chore: remove benchmark-commands spec/plan docs now that work is complete

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

.claude/skills/paperless-benchmarking/SKILL.md

@@ -0,0 +1,187 @@
+---
+name: paperless-benchmarking
+description: Use when profiling paperless-ngx performance, running `manage.py benchmark`, investigating a slow query or endpoint, or deciding whether a profiling finding should become a permanent registered scenario. Covers command reference, the fork/merge-back branch workflow for perf investigations, and how to read query-plan output.
+---
+
+# Paperless-ngx Benchmarking
+
+This repo has a built-in benchmarking/profiling tool: `manage.py benchmark`, in
+the `paperless_benchmark` Django app. It replaces ad hoc standalone scripts —
+use it instead of writing new one-off seed/timing scripts.
+
+## Command reference
+
+```
+manage.py benchmark seed --tier {home,medium,large} [--reset --yes-i-know-this-wipes-the-database] [--seed N]
+manage.py benchmark run --repeat 5 [--label baseline]
+manage.py benchmark profile <scenario_name> [--repeat 5] [--explain]
+manage.py benchmark list-scenarios
+```
+
+- **`seed`** builds a realistic dataset at one of three scales: `home` (500
+  documents — fast, use this for iteration), `medium` (20,000 — the default
+  when `--tier` is omitted; a multi-minute seed), `large` (360,000 — matches
+  the scale reported in real large-install bug reports; slow, only use it
+  when a finding needs confirming at real scale). `--reset` wipes any
+  previously-seeded benchmark data first — but it is destructive and
+  irreversible: it deletes **all** users, **all** groups, and **all**
+  documents/tags/correspondents/document types/storage paths in the target
+  database, not just benchmark-created rows. Only run it against a disposable
+  benchmark database, never a real install. Because of that, `--reset` also
+  requires passing `--yes-i-know-this-wipes-the-database` in the same
+  invocation, or the command raises an error and does nothing. Omit both
+  flags if you want to layer more data onto an existing seed instead. `seed`
+  creates two named users, `perf_target` (mixed owned/shared documents,
+  realistic guardian permission grants) and `perf_admin` (superuser), plus a
+  general user/group pool with realistic permission-row ratios.
+- **`run`** times the 3 built-in API endpoint benchmarks
+  (`/api/documents/`, `/api/documents/?page_size=50`, `/api/tags/?page_size=100000`) for both
+  `perf_target` and `perf_admin`, reporting min/median/max wall-clock and SQL
+  query count. Requires `seed` to have already run — it reuses that data, it
+  does not seed its own.
+- **`profile`** times one named scenario from the registry (see
+  `list-scenarios`) via best-of-N repeat timing and SQL query count, against
+  `perf_target`. `--explain` additionally captures and prints the query plan:
+  real `EXPLAIN ANALYZE` execution stats on PostgreSQL/MariaDB, or
+  `EXPLAIN QUERY PLAN` (plan only, no real timing/row counts — clearly labeled
+  as such) on SQLite. Like `run`, `profile` requires `seed` to have already
+  run — it does not seed its own data either.
+- Every `run`/`profile` invocation appends a JSON line to
+  `benchmark_results/history.jsonl` at the repo root (local-only, gitignored —
+  never commit this file). Use it to compare a `before`/`after` pair across
+  two invocations without hand-copying numbers.
+- Full chain example: `seed --reset` once, then `run` and `profile` as many
+  times as you want against that same seeded data — no need to reseed between
+  them.
+
+## Adding a new scenario
+
+A "scenario" is a named, registered query/operation that `profile` can time
+and explain. To add one, edit `src/paperless_benchmark/scenarios.py`: write a
+`_<name>_run(user)` function (returns whatever `run_profile` should time) and
+optionally a `_<name>_queryset(user)` function (returns the `QuerySet` for
+`--explain` to analyze), then `register(Scenario(name=..., describe=...,
+run=..., queryset_for_explain=...))` at module level. Both functions receive
+the already-seeded `perf_target` user — they should not seed their own data.
+
+## Branch workflow
+
+`tools/benchmark-management-commands` is a **long-lived tooling branch**, not
+a feature branch that gets merged and closed:
+
+1. It is periodically brought up to date with `dev` (merge `dev` into it) so
+   the tooling doesn't drift from the schema/codebase it profiles. Do this
+   before starting a new investigation if it's been a while since the last
+   sync.
+2. **Every performance investigation forks its own branch from
+   `tools/benchmark-management-commands`** (not from `dev`). Do the
+   investigation there: write throwaway profiling code, try fixes, capture
+   before/after numbers.
+3. **That investigation branch never merges into `dev` or production.** Its
+   only job is to produce evidence and, optionally, a reusable scenario.
+4. If the investigation turns up a scenario worth keeping permanently (see
+   "When to graduate a scenario" below), open a PR that adds **just that
+   scenario** back into `tools/benchmark-management-commands` — not the rest
+   of the investigation branch's throwaway code.
+5. Any actual production fix the investigation motivates (e.g. an ORM query
+   change) goes into its own normal feature branch off `dev`, following the
+   project's regular contribution process — profiling evidence informs that
+   PR's description, but the profiling code itself does not travel with it.
+
+## Reading query-plan output
+
+- **PostgreSQL** `EXPLAIN ANALYZE`: look for `Seq Scan` on a large table
+  (missing index), a large gap between `rows=N` (planner's estimate) and the
+  actual row count in parentheses (stale statistics or a bad cardinality
+  estimate), and nested-loop joins driven by an outer relation with many
+  rows (usually the N+1 pattern this tool exists to catch).
+- **MariaDB**: verified against a real MariaDB 12.3 container that MariaDB
+  does NOT accept MySQL 8.0.18+'s `EXPLAIN ANALYZE` syntax (it's a 1064
+  syntax error) -- `capture_explain()` instead runs MariaDB's own
+  `ANALYZE <statement>` form (no `EXPLAIN` keyword), which returns a
+  tabular plan with real per-row execution columns: `rows` (estimate) vs.
+  `r_rows` (actual), and `filtered` vs. `r_filtered`. A large gap between
+  `rows` and `r_rows`, or `type: ALL` (full table scan) on a large table,
+  are the signals to look for -- the same underlying concerns as Postgres's
+  `Seq Scan`/estimate-vs-actual gap, just in MariaDB's column-based output
+  instead of Postgres's nested-tree text format.
+- **SQLite** `EXPLAIN QUERY PLAN`: no real timing/row-count data, only the
+  chosen access path (`SCAN` vs `SEARCH`, which index if any). Useful for
+  confirming an index is even being considered, not for judging real-world
+  cost — corroborate any SQLite finding against Postgres/MariaDB before
+  trusting it, since planner behavior differs meaningfully between them.
+- Compare query **count**, not just timing, between before/after: a fix that
+  keeps the same wall-clock time but drops query count from O(n) to O(1) is
+  still a real, durable improvement — timing alone is noisy and
+  environment-dependent, query count is not.
+
+## Cleaning up after an interrupted run
+
+If a `seed`/`run`/`profile` invocation gets killed mid-run (Ctrl-C, `kill -9`,
+a timed-out SSH session, etc.), check whether it left anything behind before
+trusting the next benchmark's numbers. This was verified for real: a
+`benchmark seed --tier large --reset ...` was started against both a fresh
+PostgreSQL 18 container and a fresh MariaDB 12.3 container and `kill -9`'d a
+few seconds into document seeding. In both cases, the database-side
+connection disappeared immediately -- no stuck backend, no lingering query,
+no held lock was observed in either backend once the killed process's PID
+was confirmed gone. That said, this was one interruption point (mid
+bulk-seed, between chunks); a run killed mid-query, or a driver/network
+hiccup that doesn't cleanly close the socket, could behave differently, so
+still check before trusting a number if any run in the session was
+interrupted:
+
+- **PostgreSQL**: look for leftover connections against the benchmark
+  database:
+
+  ```sql
+  SELECT pid, state, query, query_start
+  FROM pg_stat_activity
+  WHERE datname = current_database() AND pid <> pg_backend_pid();
+  ```
+
+  If a stuck backend shows up, clear it with:
+
+  ```sql
+  SELECT pg_terminate_backend(pid)
+  FROM pg_stat_activity
+  WHERE datname = current_database() AND pid <> pg_backend_pid();
+  ```
+
+- **MariaDB**: look for leftover connections/queries:
+
+  ```sql
+  SHOW FULL PROCESSLIST;
+  ```
+
+  If a stuck connection shows up (anything other than your current admin
+  session), clear it with:
+
+  ```sql
+  KILL <id>;
+  ```
+
+A stray connection left running concurrently with a subsequent benchmark run
+would add real, contaminating load (extra queries competing for the same
+rows, possibly held locks slowing the next run's timings) -- cheap to rule
+out, expensive to silently trust a number that was actually measured
+alongside a zombie connection.
+
+## When to graduate a one-off finding into a permanent scenario
+
+Register a scenario (rather than leaving it as throwaway code on the
+investigation branch) when **both** are true:
+
+- The query pattern is one this codebase is likely to regress on again (e.g.
+  it involves a permission-check join, a bulk operation, or anything else
+  with an easy-to-reintroduce N+1) — not a one-time fluke specific to this
+  investigation.
+- Re-running it later, against a fresh seed, would still produce a
+  meaningful signal (it doesn't depend on investigation-specific throwaway
+  data or a fix that's already permanently landed and can't regress the same
+  way).
+
+If a finding doesn't meet both bars, keep it as disposable code on the
+investigation branch and let the branch's evidence (captured in the PR
+description of whatever production fix it motivates) be the permanent
+record instead.

