![]() Match the rows found in step 2 against the pattern to eliminate false-positives.This step might result false-positives - rows that do contain one of the trigrams but don't match the pattern. Use the trigram index to find rows containing all these trigrams.Determine trigrams from the pattern, e.g.If we match last_name with LIKE against %newt% and the column has a trigram index then PostgreSQL will use the following algorithm: This can work for auto-completion but not for general searches (what if Isaac's email was Customer support staff I was trying to help couldn't accept such a limitation. ![]() Traditional (B-tree) indexes only improve LIKE performance when the pattern is left-anchored, e.g. Concurrent index creation is much slower and have more failure modes but doesn't lock out the table for writes. In this case, you can replace CREATE INDEX with CREATE INDEX CONCURRENTLY to create the index in the background. If your data set is large and your app needs to write to the table then you risk downtime. Keep in mind that creating an index locks out the table for writes. These operator classes support LIKE, ILIKE (case-insensitive matches), ~ (case-sensitive regular expressions), and ~* (case-insensitive regular expressions). They tell PostgreSQL how an operator (e.g. Similarly, there are two operator classes: gin_trgm_ops (for GIN) and gist_trgm_ops (for GiST). Choose the right type depending on the trade-offs your app is facing. GiST is faster to write but slower to read and is smaller on disk. ![]() GIN is faster to read but slower to write. There are two trigram index types: GIN and GiST. Create an index named customer_names_on_last_name_idx.In our case, to index customer_names.last_name, we need to issue: CREATE INDEX customer_names_on_last_name_idx ON customer_names USING GIN(last_name gin_trgm_ops) First, we need to active the pg_trgm extension: CREATE EXTENSION pg_trgm Using trigram indexes is a two-step operation. PostgreSQL can use this index to find all rows where last_name contains a given trigram or trigrams. For example: IDĪ trigram index on last_name looks like this: Trigram of last_name A single row in the table can have multiple index entires, one for each trigram. A trigram index is similar but stores trigrams extracted from a value instead of the value itself. The trigrams of the whole string are the sum of the trigrams of Ruby and Rails.Īn index stores order values extracted from a column along with a pointer to the row the value was extracted from (this is a simplistic model but good enough for our case). Ruby is normalized to ruby and its trigrams are r, ru, rub, uby, by. Ruby|Rails consists of two words, Ruby and Rails, that are handled separately.Rails is turned into rails thus the trigrams are r, ra, rai, ail, ils, and ls.Let's illustrate these rules with two examples: Note that downcasing makes trigrams case-insensitive. Non-alphanumeric characters are considered to be word boundaries. It also normalizes the word by downcasing it, prefixing two spaces and suffixing one. PostgreSQL splits a string into words and determines trigrams for each word separately. For example, the trigrams of Rails are Rai, ail, and ils. A Bit of Trigram TheoryĪ trigram is a sequence of three consecutive characters in a string. In this article, we'll cover cases 2 and 3. Such patterns aren't supported by B-tree indexes. You want to search for patterns that aren't left-anchored (e.g.You want to speed up LIKE, ILIKE, ~ or ~*.You need fuzzy case-insensitive string matching.The extension ships with PostgreSQL so you should be able to use it with most installations. On top of that it can speed up LIKE, ILIKE, ~ and ~* with trigram indexes, a new index type added by the extension. It's operational and conceptual overhead is much lower than that of PostgreSQL full-text search or a separate search engine. Pg_trgm is a PostgreSQL extension providing simple fuzzy string matching. In this article, I'll show you how I sped things up with the PostgreSQL trigram extension. Customer support staff would search this dataset multiple times a day. All these objects totaled to about 500,000 database rows. Each user could have multiple names, emails, phone numbers and addresses associated with his account. This week, Rails Postgres Guru Greg Navis shares another powerful tip.Ī few months ago, I was working on a project that had about 100,000 users. There's nothing quite like having a "tool-belt" full of tricks for getting the most performance out of your Rails app.
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