feat(management): 员工指标宽表——1:1口径/串类与fact同源/生命周期列/幽灵过滤/纯生命周期日出行
Co-Authored-By: Claude Code <noreply@anthropic.com>
This commit is contained in:
浅念
@@ -12,12 +12,28 @@
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from __future__ import annotations
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import logging
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import math
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from bisect import bisect_right
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from collections.abc import Iterable
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import sqlalchemy as sa
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.db.models import EmployeeCallDaily, Message, PhoneCall, Staff
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from app.analytics.lifecycle import classify
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from app.db.models import (
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Customer,
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EmployeeCallDaily,
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EmployeeMetricDaily,
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Message,
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MsgDailyStats,
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PhoneCall,
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Staff,
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)
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from app.storage.first_reply_facts import (
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compute_conversation,
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load_conversation,
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pairs_active_in_days,
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)
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from app.storage.staff_summary import account_map
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from app.storage.summary import day_bounds_ms
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@@ -131,3 +147,202 @@ async def recompute_call_days(session: AsyncSession, days: Iterable[int]) -> int
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)
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written += 1
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return written
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METRIC_VERSION = "v1"
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def _percentile(vals: list[int], q: float) -> int | None:
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"""最近邻秩分位 ceil(q·n);空列表→None(与 0 严格区分)。"""
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if not vals:
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return None
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s = sorted(vals)
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idx = min(len(s) - 1, max(0, math.ceil(q * len(s)) - 1))
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return s[idx]
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async def _lifecycle_daily(
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session: AsyncSession,
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days: list[int],
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channel: str,
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amap: dict[str, str],
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valid_users: set[str],
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) -> dict[int, dict[str, dict[str, int]]]:
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"""逐日 {day: {user: {state: count}}}——customer 当前归属 + 互动日 sweep。
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数据源:msg_daily_stats friend 行全历史(互动日/条数)。16k 客户 × 窗口
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天数 bisect,夜间全量约秒级;4h tick 只算当日+3 天。
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"""
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rows = (
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await session.execute(
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sa.select(
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MsgDailyStats.peer_id, MsgDailyStats.day,
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MsgDailyStats.send_cnt, MsgDailyStats.recv_cnt,
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).where(
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MsgDailyStats.channel == channel,
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MsgDailyStats.peer_kind == "friend",
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)
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)
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).all()
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days_map: dict[str, set[int]] = {}
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cnt: dict[str, int] = {}
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for peer, day, snd, rcv in rows:
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days_map.setdefault(peer, set()).add(day)
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cnt[peer] = cnt.get(peer, 0) + int(snd or 0) + int(rcv or 0)
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interaction = {p: sorted(ds) for p, ds in days_map.items()}
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custs = (
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await session.execute(
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sa.select(Customer.owner_account_id, Customer.friend_id).where(
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Customer.channel == channel,
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sa.or_(Customer.delete_flag.is_(None), Customer.delete_flag == 0),
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Customer.owner_account_id.is_not(None),
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)
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)
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).all()
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peer_to_user: dict[str, str] = {}
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for owner, peer in custs:
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uid = amap.get(owner or "")
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if uid and uid in valid_users and peer:
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peer_to_user[str(peer)] = uid
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out: dict[int, dict[str, dict[str, int]]] = {d: {} for d in days}
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for peer, days_list in interaction.items():
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uid = peer_to_user.get(peer)
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if uid is None:
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continue
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first = days_list[0]
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hist = cnt.get(peer, 0)
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for d in days:
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idx = bisect_right(days_list, d) - 1
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if idx < 0:
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continue
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last = days_list[idx]
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prev = days_list[idx - 1] if idx > 0 else None
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state = classify(first, last, prev, d, hist)
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if state:
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out[d].setdefault(uid, {}).setdefault(state, 0)
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out[d][uid][state] += 1
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return out
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async def recompute_metric_days(
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session: AsyncSession,
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days: Iterable[int],
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channels: tuple[str, ...] = ("wechat", "whatsapp"),
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) -> int:
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"""重算给定日的宽表:按 (day, channel) delete+insert,幂等。返回写入行数。
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串类列与首响日值:扫窗口内活跃 friend 会话的**全历史**(每会话一次),
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与 first_reply_fact 同一 compute_conversation——分子分母同源(v2 §1.1 #5)。
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"""