.gitignore

@@ -115,3 +115,6 @@ celerybeat-schedule*
 
 # Git worktree local folder
 .worktrees
+
+# Benchmark tooling output (local only, never committed)
+/benchmark_results/

docs/configuration.md

@@ -948,11 +948,10 @@ for display in the web interface.
 
     !!! note
 
-        The **remote OCR parser** (Azure AI) also honors this setting: when
-        no archive is requested (`never`, or `auto` with a born-digital PDF),
-        the remote engine is skipped entirely and locally-extracted text is
-        used instead, avoiding an unnecessary API call and a duplicate text
-        layer.
+        The **remote OCR parser** (Azure AI) always produces a searchable
+        PDF and stores it as the archive copy, regardless of this setting.
+        `ARCHIVE_FILE_GENERATION=never` has no effect when the remote
+        parser handles a document.
 
 #### [`PAPERLESS_OCR_CLEAN=<mode>`](#PAPERLESS_OCR_CLEAN) {#PAPERLESS_OCR_CLEAN}
 

docs/migration-v3.md

@@ -187,11 +187,10 @@ PAPERLESS_ARCHIVE_FILE_GENERATION=auto
 
 ### Remote OCR parser
 
-If you use the **remote OCR parser** (Azure AI), `ARCHIVE_FILE_GENERATION` is
-honored the same way as for the local engine: when no archive is requested
-(`never`, or `auto` with a born-digital PDF), the remote engine is skipped
-entirely and locally-extracted text is used instead, avoiding an unnecessary
-API call and a duplicate text layer.
+If you use the **remote OCR parser** (Azure AI), note that it always produces a
+searchable PDF and stores it as the archive copy. `ARCHIVE_FILE_GENERATION=never`
+has no effect for documents handled by the remote parser - the archive is produced
+unconditionally by the remote engine.
 
 ## Search Index (Whoosh -> Tantivy)
 

src-ui/src/app/components/common/pdf-viewer/pdf-viewer.component.spec.ts

@@ -129,13 +129,25 @@ describe('PngxPdfViewerComponent', () => {
     ;(component as any).applyScale()
     expect(viewer.currentScaleValue).toBe(PdfZoomScale.PageFit)
     expect(viewer.currentScale).toBe(2)
+  })
 
+  it('does not reapply scale for page-only changes', async () => {
+    await initComponent()
+
+    const pdf = (component as any).pdf as { numPages: number }
+    pdf.numPages = 3
+    const viewer = (component as any).pdfViewer as PDFViewer
+    viewer.setDocument(pdf)
     const applyScaleSpy = jest.spyOn(component as any, 'applyScale')
     component.page = 2
-    ;(component as any).lastViewerPage = 2
-    ;(component as any).applyViewerState()
+
+    component.ngOnChanges({
+      page: new SimpleChange(1, 2, false),
+    })
+
+    expect(viewer.currentPageNumber).toBe(2)
     expect((component as any).lastViewerPage).toBeUndefined()
-    expect(applyScaleSpy).toHaveBeenCalled()
+    expect(applyScaleSpy).not.toHaveBeenCalled()
   })
 
   it('does not reset the viewer when it is already on the requested page', async () => {

src-ui/src/app/components/common/pdf-viewer/pdf-viewer.component.ts

@@ -116,7 +116,10 @@ export class PngxPdfViewerComponent
       changes['zoomScale'] ||
       changes['rotation']
     ) {
-      this.applyViewerState()
+      // Prevent loop with page / scale application see https://github.com/paperless-ngx/paperless-ngx/issues/13404
+      this.applyViewerState(
+        !!(changes['zoom'] || changes['zoomScale'] || changes['rotation'])
+      )
     }
 
     if (changes['searchQuery']) {
@@ -240,7 +243,7 @@ export class PngxPdfViewerComponent
     }
   }
 
-  private applyViewerState(): void {
+  private applyViewerState(applyScale = true): void {
     if (!this.pdfViewer) {
       return
     }
@@ -264,7 +267,7 @@ export class PngxPdfViewerComponent
     if (this.page === this.lastViewerPage) {
       this.lastViewerPage = undefined
     }
-    if (hasPages) {
+    if (hasPages && applyScale) {
       this.applyScale()
     }
     this.dispatchFindIfReady()

src/documents/mail.py

@@ -36,6 +36,9 @@ def send_email(
 
     TODO: re-evaluate this pending https://code.djangoproject.com/ticket/35581 / https://github.com/django/django/pull/18966
     """
+    if "\r" in subject or "\n" in subject:
+        subject = " ".join(line.strip(" \t") for line in subject.splitlines())
+
     email = EmailMessage(
         subject=subject,
         body=body,

src/documents/management/commands/document_importer.py

@@ -386,10 +386,19 @@ class Command(CryptMixin, PaperlessCommand):
                 raise DeserializationError(
                     f"{model.__name__} has no updatable fields; PK-only models are not supported by the importer",
                 )
+            # MySQL/MariaDB support upserts via ON DUPLICATE KEY UPDATE but,
+            # unlike PostgreSQL/SQLite, cannot target a specific unique field
+            # for the conflict -- passing unique_fields there raises
+            # NotSupportedError.
+            unique_fields = (
+                [model._meta.pk.attname]
+                if connection.features.supports_update_conflicts_with_target
+                else None
+            )
             model.objects.bulk_create(  # type: ignore[attr-defined]
                 instances,
                 update_conflicts=True,
-                unique_fields=[model._meta.pk.attname],
+                unique_fields=unique_fields,
                 update_fields=update_fields,
             )
             loaded_models.add(model)

src/documents/tests/test_api_email.py

@@ -75,7 +75,7 @@ class TestEmail(DirectoriesMixin, SampleDirMixin, APITestCase):
                 {
                     "documents": [self.doc1.pk, self.doc2.pk],
                     "addresses": "hello@paperless-ngx.com,test@example.com",
-                    "subject": "Bulk email test",
+                    "subject": "Bulk email\n test",
                     "message": "Here are your documents",
                 },
             ),

src/paperless/parsers/remote.py

@@ -3,9 +3,7 @@ Built-in remote-OCR document parser.
 
 Handles documents by sending them to a configured remote OCR engine
 (currently Azure AI Vision / Document Intelligence) and retrieving both
-the extracted text and a searchable PDF with an embedded text layer. For
-born-digital PDFs that need no archive copy, the remote call is skipped
-entirely in favor of locally-extracted text (see ``RemoteDocumentParser.parse``).
+the extracted text and a searchable PDF with an embedded text layer.
 
 When no engine is configured, ``score()`` returns ``None`` so the parser
 is effectively invisible to the registry — the tesseract parser handles
@@ -23,8 +21,6 @@ from typing import Self
 
 from django.conf import settings
 
-from paperless.parsers.utils import extract_pdf_text
-from paperless.parsers.utils import post_process_text
 from paperless.version import __full_version_str__
 
 if TYPE_CHECKING:
@@ -73,11 +69,8 @@ class RemoteDocumentParser:
     """Parse documents via a remote OCR API (currently Azure AI Vision).
 
