10.行转列-sparksql-pivot子句
今天给大家带来spark的一个新的知识点,pivot,这个不是函数,是一个子句。
描述
PIVOT
子句用于数据透视。我们可以根据特定列的值获取聚合值,这些值将转变成在 SELECT 子句中使用的多个列。PIVOT 子句可以在表名或子查询之后指定。
语法
PIVOT ( { aggregate_expression [ AS aggregate_expression_alias ] } [ , ... ]
FOR column_list IN ( expression_list ) )
参数
- aggregate_expression 一个聚合表达式(例如 sum(a),count(distinct b) 等等
- aggregate_expression_alias 聚合表达式的别名
- column_list 包含 FROM 子句中的列,这些列是我们想要用新列替换的列。我们可以使用括号来包围这些列,例如 (c1, c2)。
- expression_list 指定新列,这些新列用于将 column_list 中的值作为聚合条件进行匹配。我们还可以为它们添加别名。
样例
CREATE TABLE person (id INT, name STRING, age INT, class INT, address STRING);
INSERT INTO person VALUES
(100, 'John', 30, 1, 'Street 1'),
(200, 'Mary', NULL, 1, 'Street 2'),
(300, 'Mike', 80, 3, 'Street 3'),
(400, 'Dan', 50, 4, 'Street 4');
SELECT * FROM person
PIVOT (
SUM(age) AS a, AVG(class) AS c
FOR name IN ('John' AS john, 'Mike' AS mike)
);
+------+-----------+---------+---------+---------+---------+
| id | address | john_a | john_c | mike_a | mike_c |
+------+-----------+---------+---------+---------+---------+
| 200 | Street 2 | NULL | NULL | NULL | NULL |
| 100 | Street 1 | 30 | 1.0 | NULL | NULL |
| 300 | Street 3 | NULL | NULL | 80 | 3.0 |
| 400 | Street 4 | NULL | NULL | NULL | NULL |
+------+-----------+---------+---------+---------+---------+
SELECT * FROM person
PIVOT (
SUM(age) AS a, AVG(class) AS c
FOR (name, age) IN (('John', 30) AS c1, ('Mike', 40) AS c2)
);
+------+-----------+-------+-------+-------+-------+
| id | address | c1_a | c1_c | c2_a | c2_c |
+------+-----------+-------+-------+-------+-------+
| 200 | Street 2 | NULL | NULL | NULL | NULL |
| 100 | Street 1 | 30 | 1.0 | NULL | NULL |
| 300 | Street 3 | NULL | NULL | NULL | NULL |
| 400 | Street 4 | NULL | NULL | NULL | NULL |
+------+-----------+-------+-------+-------+-------+
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