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Trenton HandClaude Sonnet 5.5 7adc03dd66 Chore: use document type ids in the bulk edit selection_data test
Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
2026-10-30 14:08:37 -07:00
Trenton HandClaude Sonnet 5.5 01f5c81bc2 Fix: count selection_data from one pass over the filtered documents
Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
2026-10-30 14:08:37 -07:00
4 changed files with 180 additions and 70 deletions

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+3 -6
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@@ -576,12 +576,9 @@ def annotate_document_count_by_ids(
or CustomFieldInstance) or CustomFieldInstance)
related_object_field: field on the relation pointing back to queryset pk related_object_field: field on the relation pointing back to queryset pk
document_ids: the document ids to count against -- a concrete list/set, document_ids: the document ids to count against -- a concrete list/set,
or a simple (already resolved) queryset of ids. Callers or a simple (already resolved) queryset of ids. Keep
that need this filtered by a complex condition (e.g. a the list small: it is embedded in the statement as
permission check) should resolve it to a concrete list literal parameters.
first if the same ids will be reused across multiple
calls, rather than passing the complex queryset itself
into each -- see ``_get_selection_data_for_queryset``.
target_field: field on the relation pointing to Document id target_field: field on the relation pointing to Document id
""" """
+2 -2
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@@ -1141,8 +1141,8 @@ class TestBulkEditAPI(DirectoriesMixin, APITestCase):
self.assertCountEqual( self.assertCountEqual(
response.data["selected_document_types"], response.data["selected_document_types"],
[ [
{"id": self.c1.id, "document_count": 1}, {"id": self.dt1.id, "document_count": 1},
{"id": self.c2.id, "document_count": 0}, {"id": self.dt2.id, "document_count": 0},
], ],
) )
+101
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@@ -49,6 +49,7 @@ from documents.models import WorkflowTrigger
from documents.search import get_backend from documents.search import get_backend
from documents.signals.handlers import run_workflows from documents.signals.handlers import run_workflows
from documents.tests.utils import ConsumeTaskMixin from documents.tests.utils import ConsumeTaskMixin
from documents.views import DocumentViewSet
from paperless_testing.dirs import DirectoriesMixin from paperless_testing.dirs import DirectoriesMixin
from paperless_testing.factories import DocumentFactory from paperless_testing.factories import DocumentFactory
from paperless_testing.factories import TagFactory from paperless_testing.factories import TagFactory
@@ -1755,6 +1756,106 @@ class TestDocumentApi(DirectoriesMixin, ConsumeTaskMixin, APITestCase):
} }
self.assertEqual(counts_by_field[custom_field.id], 1) self.assertEqual(counts_by_field[custom_field.id], 1)
def test_selection_data_counts_with_shared_by_filter(self) -> None:
"""
GIVEN:
- Several documents owned by one user with the same correspondent
and document type, only one of which is shared
WHEN:
- Requesting the document list filtered by shared_by__id with
include_selection_data=true
THEN:
- Counts reflect only the shared document, not every document the
owner has with that correspondent or document type, however the
link-table lookups are batched
"""
owner = User.objects.create_user(username="owner")
other = User.objects.create_user(username="other")
correspondent = Correspondent.objects.create(name="c1")
doc_type = DocumentType.objects.create(name="dt1")
tag = Tag.objects.create(name="tag")
docs = [
Document.objects.create(
checksum=str(i),
owner=owner,
correspondent=correspondent,
document_type=doc_type,
)
for i in range(3)
]
for doc in docs:
doc.tags.add(tag)
grant_object(other, docs[0], "view_document")
for batch_size in (10_000, 1):
with (
self.subTest(link_batch_size=batch_size),
mock.patch.object(
DocumentViewSet,
"selection_data_link_batch_size",
batch_size,
),
):
response = self.client.get(
f"/api/documents/?shared_by__id={owner.id}"
"&include_selection_data=true",
)
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertEqual(response.data["count"], 1)
selection_data = response.data["selection_data"]
for key, obj in (
("selected_correspondents", correspondent),
("selected_document_types", doc_type),
("selected_tags", tag),
):
counts = {
item["id"]: item["document_count"]
for item in selection_data[key]
}
self.assertEqual(counts[obj.id], 1, key)
def test_selection_data_counts_documents_once_when_filter_joins(self) -> None:
"""
GIVEN:
- A document matching a multi-valued filter through two tags
WHEN:
- Requesting the document list with include_selection_data=true
THEN:
- The document is counted once per correspondent, not once per
matching tag, however the link-table lookups are batched
"""
correspondent = Correspondent.objects.create(name="c1")
tag_a = Tag.objects.create(name="a")
tag_b = Tag.objects.create(name="b")
both = Document.objects.create(checksum="1", correspondent=correspondent)
both.tags.add(tag_a, tag_b)
only_a = Document.objects.create(checksum="2", correspondent=correspondent)
only_a.tags.add(tag_a)
for batch_size in (10_000, 1):
with (
self.subTest(link_batch_size=batch_size),
mock.patch.object(
DocumentViewSet,
"selection_data_link_batch_size",
batch_size,
),
):
response = self.client.get(
f"/api/documents/?tags__id__in={tag_a.id},{tag_b.id}"
"&include_selection_data=true",
)
self.assertEqual(response.status_code, status.HTTP_200_OK)
counts = {
item["id"]: item["document_count"]
for item in response.data["selection_data"][
"selected_correspondents"
]
}
self.assertEqual(counts[correspondent.id], 2)
def test_statistics(self) -> None: def test_statistics(self) -> None:
doc1 = Document.objects.create( doc1 = Document.objects.create(
title="none1", title="none1",
+74 -62
View File
@@ -5,6 +5,7 @@ import platform
import re import re
import tempfile import tempfile
import zipfile import zipfile
from collections import Counter
from collections import deque from collections import deque
from collections.abc import Callable from collections.abc import Callable
from datetime import datetime from datetime import datetime
@@ -168,7 +169,6 @@ from documents.permissions import PaperlessNotePermissions
from documents.permissions import PaperlessObjectPermissions from documents.permissions import PaperlessObjectPermissions
from documents.permissions import TrashPermissions from documents.permissions import TrashPermissions
from documents.permissions import ViewDocumentsPermissions from documents.permissions import ViewDocumentsPermissions
from documents.permissions import annotate_document_count_by_ids
from documents.permissions import annotate_document_count_for_related_queryset from documents.permissions import annotate_document_count_for_related_queryset
from documents.permissions import get_document_count_filter_for_user from documents.permissions import get_document_count_filter_for_user
from documents.permissions import get_permission_grantees from documents.permissions import get_permission_grantees
@@ -1022,71 +1022,83 @@ class DocumentViewSet(
"custom_field_", "custom_field_",
) )
def _get_selection_data_for_queryset(self, queryset): # Matching document ids are looked up in the link tables in batches of this
# Resolve once instead of once per model below. `queryset` can carry an # size: one literal IN (...) over every match is a multi-MB statement per
# arbitrarily expensive WHERE clause (user filters plus the permission # relation and, on SQLite, exceeds the bound-parameter limit.
