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7adc03dd66 | ||
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01f5c81bc2 |
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@@ -576,12 +576,9 @@ def annotate_document_count_by_ids(
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or CustomFieldInstance)
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related_object_field: field on the relation pointing back to queryset pk
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document_ids: the document ids to count against -- a concrete list/set,
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or a simple (already resolved) queryset of ids. Callers
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that need this filtered by a complex condition (e.g. a
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permission check) should resolve it to a concrete list
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first if the same ids will be reused across multiple
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calls, rather than passing the complex queryset itself
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into each -- see ``_get_selection_data_for_queryset``.
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or a simple (already resolved) queryset of ids. Keep
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the list small: it is embedded in the statement as
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literal parameters.
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target_field: field on the relation pointing to Document id
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"""
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@@ -1141,8 +1141,8 @@ class TestBulkEditAPI(DirectoriesMixin, APITestCase):
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self.assertCountEqual(
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response.data["selected_document_types"],
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[
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{"id": self.c1.id, "document_count": 1},
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{"id": self.c2.id, "document_count": 0},
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{"id": self.dt1.id, "document_count": 1},
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{"id": self.dt2.id, "document_count": 0},
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],
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)
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@@ -49,6 +49,7 @@ from documents.models import WorkflowTrigger
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from documents.search import get_backend
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from documents.signals.handlers import run_workflows
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from documents.tests.utils import ConsumeTaskMixin
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from documents.views import DocumentViewSet
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from paperless_testing.dirs import DirectoriesMixin
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from paperless_testing.factories import DocumentFactory
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from paperless_testing.factories import TagFactory
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@@ -1755,6 +1756,106 @@ class TestDocumentApi(DirectoriesMixin, ConsumeTaskMixin, APITestCase):
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}
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self.assertEqual(counts_by_field[custom_field.id], 1)
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def test_selection_data_counts_with_shared_by_filter(self) -> None:
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"""
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GIVEN:
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- Several documents owned by one user with the same correspondent
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and document type, only one of which is shared
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WHEN:
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- Requesting the document list filtered by shared_by__id with
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include_selection_data=true
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THEN:
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- Counts reflect only the shared document, not every document the
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owner has with that correspondent or document type, however the
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link-table lookups are batched
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"""
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owner = User.objects.create_user(username="owner")
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other = User.objects.create_user(username="other")
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correspondent = Correspondent.objects.create(name="c1")
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doc_type = DocumentType.objects.create(name="dt1")
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tag = Tag.objects.create(name="tag")
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docs = [
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Document.objects.create(
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checksum=str(i),
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owner=owner,
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correspondent=correspondent,
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document_type=doc_type,
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)
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for i in range(3)
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]
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for doc in docs:
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doc.tags.add(tag)
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grant_object(other, docs[0], "view_document")
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for batch_size in (10_000, 1):
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with (
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self.subTest(link_batch_size=batch_size),
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mock.patch.object(
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DocumentViewSet,
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"selection_data_link_batch_size",
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batch_size,
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),
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):
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response = self.client.get(
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f"/api/documents/?shared_by__id={owner.id}"
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"&include_selection_data=true",
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)
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self.assertEqual(response.status_code, status.HTTP_200_OK)
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self.assertEqual(response.data["count"], 1)
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selection_data = response.data["selection_data"]
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for key, obj in (
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("selected_correspondents", correspondent),
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("selected_document_types", doc_type),
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("selected_tags", tag),
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):
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counts = {
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item["id"]: item["document_count"]
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for item in selection_data[key]
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}
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self.assertEqual(counts[obj.id], 1, key)
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def test_selection_data_counts_documents_once_when_filter_joins(self) -> None:
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"""
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GIVEN:
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- A document matching a multi-valued filter through two tags
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WHEN:
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- Requesting the document list with include_selection_data=true
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THEN:
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- The document is counted once per correspondent, not once per
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matching tag, however the link-table lookups are batched
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"""
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correspondent = Correspondent.objects.create(name="c1")
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tag_a = Tag.objects.create(name="a")
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tag_b = Tag.objects.create(name="b")
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both = Document.objects.create(checksum="1", correspondent=correspondent)
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both.tags.add(tag_a, tag_b)
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only_a = Document.objects.create(checksum="2", correspondent=correspondent)
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only_a.tags.add(tag_a)
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for batch_size in (10_000, 1):
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with (
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self.subTest(link_batch_size=batch_size),
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mock.patch.object(
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DocumentViewSet,
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"selection_data_link_batch_size",
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batch_size,
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),
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):
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response = self.client.get(
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f"/api/documents/?tags__id__in={tag_a.id},{tag_b.id}"
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"&include_selection_data=true",
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)
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self.assertEqual(response.status_code, status.HTTP_200_OK)
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counts = {
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item["id"]: item["document_count"]
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for item in response.data["selection_data"][
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"selected_correspondents"
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]
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}
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self.assertEqual(counts[correspondent.id], 2)
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def test_statistics(self) -> None:
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doc1 = Document.objects.create(
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title="none1",
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+75
-63
@@ -5,6 +5,7 @@ import platform
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import re
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import tempfile
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import zipfile
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from collections import Counter
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from collections import deque
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from collections.abc import Callable
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from datetime import datetime
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@@ -168,7 +169,6 @@ from documents.permissions import PaperlessNotePermissions
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from documents.permissions import PaperlessObjectPermissions
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from documents.permissions import TrashPermissions
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from documents.permissions import ViewDocumentsPermissions
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from documents.permissions import annotate_document_count_by_ids
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from documents.permissions import annotate_document_count_for_related_queryset
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from documents.permissions import get_document_count_filter_for_user
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from documents.permissions import get_permission_grantees
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@@ -1022,71 +1022,83 @@ class DocumentViewSet(
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"custom_field_",
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)
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def _get_selection_data_for_queryset(self, queryset):
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# Resolve once instead of once per model below. `queryset` can carry an
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# arbitrarily expensive WHERE clause (user filters plus the permission
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# filter); re-embedding it as a subquery inside 5 separate Count(...)
