Add one-skill

one-skill/smart-data-developer/references/sql/templates/hive/etl-template.sql

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+-- =====================================================================
+-- @Name:             HIVE-D-SQL-{表名}-ETL
+-- @Version:          1.0
+-- @Desc:             Hive ETL 数据处理模板（临时表链式处理）
+-- @TargetDatabase:   Hive
+-- @说明:             和 Spark 类似，禁止 CTE，每步物化为临时表
+-- =====================================================================
+
+-- ============================================================================
+-- Step01: 基础清洗与过滤
+-- ============================================================================
+-- 说明：从源表读取数据，进行基础过滤和清洗
+-- 输入：{源表名}
+-- 输出：tmp_etl_01
+
+DROP TABLE IF EXISTS ${db_tmp_env}.tmp_xxx_01;
+CREATE TABLE ${db_tmp_env}.tmp_xxx_01 AS
+SELECT 
+    id,
+    name,
+    amount,
+    status,
+    dept_id,
+    category_id,
+    created_at,
+    day_id
+FROM db_name.source_table
+WHERE day_id = '${day_id}'              -- 分区过滤（必须）
+  AND status IN ('active', 'valid')     -- 业务过滤
+  AND amount > 0                        -- 数据质量过滤
+  AND id IS NOT NULL;                   -- NULL过滤
+
+-- ============================================================================
+-- Step02: 多表关联与维度补全
+-- ============================================================================
+-- 说明：关联维度表，补全业务属性字段
+-- 输入：tmp_xxx_01, dim_department, dim_category
+-- 输出：tmp_xxx_02
+
+DROP TABLE IF EXISTS ${db_tmp_env}.tmp_xxx_02;
+CREATE TABLE ${db_tmp_env}.tmp_xxx_02 AS
+SELECT 
+    a.id,
+    a.name,
+    a.amount,
+    a.status,
+    b.dept_name,                        -- 维度补全：部门名称
+    c.category_name,                    -- 维度补全：类别名称
+    a.created_at,
+    a.day_id
+FROM ${db_tmp_env}.tmp_xxx_01 a
+LEFT JOIN db_name.dim_department b 
+    ON a.dept_id = b.dept_id
+    AND b.day_id = '${day_id}'          -- 维度表分区过滤
+LEFT JOIN db_name.dim_category c 
+    ON a.category_id = c.category_id
+    AND c.day_id = '${day_id}';         -- 维度表分区过滤
+
+-- ============================================================================
+-- Step03: 聚合计算与指标生成
+-- ============================================================================
+-- 说明：按业务维度聚合，计算统计指标
+-- 输入：tmp_xxx_02
+-- 输出：tmp_xxx_03
+
+DROP TABLE IF EXISTS ${db_tmp_env}.tmp_xxx_03;
+CREATE TABLE ${db_tmp_env}.tmp_xxx_03 AS
+SELECT 
+    day_id,
+    dept_name,
+    category_name,
+    COUNT(*) AS record_count,           -- 记录数
+    COUNT(DISTINCT id) AS unique_count, -- 唯一计数
+    SUM(amount) AS total_amount,        -- 总金额
+    AVG(amount) AS avg_amount,          -- 平均金额
+    MAX(amount) AS max_amount,          -- 最大金额
+    MIN(amount) AS min_amount           -- 最小金额
+FROM ${db_tmp_env}.tmp_xxx_02
+GROUP BY day_id, dept_name, category_name;
+
+-- ============================================================================
+-- Step04: 最终输出写入目标表
+-- ============================================================================
+-- 说明：补全目标表标准字段，写入结果表
+-- 输入：tmp_xxx_03
+-- 输出：目标表
+
+INSERT OVERWRITE TABLE ${db_eda_env}.target_table
+PARTITION (day_id = '${day_id}')
+SELECT 
+    -- 业务字段
+    dept_name,
+    category_name,
+    record_count,
+    unique_count,
+    total_amount,
+    avg_amount,
+    max_amount,
+    min_amount,
+    
+    -- 技术字段
+    current_timestamp() AS etl_time,    -- 数据加工时间
+    '${day_id}' AS stat_date;           -- 统计日期
+
+-- ============================================================================
+-- 关键规则说明
+-- ============================================================================
+/*
+1. 禁止使用 CTE (WITH 子句)
+   - 每个步骤必须物化为临时表
+   - 原因：Hive CTE 可能在某些版本有性能问题
+   - 物化临时表便于调试和断点续跑
+
+2. 先 DROP 再 CREATE
+   - 每个临时表创建前必须先 DROP TABLE IF EXISTS
+   - 原因：防止表已存在导致失败
+
+3. 分区过滤必须前置
+   - 所有源表和维度表查询必须带 day_id 过滤
+   - 原因：避免全表扫描，提升性能
+
+4. JOIN 条件下推
+   - 维度表关联时带上分区过滤条件
+   - 原因：减少关联数据量
+
+5. 临时表命名规范
+   - 格式：tmp_{业务简称}_{步骤序号}
+   - 示例：tmp_order_stats_01, tmp_order_stats_02
+
+6. 目标表写入规范
+   - 使用 INSERT OVERWRITE（覆盖写入，幂等）
+   - 明确指定分区
+   - 补全技术字段（etl_time 等）
+
+7. 存储格式建议
+   - 临时表：默认格式即可（中间结果不需要优化存储）
+   - 如需优化：STORED AS ORC
+*/