带有过滤器的Spark SQL DataFrame连接不起作用

Question

I'm trying to filter df1 by joining df2 based on some column and then filter some rows from df1 based on filter.

df1:

+---------------+----------+
|        channel|rag_status|
+---------------+----------+
|            STS|     green|
|Rapid Cash Plus|     green|
|        DOTOPAL|     green|
|     RAPID CASH|     green|

df2:

+---------------+----------+
|        channel|rag_status|
+---------------+----------+
|            STS|      blue|
|Rapid Cash Plus|      blue|
|        DOTOPAL|      blue|
+---------------+----------+

Sample code is:

df1.join(df2, df1.col("channel") === df2.col("channel"), "leftouter")
      .filter(not(df1.col("rag_status") === "green"))
      .select(df1.col("channel"), df1.col("rag_status")).show

Its not returning any records.

I'm expecting the output as below one, which is returned from df1 after filtering the records based on channel and green status condition. If the same channel is available in the df2 and the df1 rag_status is green, then remove that record from df1 and return the remaining records only from df1.

Expected output is:

+---------------+----------+
|        channel|rag_status|
+---------------+----------+
|     RAPID CASH|     green|

Answer 1

You can work something like this :

val df1=sc.parallelize(Seq(("STS","green"),("Rapid Cash Plus","green"),("RAPID CASH","green"))).toDF("channel","rag_status").where($"rag_status"==="green")
val df2=sc.parallelize(Seq(("STS","blue"),("Rapid Cash Plus","blue"),("DOTOPAL","blue"))).toDF("channel","rag_status").withColumnRenamed("rag_status","rag_status2")
val leftJoinResult=df1.join(df2,Array("channel"),"left")
val innerJoinResult=df1.join(df2,"channel")
val resultDF=leftJoinResult.except(innerJoinResult).drop("rag_status2")
resultDF.show

Spark-shell Output:

scala> val df1=sc.parallelize(Seq(("STS","green"),("Rapid Cash Plus","green"),("RAPID CASH","green"))).toDF("channel","rag_status").where($"rag_status"==="green")
df1: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [channel: string, rag_status: string]

scala> val df2=sc.parallelize(Seq(("STS","blue"),("Rapid Cash Plus","blue"),("DOTOPAL","blue"))).toDF("channel","rag_status").withColumnRenamed("rag_status","rag_status2")
df2: org.apache.spark.sql.DataFrame = [channel: string, rag_status2: string]

scala> val leftJoinResult=df1.join(df2,Array("channel"),"left")
leftJoinResult: org.apache.spark.sql.DataFrame = [channel: string, rag_status: string ... 1 more field]

scala> val innerJoinResult=df1.join(df2,"channel")
innerJoinResult: org.apache.spark.sql.DataFrame = [channel: string, rag_status: string ... 1 more field]

scala> val resultDF=leftJoinResult.except(innerJoinResult).drop("rag_status2")
resultDF: org.apache.spark.sql.DataFrame = [channel: string, rag_status: string]

scala> resultDF.show
+----------+----------+                                                         
|   channel|rag_status|
+----------+----------+
|RAPID CASH|     green|
+----------+----------+

Answer 2

您可以使用以下代码获得预期的输出：

df1.join(df2, Seq("channel"), "leftouter").filter(row => row(3) != "blue")