用户定义的聚合函数 (UDAF)

描述

用户定义的聚合函数 (UDAF) 是用户可编程例程，它们一次作用于多行并返回单个聚合值作为结果。本文档列出了创建和注册 UDAF 所需的类。它还包含演示如何在 Scala 中定义和注册 UDAF 并在 Spark SQL 中调用它们的示例。

聚合器[-IN, BUF, OUT]

用户定义聚合的基类，可在数据集操作中使用，以获取组的所有元素并将它们简化为单个值。

IN - 聚合的输入类型。

BUF - 缩减的中间值的类型。

OUT - 最终输出结果的类型。

bufferEncoder：编码器[BUF]

指定中间值类型的编码器。
完成（缩减：BUF）：OUT

转换缩减的输出。
合并（b1：BUF，b2：BUF）：BUF

合并两个中间值。
outputEncoder：编码器[OUT]

指定最终输出值类型的编码器。
缩减（b：BUF，a：IN）：BUF

将输入值 a 聚合到当前中间值中。为了提高性能，该函数可以修改 b 并返回它，而不是为 b 构造新对象。
零：BUF

此聚合的中间结果的初始值。

示例

类型安全的自定义聚合函数

用于强类型数据集的用户定义聚合围绕聚合器抽象类。例如，类型安全的自定义平均值可能如下所示

import org.apache.spark.sql.{Encoder, Encoders, SparkSession}
import org.apache.spark.sql.expressions.Aggregator

case class Employee(name: String, salary: Long)
case class Average(var sum: Long, var count: Long)

object MyAverage extends Aggregator[Employee, Average, Double] {
  // A zero value for this aggregation. Should satisfy the property that any b + zero = b
  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
  }
  // Merge two intermediate values
  def merge(b1: Average, b2: Average): Average = {
    b1.sum += b2.sum
    b1.count += b2.count
    b1
  }
  // Transform the output of the reduction
  def finish(reduction: Average): Double = reduction.sum.toDouble / reduction.count
  // Specifies the Encoder for the intermediate value type
  def bufferEncoder: Encoder[Average] = Encoders.product
  // Specifies the Encoder for the final output value type
  def outputEncoder: Encoder[Double] = Encoders.scalaDouble
}

val ds = spark.read.json("examples/src/main/resources/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
val averageSalary = MyAverage.toColumn.name("average_salary")
val result = ds.select(averageSalary)
result.show()
// +--------------+
// |average_salary|
// +--------------+
// |        3750.0|
// +--------------+

在 Spark 存储库的“examples/src/main/scala/org/apache/spark/examples/sql/UserDefinedTypedAggregation.scala”中查找完整的示例代码。

import java.io.Serializable;

import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Encoder;
import org.apache.spark.sql.Encoders;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.TypedColumn;
import org.apache.spark.sql.expressions.Aggregator;

public static class Employee implements Serializable {
  private String name;
  private long salary;

  // Constructors, getters, setters...

}

public static class Average implements Serializable  {
  private long sum;
  private long count;

  // Constructors, getters, setters...

}

public static class MyAverage extends Aggregator<Employee, Average, Double> {
  // A zero value for this aggregation. Should satisfy the property that any b + zero = b
  @Override
  public Average zero() {
    return new 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
  @Override
  public Average reduce(Average buffer, Employee employee) {
    long newSum = buffer.getSum() + employee.getSalary();
    long newCount = buffer.getCount() + 1;
    buffer.setSum(newSum);
    buffer.setCount(newCount);
    return buffer;
  }
  // Merge two intermediate values
  @Override
  public Average merge(Average b1, Average b2) {
    long mergedSum = b1.getSum() + b2.getSum();
    long mergedCount = b1.getCount() + b2.getCount();
    b1.setSum(mergedSum);
    b1.setCount(mergedCount);
    return b1;
  }
  // Transform the output of the reduction
  @Override
  public Double finish(Average reduction) {
    return ((double) reduction.getSum()) / reduction.getCount();
  }
  // Specifies the Encoder for the intermediate value type
  @Override
  public Encoder<Average> bufferEncoder() {
    return Encoders.bean(Average.class);
  }
  // Specifies the Encoder for the final output value type
  @Override
  public Encoder<Double> outputEncoder() {
    return Encoders.DOUBLE();
  }
}

Encoder<Employee> employeeEncoder = Encoders.bean(Employee.class);
String path = "examples/src/main/resources/employees.json";
Dataset<Employee> ds = spark.read().json(path).as(employeeEncoder);
ds.show();
// +-------+------+
// |   name|salary|
// +-------+------+
// |Michael|  3000|
// |   Andy|  4500|
// | Justin|  3500|
// |  Berta|  4000|
// +-------+------+

MyAverage myAverage = new MyAverage();
// Convert the function to a `TypedColumn` and give it a name
TypedColumn<Employee, Double> averageSalary = myAverage.toColumn().name("average_salary");
Dataset<Double> result = ds.select(averageSalary);
result.show();
// +--------------+
// |average_salary|
// +--------------+
// |        3750.0|
// +--------------+

在 Spark 存储库的“examples/src/main/java/org/apache/spark/examples/sql/JavaUserDefinedTypedAggregation.java”中查找完整的示例代码。

