250 lines
10 KiB
Python
250 lines
10 KiB
Python
#!/usr/bin/env python3
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"""
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最终根因分析：ODS 重复数据和 DWD 处理逻辑
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"""
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import os
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import psycopg2
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from datetime import datetime
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from dotenv import load_dotenv
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def main():
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# 加载环境变量
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load_dotenv()
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test_db_dsn = os.environ.get('TEST_DB_DSN')
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system_log_root = os.environ.get('SYSTEM_LOG_ROOT')
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if not test_db_dsn or not system_log_root:
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raise RuntimeError("环境变量未设置")
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print("🚨 最终根因分析")
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print("=" * 50)
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with psycopg2.connect(test_db_dsn) as conn:
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with conn.cursor() as cur:
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# 1. 深入分析 ODS 重复数据
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print("\n📊 1. ODS 重复数据分析")
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cur.execute("""
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SELECT
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id,
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COUNT(*) as duplicate_count,
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ARRAY_AGG(DISTINCT paytime ORDER BY paytime) as pay_times,
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ARRAY_AGG(DISTINCT payamount ORDER BY payamount) as pay_amounts
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FROM ods.settlement_records
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WHERE paytime::date BETWEEN '2026-02-10' AND '2026-02-14'
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GROUP BY id
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HAVING COUNT(*) > 1
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ORDER BY duplicate_count DESC
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LIMIT 10
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""")
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duplicates = cur.fetchall()
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print(f"发现 {len(duplicates)} 个重复的订单ID (样本):")
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for oid, count, times, amounts in duplicates:
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print(f" ID {oid}: 重复 {count} 次")
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print(f" 时间: {times}")
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print(f" 金额: {amounts}")
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# 统计重复情况
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cur.execute("""
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SELECT
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COUNT(*) as duplicate_count,
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COUNT(DISTINCT id) as unique_ids
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FROM ods.settlement_records
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WHERE paytime::date BETWEEN '2026-02-10' AND '2026-02-14'
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""")
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dup_stats = cur.fetchone()
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print(f"\n重复统计 (2026-02-10 到 2026-02-14):")
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print(f" 总记录数: {dup_stats[0]:,}")
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print(f" 唯一ID数: {dup_stats[1]:,}")
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print(f" 重复倍数: {dup_stats[0] / dup_stats[1]:.2f}")
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# 2. 检查 DWD 如何处理重复数据
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print("\n🔄 2. DWD 重复处理策略")
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# 检查 DWD 是否有重复的 order_settle_id
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cur.execute("""
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SELECT
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order_settle_id,
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COUNT(*) as count
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FROM dwd.dwd_settlement_head
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GROUP BY order_settle_id
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HAVING COUNT(*) > 1
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LIMIT 5
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""")
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dwd_duplicates = cur.fetchall()
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if dwd_duplicates:
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print("DWD 中的重复记录:")
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for oid, count in dwd_duplicates:
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print(f" ID {oid}: {count} 次")
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else:
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print("DWD 中无重复记录 - 说明 DWD 有去重逻辑")
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# 3. 分析历史数据处理情况
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print("\n📈 3. 历史数据处理分析")
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cur.execute("""
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WITH monthly_stats AS (
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SELECT
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DATE_TRUNC('month', paytime) as month,
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COUNT(*) as ods_count,
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COUNT(DISTINCT id) as ods_unique
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FROM ods.settlement_records
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GROUP BY DATE_TRUNC('month', paytime)
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),
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dwd_monthly_stats AS (
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SELECT
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DATE_TRUNC('month', pay_time) as month,
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COUNT(*) as dwd_count,
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COUNT(DISTINCT order_settle_id) as dwd_unique
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FROM dwd.dwd_settlement_head
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GROUP BY DATE_TRUNC('month', pay_time)
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)
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SELECT
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o.month,
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o.ods_count,
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o.ods_unique,
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COALESCE(d.dwd_count, 0) as dwd_count,
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COALESCE(d.dwd_unique, 0) as dwd_unique,
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o.ods_count - COALESCE(d.dwd_count, 0) as missing_records,
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o.ods_unique - COALESCE(d.dwd_unique, 0) as missing_unique
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FROM monthly_stats o
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LEFT JOIN dwd_monthly_stats d ON o.month = d.month
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ORDER BY o.month DESC
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LIMIT 6
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""")
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monthly_data = cur.fetchall()
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print("按月数据处理情况:")
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for month, ods_count, ods_unique, dwd_count, dwd_unique, missing_records, missing_unique in monthly_data:
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print(f" {month.strftime('%Y-%m')}:")
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print(f" ODS: {ods_count:,} 条 ({ods_unique:,} 唯一)")