     This parser sends documents to a remote engine that returns both
-    extracted text and a searchable PDF with an embedded text layer,
-    except when ``parse()`` is called with ``produce_archive=False`` for
-    a PDF, in which case the remote call is skipped and only locally
-    extracted text is returned (no archive). It does not depend on
-    Tesseract or ocrmypdf.
+    extracted text and a searchable PDF with an embedded text layer.
+    It does not depend on Tesseract or ocrmypdf.
 
     Class attributes
     ----------------
@@ -166,11 +159,8 @@ class RemoteDocumentParser:
         Returns
         -------
         bool
-            Always True — the remote engine is capable of returning a PDF
-            with an embedded text layer to serve as the archive copy.
-            Whether it actually does so for a given document depends on
-            ``produce_archive`` passed to :meth:`parse` (see there for when
-            the remote engine call, and thus archive generation, is skipped).
+            Always True — the remote engine always returns a PDF with an
+            embedded text layer that serves as the archive copy.
         """
         return True
 
@@ -227,12 +217,6 @@ class RemoteDocumentParser:
     ) -> None:
         """Send the document to the remote engine and store results.
 
-        When *produce_archive* is False for a PDF, the caller (via
-        ``documents.consumer.should_produce_archive``) has already determined
-        that the document is born-digital and needs no archive — skip the
-        remote engine entirely rather than re-OCRing it and creating a
-        duplicate text layer.
-
         Parameters
         ----------
         document_path:
@@ -240,8 +224,8 @@ class RemoteDocumentParser:
         mime_type:
             Detected MIME type of the document.
         produce_archive:
-            Whether an archive copy is wanted. For PDFs, False skips the
-            remote engine and uses locally-extracted text instead.
+            Ignored — the remote engine always returns a searchable PDF,
+            which is stored as the archive copy regardless of this flag.
         """
         config = RemoteEngineConfig(
             engine=settings.REMOTE_OCR_ENGINE,
@@ -256,16 +240,6 @@ class RemoteDocumentParser:
             self._text = ""
             return
 
-        if not produce_archive and mime_type == "application/pdf":
-            logger.debug(
-                "Remote OCR: skipped — no archive requested, "
-                "using locally-extracted text",
-            )
-            self._text = (
-                post_process_text(extract_pdf_text(document_path, log=logger)) or ""
-            )
-            return
-
         if config.engine == "azureai":
             self._text = self._azure_ai_vision_parse(document_path, config)
 

src/paperless/parsers/tesseract.py

@@ -3,6 +3,7 @@ from __future__ import annotations
 import importlib.resources
 import logging
 import os
+import re
 import shutil
 import tempfile
 from pathlib import Path
@@ -24,9 +25,9 @@ from paperless.config import OcrConfig
 from paperless.models import CleanChoices
 from paperless.models import ModeChoices
 from paperless.models import OutputTypeChoices
+from paperless.parsers.utils import PDF_TEXT_MIN_LENGTH
 from paperless.parsers.utils import extract_pdf_text
-from paperless.parsers.utils import pdf_has_digital_text
-from paperless.parsers.utils import post_process_text
+from paperless.parsers.utils import is_tagged_pdf
 from paperless.parsers.utils import read_file_handle_unicode_errors
 from paperless.version import __full_version_str__
 
@@ -509,10 +510,10 @@ class RasterisedDocumentParser:
 
         if mime_type == "application/pdf":
             text_original = self.extract_text(None, document_path)
-            original_has_text = pdf_has_digital_text(
-                document_path,
-                text_original,
-                log=self.log,
+            has_text = text_original is not None and len(text_original) > 0
+            original_has_text = has_text and (
+                is_tagged_pdf(document_path, log=self.log)
+                or len(text_original) > PDF_TEXT_MIN_LENGTH
             )
         else:
             text_original = None
@@ -657,3 +658,17 @@ class RasterisedDocumentParser:
                     f"No text was found in {document_path}, the content will be empty.",
                 )
                 self.text = ""
+
+
+def post_process_text(text: str | None) -> str | None:
+    if not text:
+        return None
+
+    collapsed_spaces = re.sub(r"([^\S\r\n]+)", " ", text)
+    no_leading_whitespace = re.sub(r"([\n\r]+)([^\S\n\r]+)", "\\1", collapsed_spaces)
+    no_trailing_whitespace = re.sub(r"([^\S\n\r]+)$", "", no_leading_whitespace)
+
+    # TODO: this needs a rework
+    # replace \0 prevents issues with saving to postgres.
+    # text may contain \0 when this character is present in PDF files.
+    return no_trailing_whitespace.strip().replace("\0", " ")

src/paperless/parsers/utils.py

@@ -65,63 +65,6 @@ def is_tagged_pdf(
         return False
 
 
-def pdf_has_digital_text(
-    path: Path,
-    text: str | None,
-    log: logging.Logger | None = None,
-) -> bool:
-    """Return True if a PDF already has a usable, born-digital text layer.
-
-    Combines the tagged-PDF check with an extracted-text length check.
-    Shared by the tesseract and remote OCR parsers to decide whether
-    OCR_MODE=auto/off should skip (re-)OCRing a document.
-
-    Parameters
-    ----------
-    path:
-        Absolute path to the PDF file.
-    text:
-        Text already extracted from the PDF (e.g. via ``extract_pdf_text``),
-        or ``None``.
-    log:
-        Logger for warnings.  Falls back to the module-level logger when omitted.
-
-    Returns
-    -------
-    bool
-        ``True`` when the document already contains a text layer.
-    """
-    return is_tagged_pdf(path, log=log) or (
-        text is not None and len(text) > PDF_TEXT_MIN_LENGTH
-    )
-
-
-def post_process_text(text: str | None) -> str | None:
-    """Normalise whitespace in extracted OCR/PDF text and strip NUL bytes.
-
-    Parameters
-    ----------
-    text:
-        Raw extracted text, or ``None``.
-
-    Returns
-    -------
-    str | None
-        Cleaned text, or ``None`` when *text* is falsy.
-    """
-    if not text:
-        return None
-
-    collapsed_spaces = re.sub(r"([^\S\r\n]+)", " ", text)
-    no_leading_whitespace = re.sub(r"([\n\r]+)([^\S\n\r]+)", "\\1", collapsed_spaces)
-    no_trailing_whitespace = re.sub(r"([^\S\n\r]+)$", "", no_leading_whitespace)
-
-    # TODO: this needs a rework
-    # replace \0 prevents issues with saving to postgres.
-    # text may contain \0 when this character is present in PDF files.
-    return no_trailing_whitespace.strip().replace("\0", " ")
-
-
 def extract_pdf_text(
     path: Path,
     log: logging.Logger | None = None,

src/paperless/settings/__init__.py

@@ -150,6 +150,7 @@ INSTALLED_APPS = [
     "drf_spectacular",
     "drf_spectacular_sidecar",
     "treenode",
+    "paperless_benchmark.apps.PaperlessBenchmarkConfig",
     *env_apps,
 ]
 

src/paperless/tests/parsers/test_remote_parser.py

@@ -336,117 +336,6 @@ class TestRemoteParserParse:
         assert remote_parser.get_date() is None
 
