Spark DeltaLake Upsert合并抛出“org.apache.spark.sql.AnalysisException”

问题描述

在下面，我正在尝试将数据帧合并到增量表的代码。 在这里，我将新数据框与增量表连接起来，然后转换连接的数据以匹配增量表架构，然后将其合并到增量表中。

但我收到AnalysisException。

Exception in thread "main" org.apache.spark.sql.AnalysisException: Resolved attribute(s) id#514 missing from _file_name_#872,age#516,id#879,name#636,age#881,name#880,city#882,id#631,_row_id_#866L,city#641 in operator !Join Inner,(id#514 = id#631). Attribute(s) with the same name appear in the operation: id. Please check if the right attribute(s) are used.;;
!Join Inner,(id#514 = id#631)
:- SubqueryAlias deltaData
:  +- Project [id#631,city#641]
:     +- Project [age#516,new_city#510 AS city#641]
:        +- Project [age#516,new_name#509 AS name#636,new_city#510]
:           +- Project [age#516,new_id#508 AS id#631,new_name#509,new_city#510]
:              +- Project [age#516,new_id#508,new_city#510]
:                 +- Join Inner,(id#514 = new_id#508)
:                    :- Relation[id#514,name#515,city#517] parquet
:                    +- LocalRelation [new_id#508,new_city#510]
+- Project [id#879,input_file_name() AS _file_name_#872]
   +- Project [id#879,monotonically_increasing_id() AS _row_id_#866L]
      +- Project [id#854 AS id#879,name#855 AS name#880,age#856 AS age#881,city#857 AS city#882]
         +- Relation[id#854,name#855,age#856,city#857] parquet

我的设置是Spark 3.0.0、Delta Lake 0.7.0、Hadoop 2.7.4

但是下面的代码在 Databricks 7.4 运行时运行良好，并且新数据框正在与增量表合并

代码片段：

import io.delta.tables.DeltaTable
import org.apache.spark.sql.functions.col
import org.apache.spark.sql.{SaveMode,SparkSession}

object CodePen extends App {
  val spark = SparkSession.builder().master("local[*]").getorCreate()

  val deltaPath = "<delta-path>"
  val oldEmployee = Seq(
    Employee(10,"Django",22,"Bangalore"),Employee(11,"Stephen",30,Employee(12,"Calvin",25,"Hyderabad"))

  val newEmployee = Seq(EmployeeNew(10,"Bangkok"))
  spark.createDataFrame(oldEmployee).write.format("delta").mode(SaveMode.Overwrite).save(deltaPath) // Saving the data to a delta table
  val newDf = spark.createDataFrame(newEmployee)

  val deltaTable = DeltaTable.forPath(deltaPath)
  val joinedDf = deltaTable.toDF.join(newDf,col("id") === col("new_id"),"inner")

  joinedDf.show()
  val cols = newDf.columns
  // Transforming the joined Dataframe to match the schema of the delta table
  var intDf = joinedDf.drop(cols.map(removePrefix): _*)
  for (column <- newDf.columns)
    intDf = intDf.withColumnRenamed(column,removePrefix(column))

  intDf = intDf.select(deltaTable.toDF.columns.map(col): _*)

  deltaTable.toDF.show()
  intDf.show()

  deltaTable.as("oldData")
    .merge(
      intDf.as("deltaData"),col("oldData.id") === col("deltaData.id"))
    .whenMatched()
    .updateall()
    .execute()

  deltaTable.toDF.show()

  def removePrefix(column: String) = {
    column.replace("new_","")
  }
}

case class Employee(id: Int,name: String,age: Int,city: String)

case class EmployeeNew(new_id: Int,new_name: String,new_city: String)

以下是数据帧的输出。

新数据框：

+---+------+-------+
| id|  name|   city|
+---+------+-------+
| 10|Django|Bangkok|
+---+------+-------+

加入 Datafame：

+---+------+---+---------+------+--------+--------+
| id|  name|age|     city|new_id|new_name|new_city|
+---+------+---+---------+------+--------+--------+
| 10|Django| 22|Bangalore|    10|  Django| Bangkok|
+---+------+---+---------+------+--------+--------+

增量表数据：

+---+-------+---+---------+
| id|   name|age|     city|
+---+-------+---+---------+
| 11|Stephen| 30|Bangalore|
| 12| Calvin| 25|Hyderabad|
| 10| Django| 22|Bangalore|
+---+-------+---+---------+

转换后的新数据框：

+---+------+---+-------+
| id|  name|age|   city|
+---+------+---+-------+
| 10|Django| 22|Bangkok|
+---+------+---+-------+

解决方法

您收到此 AnalysisException 是因为 deltaTable 和 intDf 的架构略有不同：

deltaTable.toDF.printSchema()
root
 |-- id: integer (nullable = true)
 |-- name: string (nullable = true)
 |-- age: integer (nullable = true)
 |-- city: string (nullable = true)



intDf.printSchema()
root
 |-- id: integer (nullable = false)
 |-- name: string (nullable = true)
 |-- age: integer (nullable = true)
 |-- city: string (nullable = true)

由于这个事实，intDf 表是由使用“id”列作为键的连接产生的，它会强制您的连接条件列不可为空。

如果您按照说明更改 nullale 属性 here，您将获得所需的输出：

+---+-------+---+---------+
| id|   name|age|     city|
+---+-------+---+---------+
| 11|Stephen| 30|Bangalore|
| 12| Calvin| 25|Hyderabad|
| 10| Django| 22|  Bangkok|
+---+-------+---+---------+

使用 Spark 3.0.1 和 Delta 0.7.0 进行测试。