Blog Engineering Fast Search Using PostgreSQL Trigram Text Indexes
Published on March 18, 2016
8 min read

Fast Search Using PostgreSQL Trigram Text Indexes

In this article we'll look at how these indexes work and how they can be used to speed up queries using LIKE conditions.

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GitLab 8.6 will ship with improved search performance for PostgreSQL thanks to the use of trigram indexes. In this article we'll look at how these indexes work and how they can be used to speed up queries using LIKE conditions.

How to Use PostgreSQL Fast Search in GitLab?

GitLab allows users to search for issues, comments, commits, code, merge requests, and snippets. The traditional approach to developing a system for searching data like this in an RDBMS is to simply use a LIKE condition. A LIKE operates on a string that specifies what to search for and optional percentage signs acting as wildcards. For example, to match all values starting with "Alice" you'd use the string 'Alice%'. If one wants to search all records in a table (e.g. all users) they might write the following query:

FROM users
WHERE name LIKE 'Alice%';

The wildcards used for a LIKE condition can appear anywhere (and optionally multiple times) in the string, as such all these values are valid:

  • 'Alice%'
  • '%Alice'
  • '%Alice%'
  • '%Al%ice%'

However, wildcards being allowed poses a problem: index usage. When a wildcard appears somewhere at the end of a string both MySQL and PostgreSQL are able to use any existing indexes. However, when a wildcard appears at the start of a string things become problematic. To better understand the problem, imagine you have the following list of names:

  • Alice
  • Bob
  • Charlie
  • Eve
  • Emily

When searching for any name containing "li" the only solution is to iterate over all values and check if each value contains the string "li". Because the value can appear anywhere in the strings to search an index won't help as we'd still have to compare every value one by one with no way of reducing the set of rows to search through. This in turn can lead to very slow queries depending on the amount of data to search through.

Since both MySQL and PostgreSQL provide full text searching capabilities one solution would be to use this instead of a regular LIKE condition. Sadly both implementations are not without their problems. Up until MySQL 5.6 full text search only worked on MyISAM tables which in turn meant not being able to use transactions. Both MySQL and PostgreSQL also use different syntax for searching and require different steps to set things up. For example, MySQL uses the following syntax:

FROM users
WHERE MATCH (username) AGAINST ('yorick');

PostgreSQL uses the following instead:

FROM users
WHERE to_tsvector('english', username) @@ to_tsquery('english', 'yorick');

The differences in syntax make the code more complex. On top of that PostgresSQL full text search works best when the text vectors are stored in physical columns with an index. This in turn means having to adjust all your queries to use these columns instead of the regular ones, resulting queries such as:

FROM users
WHERE username_tsvector @@ to_tsquery('english', 'yorick');

This assumes username_tsvector contains a text vector built from the data stored in the username column. To further complicate matters you'd have to set up a stored procedure and database trigger to keep these text vector columns in sync with the ones containing the raw data.

Another problem with full text search is that words are broken up according to the rules defined by the language of the text. For example, on PostgreSQL converting "Yorick Peterse" to a text vector results in the values "peters" and "yorick". This means that searching for "yorick" or "peterse" will match the data, but searching for "yor" will not. To showcase this we can run the following query in PostgreSQL:

WHERE to_tsvector('english', 'Yorick Peterse') @@ to_tsquery('english', 'peterse');

Here to_tsvector() creates a text vector with English as the language and "Yorick Peterse" as the input. The to_tsquery() function in turn creates a text search query with English as the language and "peterse" as the input.

Running this query will result in a single row being returned. On the other hand, this will return no rows:

WHERE to_tsvector('english', 'Yorick Peterse') @@ to_tsquery('english', 'yor');

This is problematic when you don't know exactly what you're looking for, for example when you're looking for a person but only know part of their first name.

In short, full text search is only really an option if you only support PostgreSQL or MySQL as supporting both leads to a lot of unwanted complexity.

