绑定属性，树： Scala 中的 dayofmonth(cast(timestamp#122 as date))

Question

我有一个数据框 df = [id: String, value: Int, type :String, timestamp: java.sql.Date] ，我需要结果：

我的数据框：

+----+-------++-------+------------------------+
| id | type  | value  | timestamp              |
+----+-------+--------+------------------------+
| 1 |  rent  |  12    |  2016-09-19T00:00:00Z
| 1 |   rent |  12    |  2016-09-19T00:00:00Z
| 1 | buy    |  12    |  2016-09-20T00:00:00Z
| 1 |  rent  |  12    |  2016-09-20T00:00:00Z
| 1 |   buy  |  12    |  2016-09-18T00:00:00Z
| 1 | buy    |  12    |  2016-09-18T00:00:00Z
+----+-------+-------+------------------------+ 

我需要结果为

id : 1
totalValue  : 72
typeForDay : {"rent: 2, "buy" : 2 }  --- group By based on id and dayofmonth(col("timestamp"))  atmost 1 type per day 

我试过了：

val ddf = df.
.groupBy("id", )
.agg(collect_set("type"),
sum("value") as "totalValue") 

val count_by_value = udf {( gti :scala.collection.mutable.WrappedArray[String]) => if (gti == null) null else  gti.groupBy(identity).mapValues(_.size)}


val result =  ddf.withColumn("totalValue", count_by_value($"collect_list(type)"))
.drop("collect_list(type)")

这给了我错误：

org.apache.spark.SparkException: Job aborted due to stage failure: Task 115 in stage 15.0 failed 4 times, most recent failure: Lost task 115.3 in stage 15.0 (TID 1357, ip-172-31-9-47.ec2.internal): org.apache.spark.sql.catalyst.errors.package$TreeNodeException: Binding attribute, tree: dayofmonth(cast(timestamp#122 as date))#137 
  at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:49) 
  at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1.applyOrElse(BoundAttribute.scala:86) 
  at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1.applyOrElse(BoundAttribute.scala:85) 
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:243) 
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:243) 
  at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:53) 
  at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:242) 
  at org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:233) 
  at org.apache.spark.sql.catalyst.expressions.BindReferences$.bindReference(BoundAttribute.scala:85)
  at org.apache.spark.sql.catalyst.expressions.InterpretedMutableProjection$$anonfun$$init$$2.apply(Projection.scala:62)
  at org.apache.spark.sql.catalyst.expressions.InterpretedMutableProjection$$anonfun$$init$$2.apply(Projection.scala:62)
  at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
  at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
  at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
  at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
  at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
  at scala.collection.AbstractTraversable.map(Traversable.scala:105)
  at org.apache.spark.sql.catalyst.expressions.InterpretedMutableProjection.<init>(Projection.scala:62)
  at org.apache.spark.sql.execution.SparkPlan$$anonfun$newMutableProjection$1.apply(SparkPlan.scala:234)
  at org.apache.spark.sql.execution.SparkPlan$$anonfun$newMutableProjection$1.apply(SparkPlan.scala:234)
  at org.apache.spark.sql.execution.Exchange.org$apache$spark$sql$execution$Exchange$$getPartitionKeyExtractor$1(Exchange.scala:197)
  at org.apache.spark.sql.execution.Exchange$$anonfun$3.apply(Exchange.scala:209)
  at org.apache.spark.sql.execution.Exchange$$anonfun$3.apply(Exchange.scala:208)
  at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$21.apply(RDD.scala:728)
  at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$21.apply(RDD.scala:728)
  at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
  at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
  at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
  at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73)
  at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
  at org.apache.spark.scheduler.Task.run(Task.scala:89)
  at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213)
  at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
  at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
  at java.lang.Thread.run(Thread.java:745) Caused by: java.lang.RuntimeException: Couldn't find dayofmonth(cast(timestamp#122 as date))#137 in [customerId#81,timestamp#122,benefit#111]
  at scala.sys.package$.error(package.scala:27)
  at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1$$anonfun$applyOrElse$1.apply(BoundAttribute.scala:92)
  at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1$$anonfun$applyOrElse$1.apply(BoundAttribute.scala:86)
  at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:48) ... 34 more 

Answer 1

运行您的代码（经过一些修复以使其编译...）不会产生您在我的环境中描述的异常（使用 Spark 1.6.2），但它也不会产生所需的结果 - 您无法尝试计算 ddf 上每种类型的天数，因为 ddf 已仅按 id 分组，并且时间戳数据丢失。

这是一个替代实现，使用 UDAF（用户定义的聚合函数）将 MapType 列的值“合并”到单个 Map 中：

val toMap = udf { (typ: String, count: Int) => Map(typ -> count) }

val result = df
  // First: group by id AND type, count distinct days and sum value:
  .groupBy("id", "type").agg(countDistinct(dayofmonth(col("timestamp"))) as "daysPerType", sum("value") as "valPerType")
  // Then: convert type and count into a single Map column
  .withColumn("typeForDay", toMap(col("type"), col("daysPerType")))
  // Lastly: use a custom aggregation function to "merge" the maps (assuming keys are unique to begin with!)
  .groupBy("id").agg(sum("valPerType") as "totalValue", CombineMaps(col("typeForDay")) as "typeForDay")

result.show() 
// prints:
// +---+----------+------------------------+
// | id|totalValue|              typeForDay|
// +---+----------+------------------------+
// |  1|        72|Map(buy -> 2, rent -> 2)|
// +---+----------+------------------------+

以及CombineMaps的实现：

object CombineMaps extends UserDefinedAggregateFunction {
  override def inputSchema: StructType = new StructType().add("map", dataType)
  override def bufferSchema: StructType = inputSchema
  override def dataType: DataType = MapType(StringType, IntegerType)
  override def deterministic: Boolean = true

  override def initialize(buffer: MutableAggregationBuffer): Unit = buffer.update(0 , Map[String, Int]())

  // naive implementation - assuming keys won't repeat, otherwise later value for key overrides earlier one
  override def update(buffer: MutableAggregationBuffer, input: Row): Unit = {
    val before = buffer.getAs[Map[String, Int]](0)
    val toAdd = input.getAs[Map[String, Int]](0)
    val result = before ++ toAdd
    buffer.update(0, result)
  }

  override def merge(buffer1: MutableAggregationBuffer, buffer2: Row): Unit = update(buffer1, buffer2)

  override def evaluate(buffer: Row): Any = buffer.getAs[Map[String, Int]](0)
}