 
-# ---------------------------------------------------------------------------
-# parse() — produce_archive=False skips the remote engine (PDFs only)
-# ---------------------------------------------------------------------------
-
-
-class TestRemoteParserSkipsWhenNoArchiveWanted:
-    """When the caller has already decided no archive is needed for a PDF
-    (documents.consumer.should_produce_archive), the remote engine call is
-    skipped entirely in favor of locally-extracted text.
-    """
-
-    def test_pdf_skips_azure_when_no_archive_requested(
-        self,
-        remote_parser: RemoteDocumentParser,
-        simple_digital_pdf_file: Path,
-        azure_client: Mock,
-    ) -> None:
-        """
-        GIVEN: produce_archive=False for a PDF
-        WHEN:  parse() is called
-        THEN:  Azure is never invoked, no archive is produced, and text
-               comes from local pdftotext extraction
-        """
-        remote_parser.parse(
-            simple_digital_pdf_file,
-            "application/pdf",
-            produce_archive=False,
-        )
-
-        azure_client.begin_analyze_document.assert_not_called()
-        assert remote_parser.get_archive_path() is None
-        assert remote_parser.get_text() != ""
-
-    def test_pdf_no_archive_requested_text_matches_local_extraction(
-        self,
-        remote_parser: RemoteDocumentParser,
-        simple_digital_pdf_file: Path,
-        azure_client: Mock,
-        mocker: MockerFixture,
-    ) -> None:
-        """
-        GIVEN: produce_archive=False for a PDF
-        WHEN:  parse() is called
-        THEN:  the returned text is exactly the locally-extracted text,
-               not anything from the (unused) Azure mock
-        """
-        mocker.patch(
-            "paperless.parsers.remote.extract_pdf_text",
-            return_value="Local digital text.",
-        )
-
-        remote_parser.parse(
-            simple_digital_pdf_file,
-            "application/pdf",
-            produce_archive=False,
-        )
-
-        assert remote_parser.get_text() == "Local digital text."
-
-    def test_pdf_no_archive_requested_closes_no_client(
-        self,
-        remote_parser: RemoteDocumentParser,
-        simple_digital_pdf_file: Path,
-        azure_client: Mock,
-    ) -> None:
-        remote_parser.parse(
-            simple_digital_pdf_file,
-            "application/pdf",
-            produce_archive=False,
-        )
-
-        azure_client.close.assert_not_called()
-
-    def test_non_pdf_still_calls_azure_when_no_archive_requested(
-        self,
-        remote_parser: RemoteDocumentParser,
-        simple_digital_pdf_file: Path,
-        azure_client: Mock,
-    ) -> None:
-        """
-        Images have no local-text fallback, so produce_archive=False does
-        not skip the remote engine for non-PDF MIME types.
-        """
-        remote_parser.parse(
-            simple_digital_pdf_file,
-            "image/png",
-            produce_archive=False,
-        )
-
-        azure_client.begin_analyze_document.assert_called_once()
-        assert remote_parser.get_text() == _DEFAULT_TEXT
-
-    @pytest.mark.usefixtures("no_engine_settings")
-    def test_unconfigured_engine_takes_precedence_over_skip(
-        self,
-        remote_parser: RemoteDocumentParser,
-        simple_digital_pdf_file: Path,
-    ) -> None:
-        """An unconfigured engine still short-circuits before the
-        produce_archive check, returning empty text as before.
-        """
-        remote_parser.parse(
-            simple_digital_pdf_file,
-            "application/pdf",
-            produce_archive=False,
-        )
-
-        assert remote_parser.get_text() == ""
-        assert remote_parser.get_archive_path() is None
-
-
 # ---------------------------------------------------------------------------
 # parse() — Azure failure path
 # ---------------------------------------------------------------------------

src/paperless/tests/parsers/test_tesseract_parser.py

@@ -21,7 +21,7 @@ from documents.parsers import run_convert
 from paperless.models import ModeChoices
 from paperless.parsers import ParserProtocol
 from paperless.parsers.tesseract import RasterisedDocumentParser
-from paperless.parsers.utils import post_process_text
+from paperless.parsers.tesseract import post_process_text
 
 if TYPE_CHECKING:
     from pathlib import Path

src/paperless_benchmark/apps.py

@@ -0,0 +1,8 @@
+from django.apps import AppConfig
+from django.utils.translation import gettext_lazy as _
+
+
+class PaperlessBenchmarkConfig(AppConfig):
+    name = "paperless_benchmark"
+
+    verbose_name = _("Paperless benchmark")

src/paperless_benchmark/db.py

@@ -0,0 +1,136 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+from django.db import connection
+
+if TYPE_CHECKING:
+    from django.db.models import QuerySet
+
+
+def _reset_table_names() -> list[str]:
+    from guardian.models import GroupObjectPermission
+    from guardian.models import UserObjectPermission
+
+    from documents.models import Correspondent
+    from documents.models import Document
+    from documents.models import DocumentType
+    from documents.models import StoragePath
+    from documents.models import Tag
+
+    return [
+        Document.tags.through._meta.db_table,
+        Document._meta.db_table,
+        Tag._meta.db_table,
+        Correspondent._meta.db_table,
+        DocumentType._meta.db_table,
+        StoragePath._meta.db_table,
+        UserObjectPermission._meta.db_table,
+        GroupObjectPermission._meta.db_table,
+    ]
+
+
+def _delete_all_users_and_groups() -> None:
+    # ASSUMPTION: this tool assumes a disposable benchmark database, never
+    # point it at a real install. This deletes EVERY user and group in the
+    # database (not just benchmark-created ones) -- there is no way to
+    # distinguish "real" users from seeded ones, so this is only safe against
+    # a database that exists solely to run this benchmarking tool. The
+    # `benchmark seed --reset` CLI path requires an explicit
+    # `--yes-i-know-this-wipes-the-database` flag before reaching here; do
+    # not remove that guard.
+    from django.contrib.auth import get_user_model
+    from django.contrib.auth.models import Group
+
+    get_user_model().objects.all().delete()
+    Group.objects.all().delete()
+
+
+def _reset_postgresql() -> None:
+    tables = _reset_table_names()
+    with connection.cursor() as cursor:
+        cursor.execute(f"TRUNCATE TABLE {', '.join(tables)} RESTART IDENTITY CASCADE;")
+    _delete_all_users_and_groups()
+
+
+def _reset_mariadb() -> None:
+    # MariaDB's TRUNCATE has no CASCADE clause and refuses to truncate a
+    # table referenced by a foreign key while checks are enabled, so
+    # checks are disabled for the duration of the reset.
+    tables = _reset_table_names()
+    with connection.cursor() as cursor:
+        cursor.execute("SET FOREIGN_KEY_CHECKS = 0;")
+        try:
+            for table in tables:
+                cursor.execute(f"TRUNCATE TABLE {table};")
+        finally:
+            cursor.execute("SET FOREIGN_KEY_CHECKS = 1;")
+    _delete_all_users_and_groups()
+
+
+def _reset_sqlite() -> None:
+    from documents.models import Correspondent
+    from documents.models import Document
+    from documents.models import DocumentType
+    from documents.models import StoragePath
+    from documents.models import Tag
+
+    Document.global_objects.all().delete()
+    Tag.objects.all().delete()
+    Correspondent.objects.all().delete()
+    DocumentType.objects.all().delete()
+    StoragePath.objects.all().delete()
+    _delete_all_users_and_groups()
+
+
+def reset_benchmark_data() -> None:
+    """
+    Remove all previously-seeded benchmark data (documents, tags,
+    correspondents, document types, storage paths, guardian permission
+    rows, users, and groups) so a fresh `benchmark seed` run starts
+    from an empty slate. Dispatches per-backend because TRUNCATE syntax
+    and cascade behavior differ across the 3 supported databases.
+    """
+    if connection.vendor == "postgresql":
+        _reset_postgresql()
+    elif connection.vendor == "mysql":
+        # MariaDB also reports vendor == "mysql" under Django's mysql backend.
+        _reset_mariadb()
+    else:
+        _reset_sqlite()
+
+
+def capture_explain(queryset: QuerySet) -> str:
+    """
+    Return the query plan for `queryset` using the current backend's
+    explain facility. PostgreSQL supports `EXPLAIN ANALYZE {sql}` (real
+    execution stats). MariaDB does NOT accept that syntax -- verified
+    against a real MariaDB 12.3 container: `EXPLAIN ANALYZE {sql}` raises a
+    1064 syntax error, while MariaDB's own `ANALYZE {sql}` form (no
+    `EXPLAIN` keyword) works and returns real per-row execution stats
+    (`r_rows`, `r_filtered`, etc. columns) -- this is MariaDB's
+    EXPLAIN-ANALYZE-equivalent, distinct from MySQL 8.0.18+'s
+    `EXPLAIN ANALYZE` syntax, which MariaDB does not implement. SQLite only
+    supports EXPLAIN QUERY PLAN (the chosen plan, not real timing/row
+    counts) -- that output is clearly labeled rather than silently looking
+    equivalent to the other two backends' output.
+    """
+    sql, params = queryset.query.sql_with_params()
+    with connection.cursor() as cursor:
+        if connection.vendor == "postgresql":
+            cursor.execute(f"EXPLAIN ANALYZE {sql}", params)
+            return "\n".join(str(row[0]) for row in cursor.fetchall())
+        if connection.vendor == "mysql":
+            # MariaDB also reports vendor == "mysql" under Django's mysql
+            # backend. Unlike MySQL 8.0.18+, MariaDB has no `EXPLAIN
+            # ANALYZE` syntax -- its equivalent is `ANALYZE <statement>`.
+            cursor.execute(f"ANALYZE {sql}", params)
+            columns = [c[0] for c in cursor.description]
+            header = " | ".join(columns)
+            rows = "\n".join(
+                " | ".join(str(c) for c in row) for row in cursor.fetchall()
+            )
+            return f"{header}\n{rows}"
+        cursor.execute(f"EXPLAIN QUERY PLAN {sql}", params)
+        rows = "\n".join(" | ".join(str(c) for c in row) for row in cursor.fetchall())
+        return f"(plan only -- no execution stats on SQLite)\n{rows}"

