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"""三层去重主流程。
调用顺序:check / ingest 内部按 L1 -> L2 -> L3 顺序判定,任意层命中即返回。
Deduper 不要求线程安全;批处理串行调用即可。
"""
from __future__ import annotations
from datetime import datetime
from pathlib import Path
from loguru import logger
from extractor import Article
from .hasher import (
DEFAULT_HAMMING_THRESHOLD,
content_hash,
hamming,
simhash64,
)
from .models import DedupLayer, DedupResult, DedupStats, Fingerprint
from .store import DEFAULT_DB_PATH, FingerprintStore
# 默认时间窗口(±N 天)
DEFAULT_TIME_WINDOW_DAYS = 30
def _publish_date(article: Article) -> str | None:
"""从 Article.publish_time 取 YYYY-MM-DD 字符串。"""
if article.publish_time is None:
return None
return article.publish_time.strftime("%Y-%m-%d")
def article_to_fingerprint(article: Article) -> Fingerprint:
"""构造 Fingerprint(用于 ingest 写入或对外只读)。"""
return Fingerprint(
url_hash=article.url_hash,
content_hash=content_hash(article.content),
simhash=simhash64(article.content),
source_id=article.source_id,
url=article.url,
title=article.title,
publish_date=_publish_date(article),
ingested_at=datetime.now(),
)
class Deduper:
"""三层去重器。
构造完毕后:
- check(article) 仅判断,不写入;
- ingest(article) 判断,不重复则写入指纹库,返回结果。
"""
def __init__(
self,
db_path: str | Path = DEFAULT_DB_PATH,
simhash_threshold: int = DEFAULT_HAMMING_THRESHOLD,
time_window_days: int = DEFAULT_TIME_WINDOW_DAYS,
) -> None:
self.store = FingerprintStore(db_path)
self.simhash_threshold = simhash_threshold
self.time_window_days = time_window_days
def close(self) -> None:
self.store.close()
def __enter__(self) -> Deduper:
return self
def __exit__(self, *_: object) -> None:
self.close()
# ------------------------------------------------------------------ #
# 公共 API
# ------------------------------------------------------------------ #
def check(self, article: Article) -> DedupResult:
"""三层判重(只读)。"""
fp = article_to_fingerprint(article)
# L1: URL hash
existing = self.store.get_by_url_hash(fp.url_hash)
if existing is not None:
return DedupResult(
url_hash=fp.url_hash,
is_duplicate=True,
matched_layer=DedupLayer.URL,
matched_url_hash=existing.url_hash,
matched_url=existing.url,
matched_title=existing.title,
)
# L2: 内容 hash
existing = self.store.find_by_content_hash(fp.content_hash)
if existing is not None:
return DedupResult(
url_hash=fp.url_hash,
is_duplicate=True,
matched_layer=DedupLayer.CONTENT,
matched_url_hash=existing.url_hash,
matched_url=existing.url,
matched_title=existing.title,
)
# L3: SimHash 模糊
candidates = self.store.candidates_for_simhash(
fp.publish_date, self.time_window_days
)
best_dist: int | None = None
best_match: Fingerprint | None = None
for c in candidates:
d = hamming(fp.simhash, c.simhash)
if d <= self.simhash_threshold and (best_dist is None or d < best_dist):
best_dist = d
best_match = c
if d == 0: # 不可能更近,提前结束
break
if best_match is not None:
return DedupResult(
url_hash=fp.url_hash,
is_duplicate=True,
matched_layer=DedupLayer.SIMHASH,
matched_url_hash=best_match.url_hash,
matched_url=best_match.url,
matched_title=best_match.title,
hamming_distance=best_dist,
)
return DedupResult(url_hash=fp.url_hash, is_duplicate=False)
def ingest(self, article: Article) -> DedupResult:
"""判重 + 不重复则入库。"""
result = self.check(article)
if not result.is_duplicate:
fp = article_to_fingerprint(article)
self.store.upsert(fp)
logger.debug("入库: {} {}", fp.url_hash, fp.title[:30])
else:
logger.debug("命中重复: {}", result.short_summary())
return result
def stats(self) -> DedupStats:
lo, hi = self.store.date_range()
return DedupStats(
total=self.store.count(),
by_source=self.store.count_by_source(),
earliest=lo,
latest=hi,
)