自定义聚合函数(统计每一个商品的四种行为出现次数)

要求：统计每一个商品的四种行为出现次数

案例

package SparkSQL.fun.project
 
import org.apache.spark.SparkConf
import org.apache.spark.sql.expressions.{MutableAggregationBuffer, UserDefinedAggregateFunction}
import org.apache.spark.sql.types.{DataType, DataTypes, StructField, StructType}
import org.apache.spark.sql.{DataFrame, Dataset, Row, SparkSession}
 
/**
 * 统计每一个商品的四种行为出现次数，
 *  效果：每种商品如果某个行为不存在，那么用0来表示，最后返回如下结果
 *
 *  自定义聚合函数完成--累加类型的聚合函数
 *    1、输入的参数是behavior
 *    2、输出的是一个Map
 *    goodsId,total_times
 *    |72     |[pv -> 2, buy -> 0, cart -> 0, fav -> 0] |
 *    |81     |[pv -> 13, buy -> 1, cart -> 1, fav -> 0]|
 */
object BehaviorCode1 {
  def main(args: Array[String]): Unit = {
    val sparkConf = new SparkConf().setAppName("project01").setMaster("local[*]")
    val session = SparkSession.builder().config(sparkConf).getOrCreate()
    val map = Map("mode" -> "dropMalformed", "inferSchema" -> "true")
 
    val frame = session.read.options(map).csv("G:\\shixunworkspace\\sparkcode\\src\\main\\java\\SparkSQL\\fun\\project\\b.csv")
    val frame1 = frame.toDF("userId", "goodsId", "categoryId", "behavior", "time")
    frame1.show()
 
    import session.implicits._
    // 将DataFrame转换成Dataset，一般Dataset中类型是Bean类型
    val dataset: Dataset[UserBehaviorBean] = frame1.map((row) => {
      UserBehaviorBean(row.getAs[Int](0),
        row.getInt(1),
        row.getInt(2),
        row.getString(3),
        row.getInt(4)
      )
    })
    dataset.createTempView("temp")
 
    session.udf.register("time", new BehaviorTimesFun)
    // 当前sql语句的问题：如果某个商品没有某个行为的话，不会记录
    val frame2 = session.sql("select goodsId, time(behavior) count from temp group by goodsId")
    frame2.show(100, false)
 
    session.stop()
  }
}
 
class BehaviorTimesFun extends UserDefinedAggregateFunction {
  override def inputSchema: StructType = {
    StructType(Array(
      StructField("input", DataTypes.StringType)
    ))
  }
 
  override def bufferSchema: StructType = {
    StructType(Array(
      StructField("sum",
        DataTypes.createMapType(DataTypes.StringType, DataTypes.LongType))
    ))
  }
 
  override def dataType: DataType = {
    DataTypes.createMapType(DataTypes.StringType, DataTypes.LongType)
  }
 
  override def deterministic: Boolean = true
 
  override def initialize(buffer: MutableAggregationBuffer): Unit = {
    buffer(0) = Map("pv"->0L, "buy"->0L, "cart"->0L, "fav"->0L)
  }
 
  override def update(buffer: MutableAggregationBuffer, input: Row): Unit = {
    val str: String = input.getString(0) // str为行为，如pv, fav, 等
    val map = buffer.getMap[String, Long](0)
    val map1 = map.updated(str, map.getOrElse(str, 0L) + 1L)
    buffer(0) = map1
  }
 
  override def merge(buffer1: MutableAggregationBuffer, buffer2: Row): Unit = {
    val map = buffer2.getMap[String, Long](0)
    for (elem <- map) {
      val map1 = buffer1.getMap[String, Long](0)
      val map2 = map1.updated(elem._1, map1.getOrElse(elem._1, 0L) + elem._2)
      buffer1(0) = map2
    }
  }
 
  override def evaluate(buffer: Row): Any = {
    buffer.getMap[String, Long](0)
  }
}