src/paperless_benchmark/endpoints.py

@@ -0,0 +1,81 @@
+# src/paperless_benchmark/endpoints.py
+from __future__ import annotations
+
+import statistics
+import time
+from dataclasses import dataclass
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from django.contrib.auth.models import User
+    from rest_framework.test import APIClient
+
+ENDPOINTS: tuple[tuple[str, str], ...] = (
+    ("documents_default", "/api/documents/"),
+    ("documents_page50", "/api/documents/?page_size=50"),
+    ("tags_all", "/api/tags/?page_size=100000"),
+)
+
+
+@dataclass(frozen=True, slots=True)
+class EndpointTiming:
+    user_label: str
+    endpoint_name: str
+    query_count: int
+    min_ms: float
+    median_ms: float
+    max_ms: float
+
+
+def _timed_requests(client: APIClient, url: str, n: int) -> list[float]:
+    times = []
+    for _ in range(n):
+        t0 = time.perf_counter()
+        resp = client.get(url)
+        t1 = time.perf_counter()
+        if resp.status_code != 200:
+            raise RuntimeError(
+                f"GET {url} -> {resp.status_code}: {resp.content[:300]!r}",
+            )
+        times.append(t1 - t0)
+    return times
+
+
+def _query_count(client: APIClient, url: str) -> int:
+    from django.db import connection
+    from django.test.utils import CaptureQueriesContext
+
+    with CaptureQueriesContext(connection) as ctx:
+        resp = client.get(url)
+    if resp.status_code != 200:
+        raise RuntimeError(f"GET {url} -> {resp.status_code}: {resp.content[:300]!r}")
+    return len(ctx.captured_queries)
+
+
+def run_endpoint_benchmarks(
+    *,
+    perf_target: User,
+    perf_admin: User,
+    repeat: int,
+) -> list[EndpointTiming]:
+    from rest_framework.test import APIClient
+
+    results: list[EndpointTiming] = []
+    for user_label, user in (("target", perf_target), ("admin", perf_admin)):
+        client = APIClient()
+        client.force_authenticate(user=user)
+        for name, url in ENDPOINTS:
+            client.get(url)  # warm-up request, not counted
+            qcount = _query_count(client, url)
+            times_ms = [t * 1000 for t in _timed_requests(client, url, repeat)]
+            results.append(
+                EndpointTiming(
+                    user_label=user_label,
+                    endpoint_name=name,
+                    query_count=qcount,
+                    min_ms=min(times_ms),
+                    median_ms=statistics.median(times_ms),
+                    max_ms=max(times_ms),
+                ),
+            )
+    return results

src/paperless_benchmark/harness.py

@@ -0,0 +1,56 @@
+# src/paperless_benchmark/harness.py
+from __future__ import annotations
+
+import time
+from dataclasses import dataclass
+from typing import TYPE_CHECKING
+from typing import Generic
+from typing import TypeVar
+
+from django.db import connection
+from django.test.utils import CaptureQueriesContext
+
+if TYPE_CHECKING:
+    from collections.abc import Callable
+
+T = TypeVar("T")
+
+
+@dataclass(frozen=True, slots=True)
+class ProfileResult(Generic[T]):
+    best_seconds: float
+    all_seconds: tuple[float, ...]
+    query_count: int
+    result: T
+
+
+def run_profile(fn: Callable[[], T], *, repeat: int = 5) -> ProfileResult[T]:
+    """
+    Call `fn` `repeat` times, capturing wall-clock time for every call and
+    the SQL query count for the final call. Returns the best (minimum)
+    time across all repeats, since the first call(s) can be skewed by
+    connection warm-up or cold caches.
+    """
+    if repeat < 1:
+        raise ValueError("repeat must be >= 1")
+
+    all_seconds: list[float] = []
+    result: T | None = None
+    query_count = 0
+    for i in range(repeat):
+        with CaptureQueriesContext(connection) as ctx:
+            start = time.perf_counter()
+            result = fn()
+            all_seconds.append(time.perf_counter() - start)
+        if i == repeat - 1:
+            query_count = len(ctx.captured_queries)
+    # Purely a type-narrowing aid for the type checker: the `repeat < 1`
+    # guard above already turns the one case that could leave `result`
+    # unset into a clear ValueError, so this is unreachable in practice.
+    assert result is not None
+    return ProfileResult(
+        best_seconds=min(all_seconds),
+        all_seconds=tuple(all_seconds),
+        query_count=query_count,
+        result=result,
+    )

