sparksql having

`HAVING` is a clause in Spark SQL that is used to filter data based on an aggregate function's result. It is typically used in combination with the `GROUP BY` clause to filter the result set based on specific criteria.

Here is an example of using `HAVING` in Spark SQL:

SELECT department, AVG(salary) as avg_salary
FROM employee
GROUP BY department
HAVING AVG(salary) > 50000;

In this example, we are selecting the average salary for each department from the `employee` table and filtering the result set to only include departments where the average salary is greater than 50000. The `HAVING` clause filters the result set based on the `AVG(salary)` aggregate function's result.

相关问题

sparksql sql遍历语句

Spark SQL支持常见的SQL语句，包括SELECT、FROM、WHERE、GROUP BY、HAVING、ORDER BY等。下面是一个简单的Spark SQL查询语句：

SELECT name, age FROM people WHERE age > 18;

这个查询语句从`people`表中选择`name`和`age`列，其中`age`大于18。

Spark SQL还支持子查询，如以下语句：

SELECT name, age FROM (SELECT * FROM people WHERE age > 18) subquery WHERE name LIKE 'A%';

这个查询语句首先从`people`表中选择年龄大于18的行，并将结果保存为子查询。然后，从子查询中选择`name`和`age`列，其中`name`以字母`A`开头。

Spark SQL还支持JOIN、UNION、EXCEPT、INTERSECT等操作，可以用来处理多个表之间的关系。例如：

SELECT p.name, s.grade FROM people p JOIN scores s ON p.id = s.id WHERE s.subject = 'Math';

这个查询语句将`people`表和`scores`表连接起来，使用`id`列作为连接条件，并选择`name`和`grade`列，其中`subject`为`Math`。

总之，Spark SQL支持SQL语句中常见的查询、过滤、聚合、连接等操作，可以方便地操作和分析大规模数据集。