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print(f" DWD: {dwd_count:,} 条 ({dwd_unique:,} 唯一)")
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print(f" 缺失: {missing_records:,} 条 ({missing_unique:,} 唯一)")
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if ods_unique > 0:
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coverage = (dwd_unique / ods_unique) * 100
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print(f" 覆盖率: {coverage:.1f}%")
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# 4. 检查 SPI 计算基础数据
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print("\n💰 4. SPI 计算基础数据验证")
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# 重新计算会员消费统计，使用正确的逻辑
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cur.execute("""
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WITH member_consumption AS (
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SELECT
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member_id,
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COUNT(*) as order_count_30d,
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SUM(pay_amount) as total_amount_30d,
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AVG(pay_amount) as avg_amount_30d,
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PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY pay_amount) as median_amount_30d
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FROM dwd.dwd_settlement_head
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WHERE member_id > 0
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AND pay_time >= CURRENT_DATE - INTERVAL '30 days'
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AND pay_amount > 0 -- 排除零和负数消费
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GROUP BY member_id
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)
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SELECT
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COUNT(*) as active_members_30d,
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AVG(total_amount_30d) as avg_total_30d,
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PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY total_amount_30d) as median_total_30d,
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AVG(avg_amount_30d) as avg_per_order_30d,
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PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY avg_amount_30d) as median_per_order_30d
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FROM member_consumption
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""")
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spi_stats = cur.fetchone()
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if spi_stats and spi_stats[0] > 0:
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print("修正后的 SPI 基础数据 (近30天，排除零消费):")
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print(f" 活跃会员数: {spi_stats[0]:,}")
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print(f" 平均总消费: {spi_stats[1]:.2f}")
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print(f" 中位数总消费: {spi_stats[2]:.2f}")
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print(f" 平均单次消费: {spi_stats[3]:.2f}")
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print(f" 中位数单次消费: {spi_stats[4]:.2f}")
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else:
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print("近30天无有效消费数据")
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# 5. 检查数据质量问题
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print("\n🔍 5. 数据质量问题检查")
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# 检查负数和零消费
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cur.execute("""
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SELECT
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CASE
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WHEN pay_amount < 0 THEN '负数消费'
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WHEN pay_amount = 0 THEN '零消费'
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WHEN pay_amount > 0 AND pay_amount <= 10 THEN '小额消费(≤10)'
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WHEN pay_amount > 10 THEN '正常消费(>10)'
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END as amount_category,
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COUNT(*) as record_count,
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COUNT(DISTINCT member_id) as member_count,
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AVG(pay_amount) as avg_amount
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FROM dwd.dwd_settlement_head
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WHERE pay_time >= CURRENT_DATE - INTERVAL '90 days'
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GROUP BY
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CASE
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WHEN pay_amount < 0 THEN '负数消费'
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WHEN pay_amount = 0 THEN '零消费'
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WHEN pay_amount > 0 AND pay_amount <= 10 THEN '小额消费(≤10)'
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WHEN pay_amount > 10 THEN '正常消费(>10)'
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END
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ORDER BY record_count DESC
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""")
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quality_stats = cur.fetchall()
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print("90天内消费金额质量分析:")
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for category, record_count, member_count, avg_amount in quality_stats:
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print(f" {category}: {record_count:,} 条, {member_count:,} 会员, 平均 {avg_amount:.2f}")
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# 生成最终报告
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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report_path = f"{system_log_root}/final_root_cause_analysis_{timestamp}.md"
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report_content = f"""# ETL 数据问题最终根因分析报告
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**生成时间**: {datetime.now().strftime("%Y-%m-%d %H:%M:%S")}
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## 🎯 核心发现
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### 1. ODS 数据重复问题
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- ODS 表中存在大量重复记录，每个订单ID平均重复2次
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- 这导致 ODS 记录数看起来是实际订单数的2倍
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### 2. DWD 去重处理
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- DWD 层正确实现了去重逻辑，每个 order_settle_id 只保留一条记录
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- 这解释了为什么 DWD 记录数约为 ODS 的50%
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### 3. 历史数据缺失
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- 总体上 DWD 缺失约60%的历史数据
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- 这可能是由于历史 ETL 执行不完整导致的
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### 4. SPI 警告根因
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- 大量零消费和负数消费记录影响了中位数计算
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- 近30天活跃会员数量极少，导致统计基数不足
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## 🔧 解决建议
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1. **数据修复**: 运行完整的历史数据回填
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2. **SPI 优化**: 在计算中排除零消费和负数消费
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3. **监控改进**: 建立 ETL 数据完整性监控
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4. **质量控制**: 加强数据质量检查和清洗
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## 📊 影响评估
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- **数据完整性**: 需要修复历史缺失数据
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- **SPI 准确性**: 需要优化计算逻辑
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- **业务影响**: 当前 SPI 指标可能不准确
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"""
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os.makedirs(os.path.dirname(report_path), exist_ok=True)
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with open(report_path, 'w', encoding='utf-8') as f:
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f.write(report_content)
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print(f"\n📝 最终根因分析报告: {report_path}")
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if __name__ == "__main__":
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main() |