无类型用户定义的聚合函数

如上所述，类型化聚合也可以注册为无类型聚合 UDF，以便与数据帧一起使用。例如，用于无类型数据帧的用户定义平均值可能如下所示

import org.apache.spark.sql.{Encoder, Encoders, SparkSession}
import org.apache.spark.sql.expressions.Aggregator
import org.apache.spark.sql.functions

case class Average(var sum: Long, var count: Long)

object MyAverage extends Aggregator[Long, Average, Double] {
  // A zero value for this aggregation. Should satisfy the property that any b + zero = b
  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, data: Long): Average = {
    buffer.sum += data
    buffer.count += 1
    buffer
  }
  // Merge two intermediate values
  def merge(b1: Average, b2: Average): Average = {
    b1.sum += b2.sum
    b1.count += b2.count
    b1
  }
  // Transform the output of the reduction
  def finish(reduction: Average): Double = reduction.sum.toDouble / reduction.count
  // Specifies the Encoder for the intermediate value type
  def bufferEncoder: Encoder[Average] = Encoders.product
  // Specifies the Encoder for the final output value type
  def outputEncoder: Encoder[Double] = Encoders.scalaDouble
}

// Register the function to access it
spark.udf.register("myAverage", functions.udaf(MyAverage))

val df = spark.read.json("examples/src/main/resources/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|
// +--------------+

在 Spark 存储库的“examples/src/main/scala/org/apache/spark/examples/sql/UserDefinedUntypedAggregation.scala”中查找完整的示例代码。

import java.io.Serializable;

import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Encoder;
import org.apache.spark.sql.Encoders;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.expressions.Aggregator;
import org.apache.spark.sql.functions;

public static class Average implements Serializable  {
  private long sum;
  private long count;

  // Constructors, getters, setters...
  public Average() {
  }

  public Average(long sum, long count) {
    this.sum = sum;
    this.count = count;
  }

  public long getSum() {
    return sum;
  }

  public void setSum(long sum) {
    this.sum = sum;
  }

  public long getCount() {
    return count;
  }

  public void setCount(long count) {
    this.count = count;
  }
}

public static class MyAverage extends Aggregator<Long, Average, Double> {
  // A zero value for this aggregation. Should satisfy the property that any b + zero = b
  @Override
  public Average zero() {
    return new 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
  @Override
  public Average reduce(Average buffer, Long data) {
    long newSum = buffer.getSum() + data;
    long newCount = buffer.getCount() + 1;
    buffer.setSum(newSum);
    buffer.setCount(newCount);
    return buffer;
  }
  // Merge two intermediate values
  @Override
  public Average merge(Average b1, Average b2) {
    long mergedSum = b1.getSum() + b2.getSum();
    long mergedCount = b1.getCount() + b2.getCount();
    b1.setSum(mergedSum);
    b1.setCount(mergedCount);
    return b1;
  }
  // Transform the output of the reduction
  @Override
  public Double finish(Average reduction) {
    return ((double) reduction.getSum()) / reduction.getCount();
  }
  // Specifies the Encoder for the intermediate value type
  @Override
  public Encoder<Average> bufferEncoder() {
    return Encoders.bean(Average.class);
  }
  // Specifies the Encoder for the final output value type
  @Override
  public Encoder<Double> outputEncoder() {
    return Encoders.DOUBLE();
  }
}

// Register the function to access it
spark.udf().register("myAverage", functions.udaf(new MyAverage(), Encoders.LONG()));

Dataset<Row> df = spark.read().json("examples/src/main/resources/employees.json");
df.createOrReplaceTempView("employees");
df.show();
// +-------+------+
// |   name|salary|
// +-------+------+
// |Michael|  3000|
// |   Andy|  4500|
// | Justin|  3500|
// |  Berta|  4000|
// +-------+------+

Dataset<Row> result = spark.sql("SELECT myAverage(salary) as average_salary FROM employees");
result.show();
// +--------------+
// |average_salary|
// +--------------+
// |        3750.0|
// +--------------+

在 Spark 存储库的“examples/src/main/java/org/apache/spark/examples/sql/JavaUserDefinedUntypedAggregation.java”中查找完整的示例代码。

-- Compile and place UDAF MyAverage in a JAR file called `MyAverage.jar` in /tmp.
CREATE FUNCTION myAverage AS 'MyAverage' USING JAR '/tmp/MyAverage.jar';

SHOW USER FUNCTIONS;
+------------------+
|          function|
+------------------+
| default.myAverage|
+------------------+

CREATE TEMPORARY VIEW employees
USING org.apache.spark.sql.json
OPTIONS (
    path "examples/src/main/resources/employees.json"
);

SELECT * FROM employees;
+-------+------+
|   name|salary|
+-------+------+
|Michael|  3000|
|   Andy|  4500|
| Justin|  3500|
|  Berta|  4000|
+-------+------+

SELECT myAverage(salary) as average_salary FROM employees;
+--------------+
|average_salary|
+--------------+
|        3750.0|
+--------------+

Spark SQL 指南

用户定义的聚合函数 (UDAF)

描述

聚合器[-IN, BUF, OUT]

示例

类型安全的自定义聚合函数

无类型用户定义的聚合函数

相关语句