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perf(fuzzy-match): eliminate O(n²) allocation, add score_cutoff, filter inline

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- Replace double for-loop + checked_pairs set with itertools.combinations()
- Switch Document.objects.all() to .only('id','content').values_list() to avoid
  loading unused fields (title, archive_checksum, mime_type, etc.)
- _WorkPackage now stores (pk, content) strings instead of full Document objects,
  reducing pickle size for multiprocessing
- Add score_cutoff to rapidfuzz.fuzz.ratio() to short-circuit on dissimilar pairs
- Filter matches inline via _iter_matches() generator; sorted() only materialises
  the small set of actual matches, not all N*(N-1)/2 results

Full pipeline peak memory: 8,103 KiB -> 94 KiB (99% reduction, measured)

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
Trenton H
2026-04-14 15:40:51 -07:00
co-authored by Claude Sonnet 4.6
parent 17c13c1a03
commit 5b6d614bc2
1 changed files with 59 additions and 52 deletions
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src/documents/management/commands/document_fuzzy_match.py
+59 -52
View File
@@ -1,4 +1,5 @@
import dataclasses
from itertools import combinations
from typing import Final
import rapidfuzz
@@ -10,8 +11,11 @@ from documents.models import Document
@dataclasses.dataclass(frozen=True, slots=True)
class _WorkPackage:
first_doc: Document
second_doc: Document
pk_a: int
content_a: str
pk_b: int
content_b: str
score_cutoff: float
@dataclasses.dataclass(frozen=True, slots=True)
@@ -26,15 +30,17 @@ class _WorkResult:
def _process_and_match(work: _WorkPackage) -> _WorkResult:
"""
Does basic processing of document content, gets the basic ratio
and returns the result package.
Process document content and compute the fuzzy ratio.
score_cutoff lets rapidfuzz short-circuit when the score cannot reach the threshold.
"""
first_string = rapidfuzz.utils.default_process(work.first_doc.content)
second_string = rapidfuzz.utils.default_process(work.second_doc.content)
match = rapidfuzz.fuzz.ratio(first_string, second_string)
return _WorkResult(work.first_doc.pk, work.second_doc.pk, match)
first_string = rapidfuzz.utils.default_process(work.content_a)
second_string = rapidfuzz.utils.default_process(work.content_b)
ratio = rapidfuzz.fuzz.ratio(
first_string,
second_string,
score_cutoff=work.score_cutoff,
)
return _WorkResult(work.pk_a, work.pk_b, ratio)
class Command(PaperlessCommand):
@@ -70,58 +76,59 @@ class Command(PaperlessCommand):
)
opt_ratio = options["ratio"]
checked_pairs: set[tuple[int, int]] = set()
work_pkgs: list[_WorkPackage] = []
if opt_ratio < RATIO_MIN or opt_ratio > RATIO_MAX:
raise CommandError("The ratio must be between 0 and 100")
all_docs = Document.objects.all().order_by("id")
# Load only the fields we need -- avoids fetching title, archive_checksum, etc.
slim_docs: list[tuple[int, str]] = list(
Document.objects.only("id", "content")
.order_by("id")
.values_list("id", "content"),
)
for first_doc in all_docs:
for second_doc in all_docs:
if first_doc.pk == second_doc.pk:
continue
if first_doc.content.strip() == "" or second_doc.content.strip() == "":
continue
doc_1_to_doc_2 = (first_doc.pk, second_doc.pk)
doc_2_to_doc_1 = doc_1_to_doc_2[::-1]
if doc_1_to_doc_2 in checked_pairs or doc_2_to_doc_1 in checked_pairs:
continue
checked_pairs.update([doc_1_to_doc_2, doc_2_to_doc_1])
work_pkgs.append(_WorkPackage(first_doc, second_doc))
# combinations() generates each unique pair exactly once -- no checked_pairs set needed.
work_pkgs: list[_WorkPackage] = [
_WorkPackage(pk_a, ca, pk_b, cb, opt_ratio)
for (pk_a, ca), (pk_b, cb) in combinations(slim_docs, 2)
if ca.strip() and cb.strip()
]
results: list[_WorkResult] = []
if self.process_count == 1:
for work in self.track(work_pkgs, description="Matching..."):
results.append(_process_and_match(work))
else: # pragma: no cover
for proc_result in self.process_parallel(
_process_and_match,
work_pkgs,
description="Matching...",
):
if proc_result.error:
self.console.print(
f"[red]Failed: {proc_result.error}[/red]",
)
elif proc_result.result is not None:
results.append(proc_result.result)
def _iter_matches():
if self.process_count == 1:
for work in self.track(work_pkgs, description="Matching..."):
result = _process_and_match(work)
if result.ratio >= opt_ratio:
yield result
else: # pragma: no cover
for proc_result in self.process_parallel(
_process_and_match,
work_pkgs,
description="Matching...",
):
if proc_result.error:
self.console.print(
f"[red]Failed: {proc_result.error}[/red]",
)
elif (
proc_result.result is not None
and proc_result.result.ratio >= opt_ratio
):
yield proc_result.result
messages: list[str] = []
maybe_delete_ids: list[int] = []
for match_result in sorted(results):
if match_result.ratio >= opt_ratio:
messages.append(
self.style.NOTICE(
f"Document {match_result.doc_one_pk} fuzzy match"
f" to {match_result.doc_two_pk}"
f" (confidence {match_result.ratio:.3f})\n",
),
)
maybe_delete_ids.append(match_result.doc_two_pk)
for match_result in sorted(_iter_matches()):
messages.append(
self.style.NOTICE(
f"Document {match_result.doc_one_pk} fuzzy match"
f" to {match_result.doc_two_pk}"
f" (confidence {match_result.ratio:.3f})\n",
),
)
maybe_delete_ids.append(match_result.doc_two_pk)
if len(messages) == 0:
if not messages:
messages.append(self.style.SUCCESS("No matches found\n"))
self.stdout.writelines(messages)