将@关系批处理业务迁移到 value_rank 插件
- 从 MessageStorageDB 移除@抽取与社交图写入逻辑,消息层仅保留归档职责 - 从系统级任务移除 process_pending_mentions,取消 message_to_db 中对应入口 - 在 value_rank 插件新增定时动作 value_rank_mentions_extract(每10分钟) - 在插件内实现窗口化批处理(默认10~20分钟前)、@提取、幂等写入明细/边表/日汇总及 unique_interactors 回填 - 新增插件侧可配置参数 mention_batch_size / mention_window_start_minutes / mention_window_end_minutes
This commit is contained in:
@@ -2,8 +2,6 @@
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from datetime import datetime
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import json
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import re
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import xml.etree.ElementTree as ET
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from typing import Dict, List, Optional
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from db.base import BaseDBOperator
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@@ -91,400 +89,6 @@ class MessageStorageDB(BaseDBOperator):
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# 最后的保底策略:即使序列化失败,也确保字段有可追溯文本,避免丢失原始上下文。
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return str(msg)
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def _extract_mentioned_user_ids(self, raw_xml: str) -> List[str]:
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"""从消息 XML 中提取被@用户ID列表,并返回去重后的列表。
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解析策略:
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1. 优先从 `msg.msg_source` 的 XML 里读取 `atuserlist` 节点;
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2. 若 XML 解析失败,则退化为正则提取 `atuserlist` 文本;
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3. 去重并过滤空值,保证输出稳定。
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返回值示例:`["wxid_a", "wxid_b"]`
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"""
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raw_xml = str(raw_xml or "")
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if not raw_xml:
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return []
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at_user_list_text = ""
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try:
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root = ET.fromstring(raw_xml)
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node = root.find(".//atuserlist")
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if node is not None and node.text:
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at_user_list_text = str(node.text).strip()
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except Exception:
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# 兼容异常格式 XML,采用正则兜底,确保尽量不丢数据。
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match = re.search(r"<atuserlist><!\[CDATA\[(.*?)\]\]></atuserlist>", raw_xml, flags=re.IGNORECASE | re.DOTALL)
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if match:
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at_user_list_text = str(match.group(1) or "").strip()
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if not at_user_list_text:
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return []
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# 微信 atuserlist 常见分隔符为 ',',但实际环境可能混入 ';' 或空白,这里统一兼容。
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raw_ids = re.split(r"[,\s;]+", at_user_list_text)
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seen = set()
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result = []
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for uid in raw_ids:
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normalized_uid = str(uid or "").strip()
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if not normalized_uid or normalized_uid in seen:
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continue
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seen.add(normalized_uid)
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result.append(normalized_uid)
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return result
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def get_pending_mention_extract_messages(
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self,
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limit: int = 200,
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window_start_minutes: int = 20,
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window_end_minutes: int = 10,
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) -> List[Dict]:
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"""获取待处理 @ 抽取的消息批次。
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筛选规则:
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1. 群消息;
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2. mentioned_user_ids 为空(表示还未处理);
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3. message_xml 非空;
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4. 仅处理固定时间窗口(默认:10~20分钟前),降低扫描压力与热数据竞争。
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"""
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# 兜底修正窗口参数,确保窗口有效:start > end >= 0
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start_m = max(int(window_start_minutes), 1)
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end_m = max(int(window_end_minutes), 0)
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if start_m <= end_m:
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start_m = end_m + 10
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self.LOG.warning(
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f"@抽取窗口参数异常,已自动修正: window_start_minutes={window_start_minutes}, "
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f"window_end_minutes={window_end_minutes}, 修正后=[NOW-{start_m}m, NOW-{end_m}m)"
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)
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sql = """
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SELECT message_id, group_id, sender, message_xml, timestamp
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FROM messages
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WHERE group_id IS NOT NULL
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AND group_id <> ''
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AND (mentioned_user_ids IS NULL OR mentioned_user_ids = '')
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AND message_xml IS NOT NULL
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AND message_xml <> ''
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AND timestamp >= DATE_SUB(NOW(), INTERVAL %s MINUTE)
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AND timestamp < DATE_SUB(NOW(), INTERVAL %s MINUTE)
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ORDER BY timestamp ASC
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LIMIT %s
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"""
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rows = self.execute_query(sql, (start_m, end_m, limit)) or []
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self.LOG.debug(
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f"查询待抽取@消息: window=[NOW-{start_m}m, NOW-{end_m}m), limit={limit}, 命中={len(rows)}"
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)
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return rows
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def process_pending_mentions(
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self,
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batch_size: int = 200,
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window_start_minutes: int = 20,
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window_end_minutes: int = 10,