src/paperless_benchmark/management/commands/benchmark.py

@@ -0,0 +1,222 @@
+# src/paperless_benchmark/management/commands/benchmark.py
+from __future__ import annotations
+
+from typing import Any
+
+from django.core.management.base import BaseCommand
+from django.core.management.base import CommandError
+from django.core.management.base import CommandParser
+
+
+class Command(BaseCommand):
+    help = "Seed, run, and profile paperless-ngx performance benchmarks."
+
+    def add_arguments(self, parser: CommandParser) -> None:
+        parser.add_argument(
+            "action",
+            choices=["seed", "run", "profile", "list-scenarios"],
+            help="Which benchmark action to perform.",
+        )
+        parser.add_argument(
+            "scenario",
+            nargs="?",
+            default=None,
+            help="Scenario name (required for `profile`; see `list-scenarios`).",
+        )
+        parser.add_argument(
+            "--tier",
+            choices=["home", "medium", "large"],
+            default="medium",
+            help="Dataset scale tier for `seed` (default: medium).",
+        )
+        parser.add_argument(
+            "--reset",
+            action="store_true",
+            default=False,
+            help="For `seed`: truncate existing benchmark data first.",
+        )
+        parser.add_argument(
+            "--yes-i-know-this-wipes-the-database",
+            action="store_true",
+            default=False,
+            help=(
+                "Required alongside --reset: confirms you understand `seed "
+                "--reset` deletes ALL users, ALL groups, and ALL documents/"
+                "tags/correspondents/document types/storage paths in this "
+                "database, not just benchmark-created ones."
+            ),
+        )
+        parser.add_argument(
+            "--seed",
+            type=int,
+            default=42,
+            help="RNG seed for reproducible datasets (default: 42).",
+        )
+        parser.add_argument(
+            "--repeat",
+            type=int,
+            default=5,
+            help="Number of timed repetitions for `run`/`profile` (default: 5).",
+        )
+        parser.add_argument(
+            "--label",
+            default="baseline",
+            help="Free-text tag for a `run`, printed and recorded in history only.",
+        )
+        parser.add_argument(
+            "--explain",
+            action="store_true",
+            default=False,
+            help="For `profile`: also capture and print the query plan.",
+        )
+
+    def handle(self, *args: Any, **options: Any) -> None:
+        action = options["action"]
+        if action == "seed":
+            self._handle_seed(options)
+        elif action == "run":
+            self._handle_run(options)
+        elif action == "profile":
+            self._handle_profile(options)
+        else:
+            self._handle_list_scenarios()
+
+    def _handle_seed(self, options: dict[str, Any]) -> None:
+        from paperless_benchmark.db import reset_benchmark_data
+        from paperless_benchmark.seeding import seed_benchmark_dataset
+
+        if options["reset"]:
+            if not options["yes_i_know_this_wipes_the_database"]:
+                raise CommandError(
+                    "--reset requires --yes-i-know-this-wipes-the-database. "
+                    "This deletes ALL users, ALL groups, and ALL documents, "
+                    "tags, correspondents, document types, and storage paths "
+                    "in this database -- not just benchmark-created ones. "
+                    "Only run this against a disposable benchmark database, "
+                    "never a real install. Re-run with "
+                    "--reset --yes-i-know-this-wipes-the-database to proceed.",
+                )
+            self.stdout.write("Resetting existing benchmark data...")
+            reset_benchmark_data()
+
+        data = seed_benchmark_dataset(options["tier"], seed=options["seed"])
+        self.stdout.write(
+            self.style.SUCCESS(
+                f"Seeded tier={options['tier']!r}: {data.documents} documents, "
+                f"{len(data.users)} users, {len(data.groups)} groups.",
+            ),
+        )
+
+    def _handle_run(self, options: dict[str, Any]) -> None:
+        from django.contrib.auth import get_user_model
+        from django.db import connection
+
+        from documents.models import Document
+        from paperless_benchmark.endpoints import run_endpoint_benchmarks
+        from paperless_benchmark.results import append_history
+
+        user_model = get_user_model()
+        try:
+            perf_target = user_model.objects.get(username="perf_target")
+            perf_admin = user_model.objects.get(username="perf_admin")
+        except user_model.DoesNotExist as e:
+            raise CommandError(
+                "No benchmark dataset found. Run `manage.py benchmark seed` first.",
+            ) from e
+
+        db_vendor = connection.vendor
+        document_count = Document.objects.count()
+
+        results = run_endpoint_benchmarks(
+            perf_target=perf_target,
+            perf_admin=perf_admin,
+            repeat=options["repeat"],
+        )
+
+        self.stdout.write(f"# label={options['label']} repeat={options['repeat']}")
+        self.stdout.write(
+            f"{'user':7s} {'endpoint':20s} {'queries':>8s} "
+            f"{'min_ms':>9s} {'median_ms':>10s} {'max_ms':>9s}",
+        )
+        for r in results:
+            self.stdout.write(
+                f"{r.user_label:7s} {r.endpoint_name:20s} {r.query_count:8d} "
+                f"{r.min_ms:9.1f} {r.median_ms:10.1f} {r.max_ms:9.1f}",
+            )
+            append_history(
+                {
+                    "mode": "run",
+                    "label": options["label"],
+                    "user": r.user_label,
+                    "endpoint": r.endpoint_name,
+                    "query_count": r.query_count,
+                    "min_ms": r.min_ms,
+                    "median_ms": r.median_ms,
+                    "max_ms": r.max_ms,
+                    "db_vendor": db_vendor,
+                    "document_count": document_count,
+                },
+            )
+
+    def _handle_profile(self, options: dict[str, Any]) -> None:
+        from django.contrib.auth import get_user_model
+        from django.db import connection
+
+        from documents.models import Document
+        from paperless_benchmark.db import capture_explain
+        from paperless_benchmark.harness import run_profile
+        from paperless_benchmark.results import append_history
+        from paperless_benchmark.scenarios import get as get_scenario
+
+        if not options["scenario"]:
+            raise CommandError(
+                "`profile` requires a scenario name; see `list-scenarios`.",
+            )
+
+        scenario = get_scenario(options["scenario"])
+
+        user_model = get_user_model()
+        try:
+            perf_target = user_model.objects.get(username="perf_target")
+        except user_model.DoesNotExist as e:
+            raise CommandError(
+                "No benchmark dataset found. Run `manage.py benchmark seed` first.",
+            ) from e
+
+        profile = run_profile(
+            lambda: scenario.run(perf_target),
+            repeat=options["repeat"],
+        )
+        self.stdout.write(
+            f"{scenario.name}: best={profile.best_seconds:.4f}s "
+            f"queries={profile.query_count}",
+        )
+
+        if options["explain"]:
+            if scenario.queryset_for_explain is not None:
+                plan = capture_explain(scenario.queryset_for_explain(perf_target))
+                self.stdout.write(plan)
+            else:
+                self.stdout.write(
+                    self.style.WARNING(
+                        f"--explain was requested but scenario {scenario.name!r} "
+                        "does not support it (no queryset_for_explain); skipping.",
+                    ),
+                )
+
+        append_history(
+            {
+                "mode": "profile",
+                "scenario": scenario.name,
+                "best_seconds": profile.best_seconds,
+                "query_count": profile.query_count,
+                "db_vendor": connection.vendor,
+                "document_count": Document.objects.count(),
+            },
+        )
+
+    def _handle_list_scenarios(self) -> None:
+        from paperless_benchmark.scenarios import all_scenarios
+
+        for scenario in all_scenarios():
+            self.stdout.write(f"{scenario.name}: {scenario.describe}")

src/paperless_benchmark/results.py

@@ -0,0 +1,39 @@
+# src/paperless_benchmark/results.py
+from __future__ import annotations
+
+import json
+import subprocess
+from datetime import UTC
+from datetime import datetime
+from pathlib import Path
+from typing import Any
+
+RESULTS_DIR = Path(__file__).resolve().parent.parent.parent / "benchmark_results"
+
+
+def _current_git_ref() -> str:
+    result = subprocess.run(
+        ["git", "rev-parse", "--short", "HEAD"],
+        capture_output=True,
+        text=True,
+        check=False,
+    )
+    return result.stdout.strip() or "unknown"
+
+
+def append_history(entry: dict[str, Any], *, code_ref: str | None = None) -> None:
+    """
+    Append one line to benchmark_results/history.jsonl -- a local-only,
+    append-only, cross-session record of every `benchmark run`/`profile`
+    invocation. Unlike a single overwritten snapshot file, this survives
+    across sessions so a benchmarking effort picked back up days later has
+    a full timeline instead of only the most recent result.
+    """
+    RESULTS_DIR.mkdir(exist_ok=True)
+    record = {
+        "timestamp": datetime.now(UTC).isoformat(),
+        "code_ref": code_ref or _current_git_ref(),
+        **entry,
+    }
+    with (RESULTS_DIR / "history.jsonl").open("a") as f:
+        f.write(json.dumps(record) + "\n")

src/paperless_benchmark/scenarios.py

@@ -0,0 +1,110 @@
+# src/paperless_benchmark/scenarios.py
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import TYPE_CHECKING
+from typing import Any
+
+if TYPE_CHECKING:
+    from collections.abc import Callable
+
+    from django.contrib.auth.models import User
+    from django.db.models import QuerySet
+
+
+@dataclass(frozen=True, slots=True)
+class Scenario:
+    name: str
+    describe: str
+    run: Callable[[User], Any]
+    queryset_for_explain: Callable[[User], QuerySet] | None = None
+
+
+_SCENARIOS: dict[str, Scenario] = {}
+
+
+def register(scenario: Scenario) -> None:
+    _SCENARIOS[scenario.name] = scenario
+
+
+def get(name: str) -> Scenario:
+    from django.core.management.base import CommandError
+
+    try:
+        return _SCENARIOS[name]
+    except KeyError:
+        available = ", ".join(sorted(_SCENARIOS)) or "(none registered)"
+        raise CommandError(
+            f"Unknown benchmark scenario {name!r}. Available: {available}",
+        ) from None
+
+
+def all_scenarios() -> tuple[Scenario, ...]:
+    return tuple(_SCENARIOS.values())
+
+
+def _guardian_visibility_query_run(user: User) -> list[int]:
+    from documents.models import Document
+    from documents.permissions import get_objects_for_user_owner_aware
+
+    return list(
+        get_objects_for_user_owner_aware(
+            user,
+            "documents.view_document",
+            Document,
+        ).values_list("id", flat=True),
+    )
+
+
+def _guardian_visibility_query_queryset(user: User) -> QuerySet:
+    from documents.models import Document
+    from documents.permissions import get_objects_for_user_owner_aware
+
+    return get_objects_for_user_owner_aware(user, "documents.view_document", Document)
+
+
+register(
+    Scenario(
+        name="guardian_visibility_query",
+        describe=(
+            "Document-visibility queryset for a user with mixed owned/shared "
+            "documents -- exercises documents.permissions."
+            "get_objects_for_user_owner_aware's guardian permission join."
+        ),
+        run=_guardian_visibility_query_run,
+        queryset_for_explain=_guardian_visibility_query_queryset,
+    ),
+)
+
+
+def _permitted_document_ids_run(user: User) -> list[int]:
+    from documents.models import Document
+    from documents.permissions import permitted_document_ids
+
+    return list(
+        Document.objects.filter(id__in=permitted_document_ids(user)).values_list(
+            "id",
+            flat=True,
+        ),
+    )
+
+
+def _permitted_document_ids_queryset(user: User) -> QuerySet:
+    from documents.models import Document
+    from documents.permissions import permitted_document_ids
+
+    return Document.objects.filter(id__in=permitted_document_ids(user))
+
+
+register(
+    Scenario(
+        name="permitted_document_ids",
+        describe=(
+            "Document-visibility query built from documents.permissions."
+            "permitted_document_ids -- the resolved-ID-set alternative to "
+            "guardian_visibility_query, for side-by-side comparison."
+        ),
+        run=_permitted_document_ids_run,
+        queryset_for_explain=_permitted_document_ids_queryset,
+    ),
+)

