes scale? Both on index build time and query speed.

Off topic: What is the motivation for GitLab to support two DBMS?

ah yes, the old 're-implement the search engine inside the database' project, undoubtedly put up on the board because someone is tired of their get-the-data-from-the-database-to-the-search-engine process breaking constantly.

next on the map: discovering how shitty dictionary management is, the joys of NLP, and abandoning the project entirely because you realize all this stuff has already been solved in 3 or 4 different ways and the getting-the-data-from-the-database-to-the-search-engine process isn't really that bad, and pulling your hair out from customers asking insane questions because they don't understand how search engines actually work and why can't this be like google? can't you just do it how google does it, even though you don't have $100B and 50,000 employees?

i realize this is a product feature but i have ptsd on this topic so i had to vent.