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