Add one-skill
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Xin Wang
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-- =====================================================================
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-- @Name: HIVE-D-SQL-{表名}-QUERY
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-- @Version: 1.0
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-- @Desc: Hive 查询模板
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-- @TargetDatabase: Hive
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-- =====================================================================
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-- ============================================================================
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-- 1. 单表查询
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-- ============================================================================
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SELECT
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id,
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name,
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amount,
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created_at
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FROM db_name.source_table
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WHERE day_id = '${day_id}' -- 分区过滤（必须）
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AND status = 'active'
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ORDER BY created_at DESC
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LIMIT 1000;
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-- ============================================================================
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-- 2. JOIN 查询
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-- ============================================================================
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-- 两表 JOIN
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SELECT
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a.id,
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a.name,
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a.amount,
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b.category_name
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FROM db_name.main_table a
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JOIN db_name.dim_table b ON a.category_id = b.id
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WHERE a.day_id = '${day_id}';
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-- 多表 JOIN（带别名）
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SELECT
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o.order_id,
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u.user_name,
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p.product_name,
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oi.quantity,
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oi.unit_price
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FROM db_name.orders o
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JOIN db_name.users u ON o.user_id = u.id
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JOIN db_name.order_items oi ON o.order_id = oi.order_id
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JOIN db_name.products p ON oi.product_id = p.id
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WHERE o.day_id = '${day_id}'
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AND o.status IN ('completed', 'shipped');
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-- ============================================================================
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-- 3. 聚合查询
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-- ============================================================================
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-- GROUP BY + HAVING
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SELECT
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department,
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COUNT(*) AS employee_count,
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SUM(salary) AS total_salary,
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AVG(salary) AS avg_salary,
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MAX(salary) AS max_salary
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FROM db_name.employees
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WHERE day_id = '${day_id}'
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GROUP BY department
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HAVING COUNT(*) >= 5
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ORDER BY total_salary DESC;
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-- 多字段分组 + 去重计数
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SELECT
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date,
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region,
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COUNT(*) AS order_count,
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COUNT(DISTINCT user_id) AS unique_users,
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SUM(amount) AS total_amount
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FROM db_name.orders
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WHERE day_id = '${day_id}'
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GROUP BY date, region;
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-- ============================================================================
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-- 4. 窗口函数
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-- ============================================================================
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-- ROW_NUMBER（分组取Top N）
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SELECT *
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FROM (
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SELECT
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department,
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name,
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salary,
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ROW_NUMBER() OVER (PARTITION BY department ORDER BY salary DESC) AS rn
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FROM db_name.employees
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WHERE day_id = '${day_id}'
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) t
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WHERE rn <= 3;
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-- 累计聚合
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SELECT
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date,
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amount,
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SUM(amount) OVER (ORDER BY date) AS cumulative_amount,
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AVG(amount) OVER (
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ORDER BY date
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ROWS BETWEEN 6 PRECEDING AND CURRENT ROW
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) AS moving_avg_7d
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FROM daily_sales
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WHERE day_id = '${day_id}';
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-- LAG/LEAD（环比）
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SELECT
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date,
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amount,
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LAG(amount, 1) OVER (ORDER BY date) AS prev_amount,
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amount - LAG(amount, 1) OVER (ORDER BY date) AS daily_change
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FROM daily_sales
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WHERE day_id = '${day_id}';
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-- ============================================================================
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-- 5. 子查询
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-- ============================================================================
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-- IN 子查询
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SELECT *
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FROM db_name.orders
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WHERE user_id IN (
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SELECT id FROM db_name.users WHERE vip_level >= 3
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)
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AND day_id = '${day_id}';
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-- EXISTS 子查询
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SELECT *
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FROM db_name.products p
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WHERE EXISTS (
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SELECT 1 FROM db_name.inventory i
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WHERE i.product_id = p.id
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AND i.quantity > 0
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)
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AND p.day_id = '${day_id}';
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-- ============================================================================
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-- 6. 条件聚合（CASE WHEN + 聚合）
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-- ============================================================================
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SELECT
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date,
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COUNT(*) AS total_orders,
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SUM(CASE WHEN status = 'completed' THEN 1 ELSE 0 END) AS completed_count,
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SUM(CASE WHEN status = 'cancelled' THEN 1 ELSE 0 END) AS cancelled_count,
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SUM(CASE WHEN amount > 1000 THEN amount ELSE 0 END) AS high_value_amount
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FROM db_name.orders
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WHERE day_id = '${day_id}'
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GROUP BY date;
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-- ============================================================================
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-- 7. LATERAL VIEW + explode（Hive 特有）
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-- ============================================================================
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-- 展开数组字段
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SELECT
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id,
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name,
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tag
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FROM db_name.articles
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LATERAL VIEW explode(tags) t AS tag
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WHERE day_id = '${day_id}';
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-- 展开数组并统计
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SELECT
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tag,
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COUNT(*) AS article_count
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FROM db_name.articles
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LATERAL VIEW explode(tags) t AS tag
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WHERE day_id = '${day_id}'
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GROUP BY tag;
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-- 展开 Map
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SELECT
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id,
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map_key,
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map_value
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FROM db_name.data_table
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LATERAL VIEW explode(props) m AS map_key, map_value
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WHERE day_id = '${day_id}';
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-- posexplode（带索引展开）
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SELECT
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id,
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pos,
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tag
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FROM db_name.articles
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LATERAL VIEW posexplode(tags) t AS pos, tag
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WHERE day_id = '${day_id}';
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-- ============================================================================
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-- 8. 复杂类型查询
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-- ============================================================================
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-- ARRAY 操作
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SELECT
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id,
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size(tags) AS tag_count, -- 数组长度
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array_contains(tags, '大数据') AS has_tag, -- 包含判断
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tags[0] AS first_tag -- 取第一个元素
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FROM db_name.articles
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WHERE day_id = '${day_id}';
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-- MAP 操作
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SELECT
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id,
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props['city'] AS city, -- 取值
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map_keys(props) AS all_keys, -- 所有 key
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map_values(props) AS all_values -- 所有 value
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FROM db_name.user_table
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WHERE day_id = '${day_id}';
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-- STRUCT 操作
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SELECT
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id,
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user_info.name AS user_name, -- 结构体字段访问
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user_info.age AS user_age
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FROM db_name.data_table
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WHERE day_id = '${day_id}';
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-- ============================================================================
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-- 9. 集合聚合
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-- ============================================================================
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-- collect_list / collect_set
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SELECT
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department,
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collect_list(name) AS all_names, -- 收集为数组（不去重）
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collect_set(name) AS unique_names, -- 收集为数组（去重）
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size(collect_set(name)) AS unique_count
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FROM db_name.employees
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WHERE day_id = '${day_id}'
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GROUP BY department;
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