121 lines
3.6 KiB
Python
121 lines
3.6 KiB
Python
"""深入诊断 SPI 数据时间分布"""
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import os
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from pathlib import Path
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from dotenv import load_dotenv
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load_dotenv(Path(__file__).resolve().parents[2] / ".env")
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PG_DSN = os.environ.get("PG_DSN")
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if not PG_DSN:
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raise RuntimeError("PG_DSN 未设置")
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import psycopg2
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import psycopg2.extras
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conn = psycopg2.connect(PG_DSN)
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conn.autocommit = True
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def q(sql, params=None):
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with conn.cursor(cursor_factory=psycopg2.extras.RealDictCursor) as cur:
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cur.execute(sql, params or ())
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return cur.fetchall()
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print("SPI 数据时间分布深入诊断")
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print("=" * 60)
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# 1. dwd_settlement_head 全量时间范围
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rows = q("""
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SELECT
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MIN(pay_time) AS min_pay, MAX(pay_time) AS max_pay,
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COUNT(*) AS total,
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COUNT(*) FILTER (WHERE settle_type IN (1, 3)) AS consume_cnt,
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COUNT(*) FILTER (WHERE settle_type = 2) AS recharge_cnt
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FROM dwd.dwd_settlement_head
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""")
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r = rows[0]
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print(f"dwd_settlement_head 全量:")
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print(f" 总记录: {r['total']}, 消费(1,3): {r['consume_cnt']}, 充值(2): {r['recharge_cnt']}")
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print(f" pay_time 范围: {r['min_pay']} ~ {r['max_pay']}")
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# 2. settle_type IN (1,3) 的最近数据
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rows2 = q("""
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SELECT pay_time::date AS pay_date, COUNT(*) AS cnt
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FROM dwd.dwd_settlement_head
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WHERE settle_type IN (1, 3)
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GROUP BY pay_time::date
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ORDER BY pay_date DESC
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LIMIT 20
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""")
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print(f"\n消费记录(settle_type IN 1,3) 最近 20 天:")
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for r in rows2:
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print(f" {r['pay_date']}: {r['cnt']} 条")
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# 3. SPI 用 NOW() - 90 days 作为截止，检查实际截止时间
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rows3 = q("SELECT NOW() AS now_ts, NOW() - INTERVAL '90 days' AS cutoff_90, NOW() - INTERVAL '30 days' AS cutoff_30")
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r = rows3[0]
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print(f"\n时间窗口:")
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print(f" NOW(): {r['now_ts']}")
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print(f" 90天截止: {r['cutoff_90']}")
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print(f" 30天截止: {r['cutoff_30']}")
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# 4. 在 90 天窗口内，按月统计消费记录
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rows4 = q("""
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SELECT
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DATE_TRUNC('month', pay_time)::date AS month,
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COUNT(*) AS cnt,
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COUNT(DISTINCT member_id) AS members,
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SUM(COALESCE(pay_amount, 0)) AS total_amount
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FROM dwd.dwd_settlement_head
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WHERE settle_type IN (1, 3)
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AND pay_time >= NOW() - INTERVAL '90 days'
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GROUP BY month
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ORDER BY month
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""")
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print(f"\n90天窗口内消费记录按月分布:")
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for r in rows4:
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print(f" {r['month']}: {r['cnt']} 条, {r['members']} 会员, 金额 {r['total_amount']}")
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# 5. 在 30 天窗口内的消费记录
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rows5 = q("""
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SELECT
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COUNT(*) AS cnt,
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COUNT(DISTINCT member_id) AS members,
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SUM(COALESCE(pay_amount, 0)) AS total_amount,
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MIN(pay_time) AS min_pay,
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MAX(pay_time) AS max_pay
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FROM dwd.dwd_settlement_head
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WHERE settle_type IN (1, 3)
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AND pay_time >= NOW() - INTERVAL '30 days'
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""")
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r = rows5[0]
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print(f"\n30天窗口内消费记录:")
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print(f" 记录数: {r['cnt']}, 会员数: {r['members']}, 总金额: {r['total_amount']}")
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print(f" 时间范围: {r['min_pay']} ~ {r['max_pay']}")
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# 6. 检查 ODS 层最新数据时间
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rows6 = q("""
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SELECT
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MIN(pay_time) AS min_pay, MAX(pay_time) AS max_pay, COUNT(*) AS cnt
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FROM ods.ods_settlement_records
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""")
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r = rows6[0]
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print(f"\nods_settlement_records:")
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print(f" 记录数: {r['cnt']}, pay_time 范围: {r['min_pay']} ~ {r['max_pay']}")
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# 7. 检查 DWD 层是否有 2 月中旬之后的数据
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rows7 = q("""
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SELECT pay_time::date AS pay_date, COUNT(*) AS cnt
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FROM dwd.dwd_settlement_head
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WHERE pay_time >= '2026-02-10'
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ORDER BY pay_date
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""")
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print(f"\nDWD 2026-02-10 之后的数据:")
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for r in rows7:
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print(f" {r['pay_date']}: {r['cnt']} 条")
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if not rows7:
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print(" 无数据!")
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conn.close()
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print("\n诊断完成。")
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