src/paperless_benchmark/seeding.py

@@ -0,0 +1,445 @@
+# src/paperless_benchmark/seeding.py
+from __future__ import annotations
+
+import datetime
+import random
+import time
+from dataclasses import dataclass
+from typing import TYPE_CHECKING
+from typing import Literal
+
+if TYPE_CHECKING:
+    from django.contrib.auth.models import Group
+    from django.contrib.auth.models import User
+
+Tier = Literal["home", "medium", "large"]
+
+CHUNK_SIZE = 5_000
+
+MIME_TYPES = (
+    "application/pdf",
+    "image/png",
+    "image/jpeg",
+    "text/plain",
+)
+
+# Ownership split for documents, mirroring the shape used in the #11950
+# perf-benchmark dataset (owned-by-target / owned-by-other / unowned).
+OWNED_BY_TARGET_FRACTION = 0.60
+OWNED_BY_OTHER_FRACTION = 0.30
+# remainder (0.10) is unowned
+
+# Of documents owned by "other" users, the fraction explicitly shared
+# (view, or view+change) with perf_target via guardian permissions --
+# this is what exercises the get_user_can_change() per-row N+1 that the
+# `run` endpoint benchmarks measure.
+SHARED_WITH_TARGET_FRACTION = 0.5
+SHARED_WITH_CHANGE_FRACTION = 0.5
+
+# Guardian permission-row ratios measured from a real install (discussion
+# #13276): 1,414 user-perm rows / 27,232 group-perm rows over 12,000
+# documents. Layered across ALL owned documents for the general user/group
+# pool (not just perf_target's shares), so `profile` scenarios exercise a
+# realistic permission-join shape for arbitrary users, not only perf_target.
+USER_PERM_ROWS_PER_DOC = 1_414 / 12_000
+GROUP_PERM_ROWS_PER_DOC = 27_232 / 12_000
+
+
+@dataclass(frozen=True, slots=True)
+class _TierCounts:
+    documents: int
+    tags: int
+    correspondents: int
+    document_types: int
+    storage_paths: int
+    other_users: int
+    groups: int
+    tags_per_doc: tuple[int, int]
+
+
+TIERS: dict[Tier, _TierCounts] = {
+    "home": _TierCounts(
+        documents=500,
+        tags=20,
+        correspondents=10,
+        document_types=8,
+        storage_paths=5,
+        other_users=3,
+        groups=2,
+        tags_per_doc=(1, 3),
+    ),
+    "medium": _TierCounts(
+        documents=20_000,
+        tags=100,
+        correspondents=300,
+        document_types=50,
+        storage_paths=20,
+        other_users=10,
+        groups=5,
+        tags_per_doc=(2, 6),
+    ),
+    "large": _TierCounts(
+        documents=360_000,
+        tags=1_000,
+        correspondents=5_000,
+        document_types=300,
+        storage_paths=50,
+        other_users=25,
+        groups=10,
+        tags_per_doc=(3, 7),
+    ),
+}
+
+
+def log(msg: str) -> None:
+    print(f"[{time.strftime('%H:%M:%S')}] {msg}", flush=True)  # noqa: T201
+
+
+@dataclass(frozen=True, slots=True)
+class SeededData:
+    """
+    Summary of a completed seed run. `documents`/`tags`/`correspondents`/
+    `document_types`/`storage_paths` are counts, not the seeded ORM
+    instances: at the `large` tier (360,000 documents) holding every
+    instance in memory simultaneously is a real risk for zero benefit, since
+    no caller consumes anything but the counts. `perf_target`/`perf_admin`/
+    `users`/`groups` stay as real objects -- at most ~26 users/12 groups even
+    at `large` tier, and small enough to be useful to a future caller.
+    """
+
+    perf_target: User
+    perf_admin: User
+    users: tuple[User, ...]
+    groups: tuple[Group, ...]
+    documents: int
+    tags: int
+    correspondents: int
+    document_types: int
+    storage_paths: int
+
+
+def _grant_model_level_permissions(user: User) -> None:
+    """
+    Grant perf_target Django model-level view/add/change permissions on
+    Document and Tag, on top of the per-object guardian grants seeding
+    creates elsewhere. DRF's PaperlessObjectPermissions checks model-level
+    permissions before guardian's object-level ones are ever consulted, so
+    without this perf_target gets a blanket 403 on /api/documents/ and
+    /api/tags/ regardless of which documents guardian says it can see.
+    """
+    from django.contrib.auth.models import Permission
+    from django.contrib.contenttypes.models import ContentType
+
+    from documents.models import Document
+    from documents.models import Tag
+
+    for model in (Document, Tag):
+        content_type = ContentType.objects.get_for_model(model)
+        codenames = [
+            f"{action}_{model._meta.model_name}" for action in ("view", "add", "change")
+        ]
+        perms = Permission.objects.filter(
+            content_type=content_type,
+            codename__in=codenames,
+        )
+        user.user_permissions.add(*perms)
+
+
+def _create_users_and_groups(
+    counts: _TierCounts,
+) -> tuple[User, User, tuple[User, ...], tuple[Group, ...]]:
+    from django.contrib.auth.models import Group
+
+    from documents.tests.factories import UserFactory
+
+    perf_target = UserFactory.create(username="perf_target")
+    _grant_model_level_permissions(perf_target)
+    perf_admin = UserFactory.create(username="perf_admin", superuser=True)
+    other_users = tuple(UserFactory.create_batch(counts.other_users))
+    groups = tuple(
+        Group.objects.create(name=f"benchmark_group_{i}") for i in range(counts.groups)
+    )
+    log(
+        f"Created users: 1 target, 1 superuser, {len(other_users)} other, "
+        f"{len(groups)} groups.",
+    )
+    return perf_target, perf_admin, other_users, groups
+
+
+def _create_lookup_tables(counts: _TierCounts):
+    from documents.models import Correspondent
+    from documents.models import DocumentType
+    from documents.models import StoragePath
+    from documents.models import Tag
+    from documents.tests.factories import CorrespondentFactory
+    from documents.tests.factories import DocumentTypeFactory
+    from documents.tests.factories import StoragePathFactory
+    from documents.tests.factories import TagFactory
+
+    tags = tuple(Tag.objects.bulk_create(TagFactory.build_batch(counts.tags)))
+    correspondents = tuple(
+        Correspondent.objects.bulk_create(
+            CorrespondentFactory.build_batch(counts.correspondents),
+        ),
+    )
+    document_types = tuple(
+        DocumentType.objects.bulk_create(
+            DocumentTypeFactory.build_batch(counts.document_types),
+        ),
+    )
+    storage_paths = tuple(
+        StoragePath.objects.bulk_create(
+            StoragePathFactory.build_batch(counts.storage_paths),
+        ),
+    )
+    log(
+        f"Created {len(tags)} tags, {len(correspondents)} correspondents, "
+        f"{len(document_types)} document types, {len(storage_paths)} storage paths.",
+    )
+    return tags, correspondents, document_types, storage_paths
+
+
+def _assign_owner(rng: random.Random, perf_target: User, other_users: tuple[User, ...]):
+    roll = rng.random()
+    if roll < OWNED_BY_TARGET_FRACTION:
+        return perf_target, "target"
+    if roll < OWNED_BY_TARGET_FRACTION + OWNED_BY_OTHER_FRACTION:
+        return rng.choice(other_users), "other"
+    return None, "unowned"
+
+
+def _seed_documents(
+    rng: random.Random,
+    counts: _TierCounts,
+    tags,
+    correspondents,
+    document_types,
+    storage_paths,
+    perf_target,
+    other_users,
+) -> tuple[int, list[int]]:
+    """
+    Bulk-create `counts.documents` documents in chunks. Returns the total
+    document count plus a lightweight list of pks for documents that ended
+    up with an owner (target or other) -- that's all
+    `_grant_general_permissions` needs to sample from, so full `Document`
+    instances aren't accumulated across chunks (a real memory concern at the
+    `large` tier's 360,000 documents).
+    """
+    from django.contrib.auth.models import Permission
+    from django.contrib.contenttypes.models import ContentType