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# calls forces the database to re-evaluate that whole thing 5 times, and
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# -- for FK relations especially -- can defeat semi-join planning
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# entirely at scale. A concrete id list is cheap to reuse.
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# order_by() drops the default/user ordering -- irrelevant for a plain
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# id list, but left in place it forces a sort over the full filtered
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# set before the ids can even be collected.
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document_ids = list(queryset.order_by().values_list("pk", flat=True))
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# Matching document ids are looked up in the link tables in batches of this
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# size: one literal IN (...) over every match is a multi-MB statement per
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# relation and, on SQLite, exceeds the bound-parameter limit.
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selection_data_link_batch_size = 10_000
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correspondents = Correspondent.objects.annotate(
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document_count=Count(
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"documents",
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filter=Q(documents__id__in=document_ids),
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distinct=True,
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),
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)
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document_types = DocumentType.objects.annotate(
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document_count=Count(
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"documents",
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filter=Q(documents__id__in=document_ids),
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distinct=True,
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),
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)
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storage_paths = StoragePath.objects.annotate(
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document_count=Count(
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"documents",
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filter=Q(documents__id__in=document_ids),
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distinct=True,
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),
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)
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# Tag and CustomField reach Document through an M2M/through-model table;
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# a plain Count(filter=...) there is a much more expensive plan than the
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# FK relations above once the bridge table is large -- see
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# annotate_document_count_by_ids() for why.
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tags = annotate_document_count_by_ids(
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Tag.objects.all(),
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through_model=Document.tags.through,
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related_object_field="tag_id",
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document_ids=document_ids,
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)
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custom_fields = annotate_document_count_by_ids(
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CustomField.objects.all(),
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through_model=CustomFieldInstance,
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related_object_field="field_id",
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document_ids=document_ids,
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def _get_selection_data_for_queryset(self, queryset):
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# The filtered queryset is evaluated once, as the page's own filter is,
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# and never wrapped in a subquery: re-deriving a join or a HAVING
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# (shared_by__id) per statement is very slow on some databases, and
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# regrouping the queryset by another column would miscount.
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# order_by() drops the default/user ordering, which only costs a sort.
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correspondents: Counter = Counter()
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document_types: Counter = Counter()
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storage_paths: Counter = Counter()
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document_ids: list[int] = []
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seen: set[int] = set()
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rows = queryset.order_by().values_list(
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"pk",
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"correspondent_id",
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"document_type_id",
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"storage_path_id",
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)
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for pk, correspondent, document_type, storage_path in rows:
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# A filter can join, repeating a document row.
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if pk in seen:
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continue
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seen.add(pk)
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document_ids.append(pk)
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# Documents without a value count under None, which selection()
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# never looks up.
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correspondents[correspondent] += 1
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document_types[document_type] += 1
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storage_paths[storage_path] += 1
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def link_table_counts(link_model, field_name: str) -> Counter:
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result: Counter = Counter()
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batch_size = self.selection_data_link_batch_size
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for start in range(0, len(document_ids), batch_size):
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result.update(
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dict(
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link_model.objects.filter(
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document_id__in=document_ids[start : start + batch_size],
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)
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.values_list(field_name)
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.annotate(document_count=Count("document_id")),
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),
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)
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return result
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def selection(model, counts: Counter) -> list[dict[str, int]]:
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# Every object is listed, with 0 for those with no matching document.
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return [
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{"id": pk, "document_count": counts.get(pk, 0)}
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for pk in model.objects.values_list("pk", flat=True)
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]
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return {
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"selected_correspondents": [
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{"id": t.id, "document_count": t.document_count} for t in correspondents
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],
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"selected_tags": [
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{"id": t.id, "document_count": t.document_count} for t in tags
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],
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"selected_document_types": [
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{"id": t.id, "document_count": t.document_count} for t in document_types
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],
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"selected_storage_paths": [
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{"id": t.id, "document_count": t.document_count} for t in storage_paths
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],
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"selected_custom_fields": [
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{"id": t.id, "document_count": t.document_count} for t in custom_fields
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],
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"selected_correspondents": selection(
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Correspondent,
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correspondents,
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),
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"selected_tags": selection(
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Tag,
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link_table_counts(Document.tags.through, "tag_id"),
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),
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"selected_document_types": selection(
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DocumentType,
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document_types,
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),
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"selected_storage_paths": selection(
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StoragePath,
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storage_paths,
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),
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"selected_custom_fields": selection(
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CustomField,
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link_table_counts(CustomFieldInstance, "field_id"),
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),
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}
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@classmethod
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