This document describes various guidelines to follow when writing SQL queries, either using ActiveRecord/Arel or raw SQL queries.
The most common way to search for data is using the LIKE statement. For example, to get all issues with a title starting with "WIP:" you'd write the following query:
SELECT *
FROM issues
WHERE title LIKE 'WIP:%';On PostgreSQL the LIKE statement is case-sensitive. On MySQL this depends on the case-sensitivity of the collation, which is usually case-insensitive. To perform a case-insensitive LIKE on PostgreSQL you have to use ILIKE instead. This statement in turn isn't supported on MySQL.
To work around this problem you should write LIKE queries using Arel instead of raw SQL fragments as Arel automatically uses ILIKE on PostgreSQL and LIKE on MySQL. This means that instead of this:
Issue.where('title LIKE ?', 'WIP:%')You'd write this instead:
Issue.where(Issue.arel_table[:title].matches('WIP:%'))Here matches generates the correct LIKE / ILIKE statement depending on the database being used.
If you need to chain multiple OR conditions you can also do this using Arel:
table = Issue.arel_table
Issue.where(table[:title].matches('WIP:%').or(table[:foo].matches('WIP:%')))For PostgreSQL this produces:
SELECT *
FROM issues
WHERE (title ILIKE 'WIP:%' OR foo ILIKE 'WIP:%')In turn for MySQL this produces:
SELECT *
FROM issues
WHERE (title LIKE 'WIP:%' OR foo LIKE 'WIP:%')Neither PostgreSQL nor MySQL use any indexes when using LIKE / ILIKE with a wildcard at the start. For example, this will not use any indexes:
SELECT *
FROM issues
WHERE title ILIKE '%WIP:%';Because the value for ILIKE starts with a wildcard the database is not able to use an index as it doesn't know where to start scanning the indexes.
MySQL provides no known solution to this problem. Luckily PostgreSQL does provide a solution: trigram GIN indexes. These indexes can be created as follows:
CREATE INDEX [CONCURRENTLY] index_name_here
ON table_name
USING GIN(column_name gin_trgm_ops);The key here is the GIN(column_name gin_trgm_ops) part. This creates a GIN index with the operator class set to gin_trgm_ops. These indexes can be used by ILIKE / LIKE and can lead to greatly improved performance. One downside of these indexes is that they can easily get quite large (depending on the amount of data indexed).
To keep naming of these indexes consistent please use the following naming pattern:
index_TABLE_on_COLUMN_trigramFor example, a GIN/trigram index for issues.title would be called index_issues_on_title_trigram.
Due to these indexes taking quite some time to be built they should be built concurrently. This can be done by using CREATE INDEX CONCURRENTLY instead of just CREATE INDEX. Concurrent indexes can not be created inside a transaction. Transactions for migrations can be disabled using the following pattern:
class MigrationName < ActiveRecord::Migration
disable_ddl_transaction!
endFor example:
class AddUsersLowerUsernameEmailIndexes < ActiveRecord::Migration
disable_ddl_transaction!
def up
return unless Gitlab::Database.postgresql?
execute 'CREATE INDEX CONCURRENTLY index_on_users_lower_username ON users (LOWER(username));'
execute 'CREATE INDEX CONCURRENTLY index_on_users_lower_email ON users (LOWER(email));'
end
def down
return unless Gitlab::Database.postgresql?
remove_index :users, :index_on_users_lower_username
remove_index :users, :index_on_users_lower_email
end
endThis can't be stressed enough: never use ActiveRecord's pluck to pluck a set of values into memory only to use them as an argument for another query. For example, this will make the database very sad:
projects = Project.all.pluck(:id)
MergeRequest.where(source_project_id: projects)Instead you can just use sub-queries which perform far better:
MergeRequest.where(source_project_id: Project.all.select(:id))The only time you should use pluck is when you actually need to operate on the values in Ruby itself (e.g. write them to a file). In almost all other cases you should ask yourself "Can I not just use a sub-query?".
UNIONs aren't very commonly used in most Rails applications but they're very powerful and useful. In most applications queries tend to use a lot of JOINs to get related data or data based on certain criteria, but JOIN performance can quickly deteriorate as the data involved grows.
For example, if you want to get a list of projects where the name contains a value or the name of the namespace contains a value most people would write the following query:
SELECT *
FROM projects
JOIN namespaces ON namespaces.id = projects.namespace_id
WHERE projects.name ILIKE '%gitlab%'
OR namespaces.name ILIKE '%gitlab%';Using a large database this query can easily take around 800 milliseconds to run. Using a UNION we'd write the following instead:
SELECT projects.*
FROM projects
WHERE projects.name ILIKE '%gitlab%'
UNION
SELECT projects.*
FROM projects
JOIN namespaces ON namespaces.id = projects.namespace_id
WHERE namespaces.name ILIKE '%gitlab%';This query in turn only takes around 15 milliseconds to complete while returning the exact same records.
This doesn't mean you should start using UNIONs everywhere, but it's something to keep in mind when using lots of JOINs in a query and filtering out records based on the joined data.
GitSwarm comes with a Gitlab::SQL::Union class that can be used to build a UNION of multiple ActiveRecord::Relation objects. You can use this class as follows:
union = Gitlab::SQL::Union.new([projects, more_projects, ...])
Project.from("(#{union.to_sql}) projects")When ordering records based on the time they were created you can simply order by the id column instead of ordering by created_at. Because IDs are always unique and incremented in the order that rows are created this will produce the exact same results. This also means there's no need to add an index on created_at to ensure consistent performance as id is already indexed by default.