+    from guardian.models import UserObjectPermission
+
+    from documents.models import Document
+    from documents.tests.factories import DocumentFactory
+
+    tag_ids = [t.pk for t in tags]
+    correspondent_ids = [c.pk for c in correspondents]
+    document_type_ids = [d.pk for d in document_types]
+    storage_path_ids = [s.pk for s in storage_paths]
+
+    doc_content_type = ContentType.objects.get_for_model(Document)
+    view_perm = Permission.objects.get(
+        codename="view_document",
+        content_type=doc_content_type,
+    )
+    change_perm = Permission.objects.get(
+        codename="change_document",
+        content_type=doc_content_type,
+    )
+    through_model = Document.tags.through
+
+    document_count = 0
+    owned_document_pks: list[int] = []
+    remaining = counts.documents
+    while remaining > 0:
+        chunk_n = min(CHUNK_SIZE, remaining)
+        remaining -= chunk_n
+
+        batch = []
+        owner_buckets = []
+        for _ in range(chunk_n):
+            doc = DocumentFactory.build(
+                mime_type=rng.choice(MIME_TYPES),
+                page_count=rng.randint(1, 30),
+                correspondent_id=(
+                    rng.choice(correspondent_ids)
+                    if correspondent_ids and rng.random() < 0.8
+                    else None
+                ),
+                document_type_id=(
+                    rng.choice(document_type_ids)
+                    if document_type_ids and rng.random() < 0.6
+                    else None
+                ),
+                storage_path_id=(
+                    rng.choice(storage_path_ids)
+                    if storage_path_ids and rng.random() < 0.8
+                    else None
+                ),
+                # Document.created is a plain DateField (default: today).
+                # Leaving it unset would give every seeded document the same
+                # date, collapsing Document's ("-created",) ordering index
+                # into a single-valued sort key -- spread it over a
+                # realistic multi-year window instead.
+                created=datetime.date.today()
+                - datetime.timedelta(days=rng.randint(0, 365 * 3)),
+            )
+            owner, bucket = _assign_owner(rng, perf_target, other_users)
+            doc.owner_id = owner.pk if owner else None
+            batch.append(doc)
+            owner_buckets.append(bucket)
+
+        created = Document.objects.bulk_create(batch, batch_size=CHUNK_SIZE)
+
+        through_rows = []
+        for doc in created:
+            k = rng.randint(*counts.tags_per_doc)
+            for tag_id in rng.sample(tag_ids, min(k, len(tag_ids))):
+                through_rows.append(through_model(document_id=doc.pk, tag_id=tag_id))
+        if through_rows:
+            through_model.objects.bulk_create(through_rows, batch_size=CHUNK_SIZE)
+
+        perm_rows = []
+        for doc, bucket in zip(created, owner_buckets, strict=True):
+            if bucket == "unowned":
+                continue
+            owned_document_pks.append(doc.pk)
+
+            if bucket != "other":
+                continue
+            if rng.random() >= SHARED_WITH_TARGET_FRACTION:
+                continue
+            perm_rows.append(
+                UserObjectPermission(
+                    permission=view_perm,
+                    content_type=doc_content_type,
+                    object_pk=str(doc.pk),
+                    user=perf_target,
+                ),
+            )
+            if rng.random() < SHARED_WITH_CHANGE_FRACTION:
+                perm_rows.append(
+                    UserObjectPermission(
+                        permission=change_perm,
+                        content_type=doc_content_type,
+                        object_pk=str(doc.pk),
+                        user=perf_target,
+                    ),
+                )
+        if perm_rows:
+            UserObjectPermission.objects.bulk_create(perm_rows, batch_size=CHUNK_SIZE)
+
+        document_count += len(created)
+        log(f"  {document_count}/{counts.documents} documents seeded")
+
+    return document_count, owned_document_pks
+
+
+def _grant_general_permissions(
+    rng: random.Random,
+    owned_document_pks: list[int],
+    users,
+    groups,
+) -> None:
+    """
+    Layer realistic (issue #13276-derived) guardian permission-row ratios
+    across owned documents for the general user/group pool, so `profile`
+    scenarios exercise the same permission-join shape regardless of which
+    user they check visibility for (not just perf_target).
+    """
+    from django.contrib.auth.models import Permission
+    from django.contrib.contenttypes.models import ContentType
+    from guardian.models import GroupObjectPermission
+    from guardian.models import UserObjectPermission
+
+    from documents.models import Document
+
+    if not owned_document_pks or not users:
+        return
+
+    doc_content_type = ContentType.objects.get_for_model(Document)
+    view_perm = Permission.objects.get(
+        codename="view_document",
+        content_type=doc_content_type,
+    )
+
+    n_user_perms = round(len(owned_document_pks) * USER_PERM_ROWS_PER_DOC)
+    n_group_perms = (
+        round(len(owned_document_pks) * GROUP_PERM_ROWS_PER_DOC) if groups else 0
+    )
+
+    user_rows = [
+        UserObjectPermission(
+            permission=view_perm,
+            content_type=doc_content_type,
+            object_pk=str(rng.choice(owned_document_pks)),
+            user=rng.choice(users),
+        )
+        for _ in range(n_user_perms)
+    ]
+    if user_rows:
+        UserObjectPermission.objects.bulk_create(
+            user_rows,
+            batch_size=CHUNK_SIZE,
+            ignore_conflicts=True,
+        )
+
+    group_rows = [
+        GroupObjectPermission(
+            permission=view_perm,
+            content_type=doc_content_type,
+            object_pk=str(rng.choice(owned_document_pks)),
+            group=rng.choice(groups),
+        )
+        for _ in range(n_group_perms)
+    ]
+    if group_rows:
+        GroupObjectPermission.objects.bulk_create(
+            group_rows,
+            batch_size=CHUNK_SIZE,
+            ignore_conflicts=True,
+        )
+
+    log(f"  Granted {len(user_rows)} user perms, {len(group_rows)} group perms.")
+
+
+def seed_benchmark_dataset(tier: Tier, *, seed: int = 42) -> SeededData:
+    """
+    Build a benchmark dataset at the given scale tier: a named perf_target
+    (mixed owned/shared documents) and perf_admin (superuser) for endpoint
+    benchmarking, plus a general user/group pool with realistic guardian
+    permission-row ratios for profile scenarios.
+    """
+    counts = TIERS[tier]
+    rng = random.Random(seed)
+
+    log(f"Seeding tier={tier!r}")
+    perf_target, perf_admin, other_users, groups = _create_users_and_groups(counts)
+    tags, correspondents, document_types, storage_paths = _create_lookup_tables(counts)
+    document_count, owned_document_pks = _seed_documents(
+        rng,
+        counts,
+        tags,
+        correspondents,
+        document_types,
+        storage_paths,
+        perf_target,
+        other_users,
+    )
+    all_users = (perf_target, *other_users)
+    _grant_general_permissions(rng, owned_document_pks, all_users, groups)
+
+    log(f"Done. {document_count} documents seeded for tier={tier!r}.")
+
+    return SeededData(
+        perf_target=perf_target,
+        perf_admin=perf_admin,
+        users=all_users,
+        groups=groups,
+        documents=document_count,
+        tags=len(tags),
+        correspondents=len(correspondents),
+        document_types=len(document_types),
+        storage_paths=len(storage_paths),
+    )