Spark SQL中开窗函数详解

忘是亡心i 2022-12-27 07:39 225阅读 0赞

**row\_number()开窗函数：** 其实就是给每个分组的数据，按照其排序的顺序，打上一个分组内的行号，相当于groupTopN，在实际应用中非常广泛。

需求分析：对上面数据表按照deptName分组，并按照salary降序排序，取出每个deptName组前两名。

数据源：

{ "deptName":"dept-1", "name":"Michael", "salary":3000}
    { "deptName":"dept-2", "name":"Andy", "salary":5000}
    { "deptName":"dept-1", "name":"Alex", "salary":4500}
    { "deptName":"dept-2", "name":"Justin", "salary":6700}
    { "deptName":"dept-2", "name":"Cherry", "salary":3400}
    { "deptName":"dept-1", "name":"Jack", "salary":5500}
    { "deptName":"dept-2", "name":"Jone", "salary":12000}
    { "deptName":"dept-1", "name":"Lucy", "salary":8000}
    { "deptName":"dept-2", "name":"LiLi", "salary":7600}
    { "deptName":"dept-2", "name":"Pony", "salary":4200}

初始化SparkSession

package com.kfk.spark.common
    
    import org.apache.spark.sql.SparkSession
    
    /** * @author : 蔡政洁 * @email :caizhengjie888@icloud.com * @date : 2020/12/2 * @time : 10:02 下午 */
    object CommSparkSessionScala { 
        def getSparkSession(): SparkSession ={ 
            val spark = SparkSession
                    .builder
                    .appName("CommSparkSessionScala")
                    .master("local")
                    .config("spark.sql.warehouse.dir", "/Users/caizhengjie/Document/spark/spark-warehouse")
                    .getOrCreate
    
            return spark
        }
    }

实现开窗函数

package com.kfk.spark.sql
    
    import com.kfk.spark.common.{ Comm, CommSparkSessionScala}
    
    /** * @author : 蔡政洁 * @email :caizhengjie888@icloud.com * @date : 2020/12/8 * @time : 12:22 下午 */
    object WindowFunctionScala { 
        def main(args: Array[String]): Unit = { 
            val spark = CommSparkSessionScala.getSparkSession()
    
            val userPath = Comm.fileDirPath + "users.json"
            spark.read.json(userPath).show()
    
            /** * +--------+-------+------+ * |deptName| name|salary| * +--------+-------+------+ * | dept-1|Michael| 3000| * | dept-2| Andy| 5000| * | dept-1| Alex| 4500| * | dept-2| Justin| 6700| * | dept-2| Cherry| 3400| * | dept-1| Jack| 5500| * | dept-2| Jone| 12000| * | dept-1| Lucy| 8000| * | dept-2| LiLi| 7600| * | dept-2| Pony| 4200| * +--------+-------+------+ */
    
            spark.read.json(userPath).createOrReplaceTempView("user")
    
            // 实现开窗函数：所谓开窗函数就是分组求TopN
            spark.sql("select deptName,name,salary,rank from" +
                    "(select deptName,name,salary,row_number() OVER (PARTITION BY deptName order by salary desc) rank from user) tempUser " +
                    "where rank <=2").show()
    
            /** * +--------+----+------+----+ * |deptName|name|salary|rank| * +--------+----+------+----+ * | dept-1|Lucy| 8000| 1| * | dept-1|Jack| 5500| 2| * | dept-2|Jone| 12000| 1| * | dept-2|LiLi| 7600| 2| * +--------+----+------+----+ */
    
            // 实现分组排序
            spark.sql("select * from user order by deptName,salary desc").show()
    
            /** * +--------+-------+------+ * |deptName| name|salary| * +--------+-------+------+ * | dept-1| Lucy| 8000| * | dept-1| Jack| 5500| * | dept-1| Alex| 4500| * | dept-1|Michael| 3000| * | dept-2| Jone| 12000| * | dept-2| LiLi| 7600| * | dept-2| Justin| 6700| * | dept-2| Andy| 5000| * | dept-2| Pony| 4200| * | dept-2| Cherry| 3400| * +--------+-------+------+ */
    
        }
    }

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