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days = sorted(set(days))
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valid_users = await known_user_ids(session)
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pairs = await pairs_active_in_days(session, days)
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pairs_by_channel: dict[str, list] = {}
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for p in pairs:
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pairs_by_channel.setdefault(p[0], []).append((p[1], p[2]))
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written = 0
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for channel in channels:
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amap = await account_map(session, channel)
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# ---- 串类+分位:逐会话全历史一次,聚合到 (day, user) ----
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thread_cols: dict[tuple[int, str], dict] = {}
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frs: dict[tuple[int, str], list[int]] = {}
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day_set = set(days)
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for owner, peer in pairs_by_channel.get(channel, []):
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uid = amap.get(owner)
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if uid is None or uid not in valid_users:
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continue
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rows = await load_conversation(session, channel, owner, peer)
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facts, agg = compute_conversation(rows)
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for d, a in agg.items():
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if d not in day_set:
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continue
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key = (d, uid)
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t = thread_cols.setdefault(
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key, {"threads": 0, "answered": 0, "fr": 0}
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)
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t["threads"] += a.threads
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t["answered"] += a.answered
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t["fr"] += a.fr_sum_ms
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frs.setdefault(key, []).extend(
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f.fr_ms for f in facts if f.day == d
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)
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# ---- 生命周期 ----
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life = await _lifecycle_daily(session, days, channel, amap, valid_users)
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# ---- 消息计数+负载:msg_daily_stats(算法无关列) ----
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for day in days:
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mds = (
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await session.execute(
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sa.select(
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MsgDailyStats.owner_account_id,
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MsgDailyStats.peer_kind,
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MsgDailyStats.peer_id,
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MsgDailyStats.send_cnt,
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MsgDailyStats.recv_cnt,
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).where(
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MsgDailyStats.day == day, MsgDailyStats.channel == channel
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)
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)
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).all()
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await session.execute(
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sa.delete(EmployeeMetricDaily).where(
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EmployeeMetricDaily.day == day,
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EmployeeMetricDaily.channel == channel,
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EmployeeMetricDaily.metric_version == METRIC_VERSION,
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)
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)
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agg: dict[str, dict] = {}
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for owner, kind, peer, snd, rcv in mds:
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uid = amap.get(owner)
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if uid is None or uid not in valid_users:
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continue # 幽灵过滤
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a = agg.setdefault(
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uid,
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{"sent": 0, "recv": 0, "friends": set(), "rooms": set()},
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)
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# 消息计数列 = 1:1 口径(v2 §1.1 #2):room 只进 active_rooms
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if kind == "friend":
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a["sent"] += int(snd or 0)
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a["recv"] += int(rcv or 0)
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a["friends"].add(peer)
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elif kind == "room":
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a["rooms"].add(peer)
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day_life = life.get(day, {})
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# 行并集:消息源 ∪ 生命周期源——纯生命周期日(员工当日无消息
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# 但持有降温/沉睡客户)也要出宽表行,否则客户经营维度漏天
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all_uids = sorted(set(agg) | set(day_life))
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for uid in all_uids:
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a = agg.get(
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uid, {"sent": 0, "recv": 0, "friends": set(), "rooms": set()}
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)
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key = (day, uid)
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t = thread_cols.get(key, {"threads": 0, "answered": 0, "fr": 0})
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lv = day_life.get(uid, {})
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session.add(
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EmployeeMetricDaily(
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day=day,
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channel=channel,
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user_id=uid,
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metric_version=METRIC_VERSION,
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msg_sent=a["sent"],
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msg_recv=a["recv"],
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active_friends=len(a["friends"]),
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active_rooms=len(a["rooms"]),
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cust_threads=t["threads"],
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threads_answered=t["answered"],
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fr_sum_ms=t["fr"],
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first_reply_p50_ms=_percentile(frs.get(key, []), 0.5),
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first_reply_p90_ms=_percentile(frs.get(key, []), 0.9),
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new_customer_count=lv.get("new", 0),
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cooling_customer_count=lv.get("cooling", 0),
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dormant_customer_count=lv.get("dormant", 0),
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reactivated_customer_count=lv.get("reactivated", 0),
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data_complete=True,
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)
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)
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written += 1
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return written
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