scala – Spark：分组数据的“数量”是转换还是行动？

我知道 count调用RDD或DataFrame是一个动作.但是在摆弄火花壳时,我发现了以下情况

scala> val empDF = Seq((1,"James Gordon",30,"Homicide"),(2,"Harvey Bullock",35,(3,"Kristen Kringle",28,"Records"),(4,"Edward Nygma","Forensics"),(5,"Leslie Thompkins",31,"Forensics")).toDF("id","name","age","department")
empDF: org.apache.spark.sql.DataFrame = [id: int,name: string,age: int,department: string]

scala> empDF.show
+---+----------------+---+----------+
| id|            name|age|department|
+---+----------------+---+----------+
|  1|    James Gordon| 30|  Homicide|
|  2|  Harvey Bullock| 35|  Homicide|
|  3| Kristen Kringle| 28|   Records|
|  4|    Edward Nygma| 30| Forensics|
|  5|Leslie Thompkins| 31| Forensics|
+---+----------------+---+----------+

scala> empDF.groupBy("department").count //count returned a DataFrame
res1: org.apache.spark.sql.DataFrame = [department: string,count: bigint]

scala> res1.show
+----------+-----+                                                              
|department|count|
+----------+-----+
|  Homicide|    2|
|   Records|    1|
| Forensics|    2|
+----------+-----+

当我在GroupedData(empDF.groupBy(“department”))上调用count时,我得到另一个DataFrame作为结果(res1).这让我相信在这种情况下,计数是一个转变.当我调用count时没有触发任何计算这一事实进一步支持了它,相反,当我运行res1.show时它们就开始了.

我无法找到任何表明计数也可能是转换的文档.有人可以对此有所了解吗？

解决方法

您在代码中使用的.count()是通过RelationalGroupedDataset,它创建一个包含分组数据集中元素数量的新列.这是一种转变.参考：
https://spark.apache.org/docs/1.6.0/api/scala/index.html#org.apache.spark.sql.GroupedDataset

通常在RDD / DataFrame / Dataset上使用的.count()与上面完全不同,而.count()是一个Action.参见：https://spark.apache.org/docs/1.6.0/api/scala/index.html#org.apache.spark.rdd.RDD

编辑：

在distributedDataSet上运行时,总是使用.count()和.agg(),以避免将来混淆：

empDF.groupBy($"department").agg(count($"department") as "countDepartment").show