强类型的Dataset和弱类型的DataFrame都提供了相关的聚合函数, 如 count(),countDistinct(),avg(),max(),min()。除此之外,用户可以设定自己的自定义聚合函数。
弱类型用户自定义聚合函数:通过继承UserDefinedAggregateFunction来实现用户自定义聚合函数。下面展示一个求平均工资的自定义聚合函数。
object MyAverage extends UserDefinedAggregateFunction {
// 聚合函数输入参数的数据类型
def inputSchema: StructType = StructType(StructField("inputColumn", LongType) :: Nil)
// 聚合缓冲区中值得数据类型
def bufferSchema: StructType = {
StructType(StructField("sum", LongType) :: StructField("count", LongType) :: Nil)
}
// 返回值的数据类型
def dataType: DataType = DoubleType
// 对于相同的输入是否一直返回相同的输出。
def deterministic: Boolean = true
// 初始化
def initialize(buffer: MutableAggregationBuffer): Unit = {
// 存工资的总额
buffer(0) = 0L
// 存工资的个数
buffer(1) = 0L
}
// 相同Execute间的数据合并。
def update(buffer: MutableAggregationBuffer, input: Row): Unit = {
if (!input.isNullAt(0)) {
buffer(0) = buffer.getLong(0) + input.getLong(0)
buffer(1) = buffer.getLong(1) + 1
}
}
// 不同Execute间的数据合并
def merge(buffer1: MutableAggregationBuffer, buffer2: Row): Unit = {
buffer1(0) = buffer1.getLong(0) + buffer2.getLong(0)
buffer1(1) = buffer1.getLong(1) + buffer2.getLong(1)
}
// 计算最终结果
def evaluate(buffer: Row): Double = buffer.getLong(0).toDouble / buffer.getLong(1)
}
// 注册函数
spark.udf.register("myAverage", MyAverage)
val df = spark.read.json("/root/employees.json")
df.createOrReplaceTempView("employees")
df.show()
// +-------+------+
// | name|salary|
// +-------+------+
// |Michael| 3000|
// | Andy| 4500|
// | Justin| 3500|
// | Berta| 4000|
// +-------+------+
val result = spark.sql("SELECT myAverage(salary) as average_salary FROM employees")
result.show()
// +--------------+
// |average_salary|
// +--------------+
// | 3750.0|
// +--------------+
强类型用户自定义聚合函数:通过继承Aggregator来实现强类型自定义聚合函数,同样是求平均工资
// 既然是强类型,可能有case类
case class Employee(name: String, salary: Long)
case class Average(var sum: Long, var count: Long)
object MyAverage extends Aggregator[Employee, Average, Double] {
// 定义一个数据结构,保存工资总数和工资总个数,初始都为0
def zero: Average = Average(0L, 0L)
// Combine two values to produce a new value. For performance, the function may modify `buffer`
// and return it instead of constructing a new object
def reduce(buffer: Average, employee: Employee): Average = {
buffer.sum += employee.salary
buffer.count += 1
buffer
}
// 聚合不同execute的结果,分区间的合并
def merge(b1: Average, b2: Average): Average = {
b1.sum += b2.sum
b1.count += b2.count
b1
}
// 计算输出
def finish(reduction: Average): Double = reduction.sum.toDouble / reduction.count
// 设定之间值类型的编码器,要转换成case类 固定写法
// Encoders.product是进行scala元组和case类转换的编码器
def bufferEncoder: Encoder[Average] = Encoders.product
// 设定最终输出值的编码器 固定写法
def outputEncoder: Encoder[Double] = Encoders.scalaDouble
}
import spark.implicits._
val ds = spark.read.json("/root/employees.json").as[Employee]
ds.show()
// +-------+------+
// | name|salary|
// +-------+------+
// |Michael| 3000|
// | Andy| 4500|
// | Justin| 3500|
// | Berta| 4000|
// +-------+------+
// Convert the function to a `TypedColumn` and give it a name当作一列
val averageSalary = MyAverage.toColumn.name("average_salary")
val result = ds.select(averageSalary)
result.show()
// +--------------+
// |average_salary|
// +--------------+
// | 3750.0|
// +--------------+