从Spark 2.4中的spark-shell编写AVRO

Question

Java 1.8.0_161（Scala 2.11.12）上的Spark 2.4.0

运行命令：spark-shell --jars=spark-avro_2.11-2.4.0.jar

当前正在使用小型Avro文件处理某些POC，我希望能够读取（单个）AVRO文件，进行更改，然后将其写回。

阅读很好： val myAv = spark.read.format("avro").load("myAvFile.avro")

但是，尝试回写时（甚至在进行任何更改之前），我会收到此错误消息：

scala> myAv.write.format("avro").save("./output-av-file.avro")

org.apache.spark.sql.AnalysisException:
Datasource does not support writing empty or nested empty schemas.
Please make sure the data schema has at least one or more column(s).
         ;
  at org.apache.spark.sql.execution.datasources.DataSource$.org$apache$spark$sql$execution$datasources$DataSource$$validateSchema(DataSource.scala:733)
  at org.apache.spark.sql.execution.datasources.DataSource.planForWriting(DataSource.scala:523)
  at org.apache.spark.sql.DataFrameWriter.saveToV1Source(DataFrameWriter.scala:281)
  at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:270)
  at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:228)
  ... 49 elided

我尝试手动指定数据框的架构，但无济于事： .write.option("avroSchema", c_schema.toString).format("avro") ...

Answer 1

原因很明显，架构是空的。 see here from code

if (hasEmptySchema(schema)) {
      throw new AnalysisException(
        s"""
           |Datasource does not support writing empty or nested empty schemas.
           |Please make sure the data schema has at least one or more column(s).
         """.stripMargin)
    }