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User-defined aggregate functions (UDAFs) | Databricks Documentation

Applies to: Databricks Runtime

User-defined aggregate functions (UDAFs) are user-programmable routines that act on multiple rows at once and return a single aggregated value as a result. This documentation lists the classes that are required for creating and registering UDAFs. It also contains examples that demonstrate how to define and register UDAFs in Scala and invoke them in Spark SQL.

Syntax Aggregator[-IN, BUF, OUT]

A base class for user-defined aggregations, which can be used in Dataset operations to take all of the elements of a group and reduce them to a single value.

• IN: The input type for the aggregation.

• BUF: The type of the intermediate value of the reduction.

• OUT: The type of the final output result.

• bufferEncoder: Encoder[BUF]

  The Encoder for the intermediate value type.

• finish(reduction: BUF): OUT

  Transform the output of the reduction.

• merge(b1: BUF, b2: BUF): BUF

  Merge two intermediate values.

• outputEncoder: Encoder[OUT]

  The Encoder for the final output value type.

• reduce(b: BUF, a: IN): BUF

  Aggregate input value a into current intermediate value. For performance, the function may modify b and return it instead of constructing new object for b.

• zero: BUF

  The initial value of the intermediate result for this aggregation.

User-defined aggregations for strongly typed Datasets revolve around the Aggregator abstract class. For example, a type-safe user-defined average can look like:

• Scala
• Java

Scala

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] {
  
  def zero: Average = Average(0L, 0L)
  
  
  def reduce(buffer: Average, employee: Employee): Average = {
    buffer.sum += employee.salary
    buffer.count += 1
    buffer
  }
  
  def merge(b1: Average, b2: Average): Average = {
    b1.sum += b2.sum
    b1.count += b2.count
    b1
  }
  
  def finish(reduction: Average): Double = reduction.sum.toDouble / reduction.count
  
  val bufferEncoder: Encoder[Average] = Encoders.product
  
  val outputEncoder: Encoder[Double] = Encoders.scalaDouble
}

Java

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;

    
}

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

  
}

public static class MyAverage extends Aggregator<Employee, Average, Double> {
  
  public Average zero() {
    return new Average(0L, 0L);
  }
  
  
  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;
  }
  
  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;
  }
  
  public Double finish(Average reduction) {
    return ((double) reduction.getSum()) / reduction.getCount();
  }
  
  public Encoder<Average> bufferEncoder() {
    return Encoders.bean(Average.class);
  }
  
  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().format("json").load(path).as(employeeEncoder);
ds.show();









MyAverage myAverage = new MyAverage();

TypedColumn<Employee, Double> averageSalary = myAverage.toColumn().name("average_salary");
Dataset<Double> result = ds.select(averageSalary);
result.show();

Typed aggregations, as described above, may also be registered as untyped aggregating UDFs for use with DataFrames. For example, a user-defined average for untyped DataFrames can look like:

• Scala
• Java
• SQL

Scala

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] {
  
  def zero: Average = Average(0L, 0L)
  
  
  def reduce(buffer: Average, data: Long): Average = {
    buffer.sum += data
    buffer.count += 1
    buffer
  }
  
  def merge(b1: Average, b2: Average): Average = {
    b1.sum += b2.sum
    b1.count += b2.count
    b1
  }
  
  def finish(reduction: Average): Double = reduction.sum.toDouble / reduction.count
  
  val bufferEncoder: Encoder[Average] = Encoders.product
  
  val outputEncoder: Encoder[Double] = Encoders.scalaDouble
}


spark.udf.register("myAverage", functions.udaf(MyAverage))

val df = spark.read.format("json").load("examples/src/main/resources/employees.json")
df.createOrReplaceTempView("employees")
df.show()









val result = spark.sql("SELECT myAverage(salary) as average_salary FROM employees")
result.show()

Java

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;

    

}

public static class MyAverage extends Aggregator<Long, Average, Double> {
  
  public Average zero() {
    return new Average(0L, 0L);
  }
  
  
  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;
  }
  
  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;
  }
  
  public Double finish(Average reduction) {
    return ((double) reduction.getSum()) / reduction.getCount();
  }
  
  public Encoder<Average> bufferEncoder() {
    return Encoders.bean(Average.class);
  }
  
  public Encoder<Double> outputEncoder() {
    return Encoders.DOUBLE();
  }
}


spark.udf().register("myAverage", functions.udaf(new MyAverage(), Encoders.LONG()));

Dataset<Row> df = spark.read().format("json").load("examples/src/main/resources/employees.json");
df.createOrReplaceTempView("employees");
df.show();









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

SQL

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|
+

• Scalar user defined functions (UDFs)
• Integration with Hive UDFs, UDAFs, and UDTFs

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