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) -> Dict[str, int]:
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"""批量处理待抽取 @ 的消息,并同步社交图数据。
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返回统计:
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- total: 本批读取条数
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- processed: 成功处理条数
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- with_mentions: 提取到 @ 的消息条数
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- failed: 失败条数
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"""
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started_at = datetime.now()
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self.LOG.info(
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"开始执行@批处理: "
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f"batch_size={batch_size}, window_start_minutes={window_start_minutes}, "
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f"window_end_minutes={window_end_minutes}"
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)
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rows = self.get_pending_mention_extract_messages(
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limit=batch_size,
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window_start_minutes=window_start_minutes,
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window_end_minutes=window_end_minutes,
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)
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if not rows:
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elapsed_ms = int((datetime.now() - started_at).total_seconds() * 1000)
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self.LOG.info(f"@批处理结束: 命中0条, 耗时={elapsed_ms}ms")
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return {"total": 0, "processed": 0, "with_mentions": 0, "failed": 0}
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processed = 0
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with_mentions = 0
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failed = 0
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# 记录少量失败样本,便于快速定位问题,不刷屏。
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fail_samples: List[str] = []
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for idx, row in enumerate(rows, start=1):
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try:
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message_id = str(row.get("message_id") or "").strip()
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group_id = str(row.get("group_id") or "").strip()
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sender_id = str(row.get("sender") or "").strip()
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raw_xml = str(row.get("message_xml") or "")
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ts_raw = row.get("timestamp")
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msg_time = self._safe_parse_message_time(ts_raw)
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mentioned_user_ids = self._extract_mentioned_user_ids(raw_xml)
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mentioned_user_ids_json = json.dumps(mentioned_user_ids, ensure_ascii=False)
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self._update_message_mentioned_user_ids(
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message_id=message_id,
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group_id=group_id,
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sender_id=sender_id,
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mentioned_user_ids_json=mentioned_user_ids_json,
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)
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self._persist_mention_graph_data(
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group_id=group_id,
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sender_id=sender_id,
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message_id=message_id,
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mentioned_user_ids=mentioned_user_ids,
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msg_time=msg_time,
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)
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processed += 1
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if mentioned_user_ids:
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with_mentions += 1
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if idx <= 3:
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# 前3条打 debug 明细,便于确认当前批处理真实在工作。
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self.LOG.debug(
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f"@批处理样本[{idx}]: message_id={message_id}, group_id={group_id}, "
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f"sender={sender_id}, mentioned_count={len(mentioned_user_ids)}"
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)
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except Exception as e:
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failed += 1
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self.LOG.error(f"处理待抽取@消息失败: message_id={row.get('message_id')}, error={e}")
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if len(fail_samples) < 5:
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fail_samples.append(str(row.get("message_id") or ""))
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elapsed_ms = int((datetime.now() - started_at).total_seconds() * 1000)
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stats = {
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"total": len(rows),
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"processed": processed,
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"with_mentions": with_mentions,
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"failed": failed,
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}
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self.LOG.info(
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f"@批处理结束: total={stats['total']}, processed={stats['processed']}, "
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f"with_mentions={stats['with_mentions']}, failed={stats['failed']}, 耗时={elapsed_ms}ms, "
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f"fail_samples={fail_samples}"
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)
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return stats
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@staticmethod
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def _safe_parse_message_time(value) -> datetime:
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"""安全解析消息时间,失败时回退到当前时间。"""
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if isinstance(value, datetime):
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return value
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text = str(value or "").strip()
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if not text:
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return datetime.now()
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try:
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return datetime.strptime(text, "%Y-%m-%d %H:%M:%S")
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except Exception:
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return datetime.now()
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def _update_message_mentioned_user_ids(
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self,
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message_id: str,
