Summary: always use separate tables instead of polymorphic associations.
Rails makes it possible to define so called “polymorphic associations”. This
usually works by adding two columns to a table: a target type column, and a
target ID. For example, at the time of writing we have such a setup for
members with the following columns:
source_type: a string defining the model to use, can be either
source_id: the ID of the row to retrieve based on
source_type. For example, when
source_idwill contain a project ID.
While such a setup may appear to be useful, it comes with many drawbacks; enough that you should avoid this at all costs.
Because this setup relies on string values to determine the model to use it will
end up wasting a lot of space. For example, for
maximum size is 9 bytes, plus 1 extra byte for every string when using
PostgreSQL. While this may only be 10 bytes per row, given enough tables and
rows using such a setup we can end up wasting quite a bit of disk space and
memory (for any indexes).
Because our associations are broken up into two columns this may result in requiring composite indexes for queries to be performed efficiently. While composite indexes are not wrong at all, they can be tricky to set up as the ordering of columns in these indexes is important to ensure optimal performance.
One really big problem with polymorphic associations is being unable to enforce data consistency on the database level using foreign keys. For consistency to be enforced on the database level one would have to write their own foreign key logic to support polymorphic associations.
Enforcing consistency on the database level is absolutely crucial for maintaining a healthy environment, and thus is another reason to avoid polymorphic associations.
When using polymorphic associations you always need to filter using both columns. For example, you may end up writing a query like this:
SELECT * FROM members WHERE source_type = 'Project' AND source_id = 13083;
Here PostgreSQL can perform the query quite efficiently if both columns are indexed, but as the query gets more complex it may not be able to use these indexes efficiently.
Similar to functions and classes a table should have a single responsibility: storing data with a certain set of pre-defined columns. When using polymorphic associations you are instead storing different types of data (possibly with different columns set) in the same table.
Fortunately there is a very simple solution to these problems: simply use a separate table for every type you would otherwise store in the same table. Using a separate table allows you to use everything a database may provide to ensure consistency and query data efficiently, without any additional application logic being necessary.
Let’s say you have a
members table storing both approved and pending members,
for both projects and groups, and the pending state is determined by the column
requested_at being set or not. Schema wise such a setup can lead to various
columns only being set for certain rows, wasting space. It’s also possible that
certain indexes will only be set for certain rows, again wasting space. Finally,
querying such a table requires less than ideal queries. For example:
SELECT * FROM members WHERE requested_at IS NULL AND source_type = 'GroupMember' AND source_id = 4
Instead such a table should be broken up into separate tables. For example, you may end up with 4 tables in this case:
This makes querying data trivial. For example, to get the members of a group you’d run:
SELECT * FROM group_members WHERE group_id = 4
To get all the pending members of a group in turn you’d run:
SELECT * FROM pending_group_members WHERE group_id = 4
If you want to get both you can use a UNION, though you need to be explicit about what columns you want to SELECT as otherwise the result set will use the columns of the first query. For example:
SELECT id, 'Group' AS target_type, group_id AS target_id FROM group_members UNION ALL SELECT id, 'Project' AS target_type, project_id AS target_id FROM project_members
The above example is perhaps a bit silly, but it shows that there’s nothing stopping you from merging the data together and presenting it on the same page. Selecting columns explicitly can also speed up queries as the database has to do less work to get the data (compared to selecting all columns, even ones you’re not using).
Our schema also becomes easier. No longer do we need to both store and index the
source_type column, we can define foreign keys easily, and we don’t need to
filter rows using the
IS NULL condition.
To summarize: using separate tables allows us to use foreign keys effectively, create indexes only where necessary, conserve space, query data more efficiently, and scale these tables more easily (e.g. by storing them on separate disks). A nice side effect of this is that code can also become easier as you won’t end up with a single model having to handle different kinds of data.