1. 开发环境说明

IntelliJ IDEA 2019.1.1 (Ultimate Edition)
JRE: 1.8.0_202-release-1483-b44 x86_64
JVM: OpenJDK 64-Bit Server VM by JetBrains s.r.o
macOS 10.14.4
ProjectStruct->Libraries: spark-2.3.3-bin-hadoop2.7
Global Libraries: scala-2.11.11

2. 开发测试需求说明

对数据中重复的姓名进行统计，统计后的结果显示为新的数据字段

3. 代码实现如下

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.sql.{Row, SparkSession}
import org.apache.spark.sql.expressions.{MutableAggregationBuffer, UserDefinedAggregateFunction}
import org.apache.spark.sql.types.{DataType, IntegerType, StringType, StructField, StructType}

class StringCount extends  UserDefinedAggregateFunction{

  //输入数据的数据类型
  override def inputSchema: StructType = StructType(Array(StructField("string",StringType,true)))

  //中间进行聚合操作的数据类型-缓存区数据结构
  override def bufferSchema: StructType = StructType(Array(StructField("Count",IntegerType,true)))

  //函数返回值的类型 
  //特别注册如果此给他的是StructType将会报出一个错误，详见: 5. 错误说明
  override def dataType: DataType = IntegerType

  //给定的一组输入，UDAF是否总是生成相同的结果, 确保一致性， 一般是true
  override def deterministic: Boolean = true

  //每个分组的数据进行初始化
  override def initialize(buffer: MutableAggregationBuffer): Unit = buffer(0) = 0

  //有新的数据进入，进行的聚集合值计算
  override def update(buffer: MutableAggregationBuffer, input: Row): Unit = buffer(0)=buffer.getAs[Int](0)+1

  //spark多个节点计算后，需要进行合并
  override def merge(buffer1: MutableAggregationBuffer, buffer2: Row): Unit = buffer1(0)=buffer1.getAs[Int](0)+buffer2.getAs[Int](0)

  //最终的聚合值
  override def evaluate(buffer: Row): Any = buffer.getAs[Int](0)
}

object StringCount {

  def main(args: Array[String]): Unit = {
    val conf = new SparkConf().setAppName("UDAF_StringCount").setMaster("local")
    val ss   = new SparkContext(conf)
    val spark= SparkSession.builder().config(conf).getOrCreate()

    //模拟测试数据
    val testData = Array("leo","jack","xuan","lee","xuan","john","leo","leo","lee")
    val testDataRowRDD = ss.parallelize(testData,2).map(Row(_))
    val testSchema = StructType(Array(StructField("name",StringType,true)))
    val testDF = spark.createDataFrame(testDataRowRDD,testSchema)
    testDF.createOrReplaceTempView("testTableView")

    //将StringCount类，注册为自定义函数
    spark.udf.register("stringCount",new StringCount)

    val resDF = spark.sql("select name,stringCount(name) as name_Count from testTableView group by name")
    resDF.show()

  }
}

4.执行结果如下

+----+----------+
|name|name_Count|
+----+----------+
|jack|         1|
|john|         1|
| leo|         3|
| lee|         2|
|xuan|         2|
+----+----------+

5. 错误说明

  override def dataType: DataType = IntegerType

使用下面的方式来替换后 ，1.5和之前版本是可以正常运行的，

  override def dataType: DataType = StructType(Array(StructField("Result",IntegerType,true)))

当前版本(2.x)的开发环境，如果替换后， 在运行进会出现如下的错误:

scala.MatchError: 1 (of class java.lang.Integer)

Driver stacktrace:
2019-05-20 15:08:02 INFO  DAGScheduler:54 - Job 3 failed: show at UDAF_StringCount.scala:53, took 2.079227 s
Exception in thread "main" org.apache.spark.SparkException: Job aborted due to stage failure: Task 42 in stage 7.0 failed 1 times, most recent failure: Lost task 42.0 in stage 7.0 (TID 125, localhost, executor driver): scala.MatchError: 1 (of class java.lang.Integer)
	at org.apache.spark.sql.catalyst.CatalystTypeConverters$StructConverter.toCatalystImpl(CatalystTypeConverters.scala:236)
	at org.apache.spark.sql.catalyst.CatalystTypeConverters$StructConverter.toCatalystImpl(CatalystTypeConverters.scala:231)
	at org.apache.spark.sql.catalyst.CatalystTypeConverters$CatalystTypeConverter.toCatalyst(CatalystTypeConverters.scala:103)
	at org.apache.spark.sql.catalyst.CatalystTypeConverters$$anonfun$createToCatalystConverter$2.apply(CatalystTypeConverters.scala:379)
	at org.apache.spark.sql.execution.aggregate.ScalaUDAF.eval(udaf.scala:444)

6. 分享手绘一幅

it’s created by my daughter Grace.