Filtering by label
Introduction
GitLab has labels that can be assigned to issues,
merge requests, and epics. Labels on those objects are a many-to-many relation
through the polymorphic label_links
table.
To filter these objects by multiple labels - for instance, 'all open
issues with the label ~Plan and the label ~backend' - we generate a
query containing a GROUP BY
clause. In a simple form, this looks like:
SELECT
issues.*
FROM
issues
INNER JOIN label_links ON label_links.target_id = issues.id
AND label_links.target_type = 'Issue'
INNER JOIN labels ON labels.id = label_links.label_id
WHERE
issues.project_id = 13083
AND (issues.state IN ('opened'))
AND labels.title IN ('Plan',
'backend')
GROUP BY
issues.id
HAVING (COUNT(DISTINCT labels.title) = 2)
ORDER BY
issues.updated_at DESC,
issues.id DESC
LIMIT 20 OFFSET 0
In particular, note that:
- We
GROUP BY issues.id
so that we can ... - Use the
HAVING (COUNT(DISTINCT labels.title) = 2)
condition to ensure that all matched issues have both labels.
This is more complicated than is ideal. It makes the query construction more prone to errors (such as gitlab-org/gitlab-ce#15557).
Attempt A: WHERE EXISTS
Attempt A1: use multiple subqueries with WHERE EXISTS
In
gitlab-org/gitlab-ce#37137
and its associated merge request
gitlab-org/gitlab-ce!14022,
we tried to replace the GROUP BY
with multiple uses of WHERE EXISTS
. For the
example above, this would give:
WHERE (EXISTS (
SELECT
TRUE
FROM
label_links
INNER JOIN labels ON labels.id = label_links.label_id
WHERE
labels.title = 'Plan'
AND target_type = 'Issue'
AND target_id = issues.id))
AND (EXISTS (
SELECT
TRUE
FROM
label_links
INNER JOIN labels ON labels.id = label_links.label_id
WHERE
labels.title = 'backend'
AND target_type = 'Issue'
AND target_id = issues.id))
While this worked without schema changes, and did improve readability somewhat, it did not improve query performance.
Attempt B: Denormalize using an array column
Having removed MySQL support in GitLab
12.1, using
Postgres's arrays became more
tractable as we didn't have to support two databases. We discussed denormalizing
the label_links
table for querying in
gitlab-org/gitlab-ce#49651,
with two options: label IDs and titles.
We can think of both of those as array columns on issues
, merge_requests
,
and epics
: issues.label_ids
would be an array column of label IDs, and
issues.label_titles
would be an array of label titles.
These array columns can be complemented with GIN indexes to improve matching.
Attempt B1: store label IDs for each object
This has some strong advantages over titles:
- Unless a label is deleted, or a project is moved, we never need to bulk-update the denormalized column.
- It uses less storage than the titles.
Unfortunately, our application design makes this hard. If we were able to query
just by label ID easily, we wouldn't need the INNER JOIN labels
in the initial
query at the start of this document. GitLab allows users to filter by label
title across projects and even across groups, so a filter by the label ~Plan may
include labels with multiple distinct IDs.
We do not want users to have to know about the different IDs, which means that given this data set:
Project | ~Plan label ID | ~backend label ID |
---|---|---|
A | 11 | 12 |
B | 21 | 22 |
C | 31 | 32 |
We would need something like:
WHERE
label_ids @> ARRAY[11, 12]
OR label_ids @> ARRAY[21, 22]
OR label_ids @> ARRAY[31, 32]
This can get even more complicated when we consider that in some cases, there might be two ~backend labels - with different IDs - that could apply to the same object, so the number of combinations would balloon further.
Attempt B2: store label titles for each object
From the perspective of updating the labelable object, this is the worst option. We have to bulk update the objects when:
- The objects are moved from one project to another.
- The project is moved from one group to another.
- The label is renamed.
- The label is deleted.
It also uses much more storage. Querying is simple, though:
WHERE
label_titles @> ARRAY['Plan', 'backend']
And our tests in gitlab-org/gitlab-ce#49651 showed that this could be fast.
However, at present, the disadvantages outweigh the advantages.
Conclusion
We have yet to find a method that is demonstratably better than the current method, when considering:
- Query performance.
- Readability.
- Ease of maintaining schema consistency.