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group_id: str,
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sender_id: str,
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mentioned_user_ids_json: str,
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) -> None:
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"""回填消息表的 mentioned_user_ids 字段。"""
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self.execute_update(
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"""
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UPDATE messages
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SET mentioned_user_ids = %s
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WHERE message_id = %s
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AND group_id = %s
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AND sender = %s
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""",
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(mentioned_user_ids_json, message_id, group_id, sender_id),
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)
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def _persist_mention_graph_data(
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self,
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group_id: str,
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sender_id: str,
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message_id: str,
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mentioned_user_ids: List[str],
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msg_time: datetime,
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) -> None:
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"""落盘社交图增量数据(明细 + 边 + 个人日汇总)。
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设计原则:
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1. 只在群消息中处理(group_id 为空直接忽略);
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2. 过滤无效 @ 目标(空值、@所有人、自己@自己);
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3. 统计写入失败不抛异常,不影响主消息归档。
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"""
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# 非群消息或缺少关键字段时直接跳过,避免写入脏数据。
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if not group_id or not sender_id or not message_id or not mentioned_user_ids:
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return
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# 统一清洗被@列表,避免重复统计。
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invalid_mentions = {"notify@all", "all", "@all"}
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clean_mentioned_ids: List[str] = []
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seen = set()
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for uid in mentioned_user_ids:
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normalized_uid = str(uid or "").strip()
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if (not normalized_uid or normalized_uid in invalid_mentions or
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normalized_uid == sender_id or normalized_uid in seen):
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continue
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seen.add(normalized_uid)
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clean_mentioned_ids.append(normalized_uid)
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if not clean_mentioned_ids:
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return
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try:
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stat_date = msg_time.strftime("%Y-%m-%d")
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msg_time_str = msg_time.strftime("%Y-%m-%d %H:%M:%S")
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# 幂等控制:只处理“该消息中尚未写入明细表”的新增 @ 目标。
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existed_rows = self.execute_query(
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"""
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SELECT mentioned_user_id
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FROM t_message_mentions
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WHERE message_id = %s
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AND group_id = %s
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AND sender_id = %s
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""",
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(message_id, group_id, sender_id),
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) or []
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existed_ids = {str(r.get("mentioned_user_id") or "").strip() for r in existed_rows}
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newly_mentioned_ids = [uid for uid in clean_mentioned_ids if uid not in existed_ids]
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if not newly_mentioned_ids:
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self.LOG.debug(
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f"社交图写入跳过(无新增@关系): message_id={message_id}, group_id={group_id}, sender={sender_id}"
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)
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return
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# 1) 写 @ 明细表:用于追溯“哪条消息 @ 了谁”。
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mention_rows = [
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(message_id, group_id, sender_id, mentioned_uid, stat_date, msg_time_str)
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for mentioned_uid in newly_mentioned_ids
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]
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self.execute_batch(
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"""
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INSERT IGNORE INTO t_message_mentions
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(message_id, group_id, sender_id, mentioned_user_id, stat_date, msg_time)
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VALUES (%s, %s, %s, %s, %s, %s)
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""",
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mention_rows,
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)
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# 2) 写社交日边表:一条 @ 关系视为 sender -> mentioned_uid 的一条有向边增量。
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edge_rows = [
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(stat_date, group_id, sender_id, mentioned_uid, 1, 1.0)
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for mentioned_uid in newly_mentioned_ids
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]
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self.execute_batch(
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"""
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INSERT INTO t_social_edges_daily
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(stat_date, group_id, from_user_id, to_user_id, mention_count, interaction_score)
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VALUES (%s, %s, %s, %s, %s, %s)
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ON DUPLICATE KEY UPDATE
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mention_count = mention_count + VALUES(mention_count),
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interaction_score = interaction_score + VALUES(interaction_score),
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update_time = CURRENT_TIMESTAMP
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""",
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edge_rows,