# filter); re-embedding it as a subquery inside 5 separate Count(...) selection_data_link_batch_size = 10_000
# calls forces the database to re-evaluate that whole thing 5 times, and
# -- for FK relations especially -- can defeat semi-join planning
# entirely at scale. A concrete id list is cheap to reuse.
# order_by() drops the default/user ordering -- irrelevant for a plain
# id list, but left in place it forces a sort over the full filtered
# set before the ids can even be collected.
document_ids = list(queryset.order_by().values_list("pk", flat=True))
correspondents = Correspondent.objects.annotate( def _get_selection_data_for_queryset(self, queryset):
document_count=Count( # The filtered queryset is evaluated once, as the page's own filter is,
"documents", # and never wrapped in a subquery: re-deriving a join or a HAVING
filter=Q(documents__id__in=document_ids), # (shared_by__id) per statement is very slow on some databases, and
distinct=True, # regrouping the queryset by another column would miscount.
# order_by() drops the default/user ordering, which only costs a sort.
correspondents: Counter = Counter()
document_types: Counter = Counter()
storage_paths: Counter = Counter()
document_ids: list[int] = []
seen: set[int] = set()
rows = queryset.order_by().values_list(
"pk",
"correspondent_id",
"document_type_id",
"storage_path_id",
)
for pk, correspondent, document_type, storage_path in rows:
# A filter can join, repeating a document row.
if pk in seen:
continue
seen.add(pk)
document_ids.append(pk)
# Documents without a value count under None, which selection()
# never looks up.
correspondents[correspondent] += 1
document_types[document_type] += 1
storage_paths[storage_path] += 1
def link_table_counts(link_model, field_name: str) -> Counter:
result: Counter = Counter()
batch_size = self.selection_data_link_batch_size
for start in range(0, len(document_ids), batch_size):
result.update(
dict(
link_model.objects.filter(
document_id__in=document_ids[start : start + batch_size],
)
.values_list(field_name)
.annotate(document_count=Count("document_id")),
), ),
) )
document_types = DocumentType.objects.annotate( return result
document_count=Count(
"documents", def selection(model, counts: Counter) -> list[dict[str, int]]:
filter=Q(documents__id__in=document_ids), # Every object is listed, with 0 for those with no matching document.
distinct=True, return [
), {"id": pk, "document_count": counts.get(pk, 0)}
) for pk in model.objects.values_list("pk", flat=True)
storage_paths = StoragePath.objects.annotate( ]
document_count=Count(
"documents",
filter=Q(documents__id__in=document_ids),
distinct=True,
),
)
# Tag and CustomField reach Document through an M2M/through-model table;
# a plain Count(filter=...) there is a much more expensive plan than the
# FK relations above once the bridge table is large -- see
# annotate_document_count_by_ids() for why.
tags = annotate_document_count_by_ids(
Tag.objects.all(),
through_model=Document.tags.through,
related_object_field="tag_id",
document_ids=document_ids,
)
custom_fields = annotate_document_count_by_ids(
CustomField.objects.all(),
through_model=CustomFieldInstance,
related_object_field="field_id",
document_ids=document_ids,
)
return { return {
"selected_correspondents": [ "selected_correspondents": selection(
{"id": t.id, "document_count": t.document_count} for t in correspondents Correspondent,
], correspondents,
"selected_tags": [ ),
{"id": t.id, "document_count": t.document_count} for t in tags "selected_tags": selection(
], Tag,
"selected_document_types": [ link_table_counts(Document.tags.through, "tag_id"),
{"id": t.id, "document_count": t.document_count} for t in document_types ),
], "selected_document_types": selection(
"selected_storage_paths": [ DocumentType,
{"id": t.id, "document_count": t.document_count} for t in storage_paths document_types,
], ),
"selected_custom_fields": [ "selected_storage_paths": selection(
{"id": t.id, "document_count": t.document_count} for t in custom_fields StoragePath,
], storage_paths,
),
"selected_custom_fields": selection(
CustomField,
link_table_counts(CustomFieldInstance, "field_id"),
),
} }
@classmethod @classmethod