Trigram Indexes

While MySQL offers no further solutions (that I know of), PostgreSQL on the other hand has some extra tricks up its sleeves: trigram indexes. Trigram indexes work by breaking up text in trigrams. Trigrams are basically words broken up into sequences of 3 letters. For example, the trigram for "alice" would be:

{ali, lic, ice}

PostgreSQL supports trigram indexes and operations via the pg_trgm extension. This extension adds a few functions, operators, and support for trigram indexes (Postgres using GIN or GiST indexes to be exact). To see what kind of trigrams PostgreSQL can produce we can run the following query:

select show_trgm('alice');

This will generate the trigrams for the string "alice", producing the following output:

 {"  a"," al",ali,"ce ",ice,lic}

A big benefit of this extension is that these trigram indexes can be used by the LIKE and ILIKE conditions without having to change your queries or setting up complex full text search systems. There are 2 requirements for this to work:

  1. The index created must be either a GIN or a GiST index, in case of GitLab we went with Postgres using GIN indexes due to them leading to better query timings (at the cost of being larger and somewhat slower to build).
  2. The index must have the appropriate operator class set.

In case of a GIN index the operator class we have to use is called gin_trgm_ops. We can create the appropriate indexes using a query such as the following:

CREATE INDEX CONCURRENTLY index_issues_on_title_trigram
ON issues
USING gin (title gin_trgm_ops);

To showcase the impact these indexes have on performance let's use the following query as an example:

FROM users
WHERE username ILIKE '%yorick%';

This query counts the amount of users where the username contains the string "yorick", regardless of the casing. Running this query on my local PostgreSQL database takes around 160 milliseconds and produces the following query plan:

 Aggregate  (cost=8143.40..8143.41 rows=1 width=0) (actual time=157.981..157.982 rows=1 loops=1)
   ->  Index Only Scan using index_users_on_username on users  (cost=0.42..8143.34 rows=26 width=0) (actual time=155.153..157.974 rows=6 loops=1)
         Filter: (username ~~* '%yorick%'::text)
         Rows Removed by Filter: 257532
         Heap Fetches: 0
 Planning time: 0.143 ms
 Execution time: 158.008 ms

To speed this up we'll run the following to create an index:

CREATE INDEX CONCURRENTLY index_users_on_username_trigram
ON users
USING gin (username gin_trgm_ops);

If we now re-run the query it takes only around 0.2 milliseconds and produces the following query plan:

 Aggregate  (cost=152.41..152.42 rows=1 width=0) (actual time=0.128..0.128 rows=1 loops=1)
   ->  Bitmap Heap Scan on users  (cost=52.20..152.35 rows=26 width=0) (actual time=0.115..0.126 rows=6 loops=1)
         Recheck Cond: (username ~~* '%yorick%'::text)
         Heap Blocks: exact=6
         ->  Bitmap Index Scan on index_users_on_username_trigram  (cost=0.00..52.19 rows=26 width=0) (actual time=0.106..0.106 rows=6 loops=1)
               Index Cond: (username ~~* '%yorick%'::text)
 Planning time: 0.366 ms
 Execution time: 0.167 ms

In other words, creating the trigram index results in the query being around 946 times faster.

GitLab & Trigram Indexes

GitLab 8.6 will create trigram indexes for PostgreSQL users leading to vastly improved search performance (though there's still some work to be done in the future). To make this work (while still supporting MySQL) we did have to port over some changes from an open Rails pull request to ensure the indexes were dumped properly to db/schema.rb. These changes can be found in config/initializers/postgresql_opclasses_support.rb and were taken from Rails pull request #19090.

We also had to make some changes to ensure MySQL doesn't end up trying to create these indexes when loading the schema definition into a database. For example, db/schema.rb contains lines such as add_index ..., using: :gin and the using option is passed straight to the underlying database. Since MySQL doesn't support GIN indexes this would lead to database errors when trying to load db/schema.rb. The code that makes this work can be found in config/initializers/mysql_ignore_postgresql_options.rb.

Finally we made some small changes to the code to ensure queries automatically use ILIKE on PostgreSQL instead of lower(some_column) as ILIKE performs quite a bit better. On MySQL a regular LIKE is used as it's already case-insensitive.

All of the other details can be found in GitLab CE merge request "Refactor searching and use PostgreSQL trigram indexes for significantly improved performance".

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