如何在spark中连接多列，同时从另一个表获取要连接的列名（每行不同）

Question

table - **t**
+---+----+  
| id|name|
+---+----+  
|  1|   a|  
|  2|   b|
+---+----+

table - **r**
+---+-------+
| id|   att |
+---+-------+
|  1|id,name|
|  2|   id  |
+---+-------+

如果我将这两个表连接起来，并执行如下操作，我可以concat但不是基于表r（因为新列的第一行是1，但第二行应该只有2）

t.withColumn("new",concat_ws(",",t.select("att").first.mkString.split(",").map(c => col(c)): _*)).show
+---+----+-------+---+
| id|name|  att  |new|
+---+----+-------+---+
|  1|   a|id,name|1,a|
|  2|   b|  id   |2,b|
+---+----+-------+---+

我必须在上面的查询中的select之前应用filter，但我不确定如何在withColumn中为每一行执行此操作。

下面的东西，如果可能的话。

t.withColumn("new",concat_ws(",",t.**filter**("id="+this.id).select("att").first.mkString.split(",").map(c => col(c)): _*)).show

scala> t.filter("id=1").select("att").first.mkString.split(",").map(c => col(c))
res90: Array[org.apache.spark.sql.Column] = Array(id, name)

scala> t.filter("id=2").select("att").first.mkString.split(",").map(c => col(c))
res89: Array[org.apache.spark.sql.Column] = Array(id)

+---+----+-------+---+
| id|name|  att  |new|
+---+----+-------+---+
|  1|   a|id,name|1,a|
|  2|   b|  id   |2  |
+---+----+-------+---+

Answer 1

我们可以使用UDF

此逻辑工作的要求。

表t的列名应该与表r的col att中的列名的顺序相同

scala> input_df_1.show
+---+----+
| id|name|
+---+----+
|  1|   a|
|  2|   b|
+---+----+

scala> input_df_2.show
+---+-------+
| id|    att|
+---+-------+
|  1|id,name|
|  2|     id|
+---+-------+

scala> val join_df = input_df_1.join(input_df_2,Seq("id"),"inner")
join_df: org.apache.spark.sql.DataFrame = [id: int, name: string ... 1 more field]

scala> val req_cols = input_df_1.columns
req_cols: Array[String] = Array(id, name)

scala> def new_col_udf = udf((cols : Seq[String],row : String,attr : String) => {
     |     val row_values = row.split(",")
     |     val attrs = attr.split(",")
     |     val req_val = attrs.map{at =>
     |     val index = cols.indexOf(at)
     |     row_values(index)
     |     }
     |     req_val.mkString(",")
     |     })
new_col_udf: org.apache.spark.sql.expressions.UserDefinedFunction

scala>  val intermediate_df = join_df.withColumn("concat_column",concat_ws(",",'id,'name)).withColumn("new_col",new_col_udf(lit(req_cols),'concat_column,'att))
intermediate_df: org.apache.spark.sql.DataFrame = [id: int, name: string ... 3 more fields]

scala> val result_df = intermediate_df.select('id,'name,'att,'new_col)
result_df: org.apache.spark.sql.DataFrame = [id: int, name: string ... 2 more fields]

scala> result_df.show
+---+----+-------+-------+
| id|name|    att|new_col|
+---+----+-------+-------+
|  1|   a|id,name|    1,a|
|  2|   b|     id|      2|
+---+----+-------+-------+