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)
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# 3) 写个人日汇总:更新被@次数/主动@次数,供 value_rank 直接读取。
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sender_social_row = (
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stat_date, group_id, sender_id,
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0, len(newly_mentioned_ids), 0,
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float(len(newly_mentioned_ids)),
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)
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self.execute_update(
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"""
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INSERT INTO t_value_rank_social_daily
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(stat_date, group_id, user_id, mentioned_count, mention_others_count, unique_interactors, interaction_score)
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VALUES (%s, %s, %s, %s, %s, %s, %s)
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ON DUPLICATE KEY UPDATE
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mention_others_count = mention_others_count + VALUES(mention_others_count),
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interaction_score = interaction_score + VALUES(interaction_score),
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update_time = CURRENT_TIMESTAMP
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""",
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sender_social_row,
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)
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receiver_social_rows = [
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(stat_date, group_id, mentioned_uid, 1, 0, 0, 1.0)
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for mentioned_uid in newly_mentioned_ids
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]
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self.execute_batch(
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"""
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INSERT INTO t_value_rank_social_daily
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(stat_date, group_id, user_id, mentioned_count, mention_others_count, unique_interactors, interaction_score)
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VALUES (%s, %s, %s, %s, %s, %s, %s)
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ON DUPLICATE KEY UPDATE
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mentioned_count = mentioned_count + VALUES(mentioned_count),
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interaction_score = interaction_score + VALUES(interaction_score),
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update_time = CURRENT_TIMESTAMP
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""",
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receiver_social_rows,
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)
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# 4) 回填 unique_interactors:针对本条消息受影响的用户实时重算“去重互动人数”。
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affected_user_ids = [sender_id, *newly_mentioned_ids]
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self._refresh_unique_interactors(stat_date, group_id, affected_user_ids)
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self.LOG.debug(
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f"社交图写入完成: message_id={message_id}, group_id={group_id}, sender={sender_id}, "
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f"new_mentions={len(newly_mentioned_ids)}, affected_users={len(affected_user_ids)}"
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)
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except Exception as e:
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# 社交图统计属于增强链路,不能反向影响主消息入库稳定性。
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self.LOG.error(f"写入社交图增量数据失败: {e}")
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def _refresh_unique_interactors(self, stat_date: str, group_id: str, user_ids: List[str]) -> None:
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"""重算并回填用户在指定日期内的去重互动人数。
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定义:
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- 某用户当天主动@过的人 + 被谁@过(去重并集)
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"""
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if not user_ids:
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return
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deduped_user_ids = []
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seen = set()
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for uid in user_ids:
|
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normalized_uid = str(uid or "").strip()
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if not normalized_uid or normalized_uid in seen:
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continue
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seen.add(normalized_uid)
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deduped_user_ids.append(normalized_uid)
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|
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for uid in deduped_user_ids:
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try:
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row = self.execute_query(
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"""
|
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SELECT COUNT(DISTINCT partner_id) AS partner_count
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FROM (
|
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SELECT mentioned_user_id AS partner_id
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FROM t_message_mentions
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WHERE stat_date = %s AND group_id = %s AND sender_id = %s
|
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UNION
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SELECT sender_id AS partner_id
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FROM t_message_mentions
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WHERE stat_date = %s AND group_id = %s AND mentioned_user_id = %s
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) t
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""",
|
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(stat_date, group_id, uid, stat_date, group_id, uid),
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fetch_one=True,
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) or {}
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partner_count = int(row.get("partner_count") or 0)
|
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self.execute_update(
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"""
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UPDATE t_value_rank_social_daily
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SET unique_interactors = %s, update_time = CURRENT_TIMESTAMP
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WHERE stat_date = %s AND group_id = %s AND user_id = %s
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""",
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||||
(partner_count, stat_date, group_id, uid),
|
||||
)
|
||||
except Exception as e:
|
||||
self.LOG.error(f"回填 unique_interactors 失败: group={group_id}, user={uid}, err={e}")
|
||||
|
||||
def get_recent_messages(self, group_id: str, hours_ago: int = 8, min_content_length: int = 6) -> List[Dict]:
|
||||
"""获取最近的消息"""
|
||||
sql = """
|
||||
|
||||
Reference in New Issue
Block a user