Apache spark Spark：使用Dataframes从列创建映射类型的基于列的值

样本输入：

Item_Id Item_Name Buyer's_Id Buyers_Name  
0001    Keyboard  10000      ABC  
0002    Monitor   10010      XYZ  
0001    Keyboard  10005      DXC  

中间输出示例：

0001,Keyboard,{"Buyer's_Id":"10000","Buyers_Name":"ABC"}    
0002,Monitor,{"Buyer's_Id":"10010","Buyers_Name":"XYZ"}     
0001,Keyboard,{"Buyer's_Id":"10005","Buyers_Name":"DXC"}    

最终输出：

0001,Keyboard,[{"Buyer's_Id":"10000","Buyers_Name":"Abc"},{"Buyer's_Id":"10005","Buyers_Name":"DXC"}]  
0002,Monitor,[{"Buyer's_Id":"10010","Buyers_Name":"XYZ"}]     

---

您想要实现的目标可以使用

map

处理每行

mapPartitions

处理每个分区

scala> input_df.show
+-------+---------+----------+-----------+
|Item_Id|Item_Name|Buyer's_Id|Buyers_Name|
+-------+---------+----------+-----------+
|      1| Keyboard|     10000|        ABC|
|      2|  Monitor|     10010|        XYZ|
|      1| Keyboard|     10005|        DXC|
+-------+---------+----------+-----------+

import org.apache.spark.sql.Row
import org.apache.spark.sql.catalyst.encoders.RowEncoder
import org.apache.spark.sql.types._
import org.apache.spark.sql.functions.collect_set

因为中间数据帧具有不同的模式。所以我们需要定义新的模式

scala> val schema = StructType(Seq(
     |       StructField("item_number", IntegerType),
     |       StructField("item_name", StringType),
     |       StructField("json_string", StringType)
     |     ))
scala> val encoder = RowEncoder(schema)
scala> val intermediate_df = input_df.map{row =>
     |     val itm_nbr = row.getAs[Integer]("Item_Id")
     |     val itm_nme = row.getAs[String]("Item_Name")
     |     val byer_id = row.getAs[Integer]("Buyer's_Id")
     |     val byer_nme = row.getAs[String]("Buyers_Name")
     |     val req_string = s"""{"Buyer's_id" :  $byer_id,"Buyers_Name" : $byer_nme}"""
     |     Row(itm_nbr,itm_nme,req_string)
     | }(encoder)
intermediate_df: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [item_number: int, item_name: string ... 1 more field]

scala> intermediate_df.show(false)
+-----------+---------+-------------------------------------------+
|item_number|item_name|json_string                                |
+-----------+---------+-------------------------------------------+
|1          |Keyboard |{"Buyer's_id" :  10000,"Buyers_Name" : ABC}|
|2          |Monitor  |{"Buyer's_id" :  10010,"Buyers_Name" : XYZ}|
|1          |Keyboard |{"Buyer's_id" :  10005,"Buyers_Name" : DXC}|
+-----------+---------+-------------------------------------------+

scala> val result_df = intermediate_df.groupBy('item_number,'item_name).agg(collect_set('json_string).as("json_list")).orderBy('item_number)
result_df: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [item_number: int, item_name: string ... 1 more field]

scala> result_df.show(false)
+-----------+---------+------------------------------------------------------------------------------------------+
|item_number|item_name|json_list                                                                                 |
+-----------+---------+------------------------------------------------------------------------------------------+
|1          |Keyboard |[{"Buyer's_id" :  10000,"Buyers_Name" : ABC}, {"Buyer's_id" :  10005,"Buyers_Name" : DXC}]|
|2          |Monitor  |[{"Buyer's_id" :  10010,"Buyers_Name" : XYZ}]                                             |
+-----------+---------+------------------------------------------------------------------------------------------+

---

希望这是有帮助的

虽然这段代码可以回答这个问题，但提供关于如何以及为什么解决问题的信息可以提高其长期价值。

dF.select(
  $"Item_Id",
  $"Item_Name",
  map(
    lit("Buyer's_Id"),$"Buyer's_Id",
    lit("Buyers_Name"),$"Buyers_Name"
  ).as("newCol")
).groupBy("Item_Id","Item_Name")
 .agg(
   collect_set($"newCol").as("mapCol")
).orderBy("Item_Id")