在Spark Dataset mapGroups操作之后，Value Type是二进制的，甚至在函数中返回一个Strin

Question

环境：

Spark version: 2.3.0
Run Mode: Local
Java version: Java 8

火花应用程序尝试执行以下

1( 将输入数据转换为数据集〔GenericRecord〕

2( 按GenericRecord 的关键属性分组

3( 使用mapGroups-after-group迭代值列表，并以字符串格式获得一些结果

4( 将结果输出为文本文件中的字符串。

写入文本文件时出错。Spark推断，步骤3中生成的数据集有一个二进制列，而不是字符串列。但实际上它在mapGroups函数中返回了一个String。

有没有办法进行列数据类型转换，或者让Spark知道它实际上是一个字符串列而不是二进制列？

val dslSourcePath = args(0)
val filePath = args(1)
val targetPath = args(2)
val df = spark.read.textFile(filePath)
implicit def kryoEncoder[A](implicit ct: ClassTag[A]): Encoder[A] = Encoders.kryo[A](ct)
val mapResult = df.flatMap(abc => {
JavaConversions.asScalaBuffer(some how return a list of Avro GenericRecord using a java library).seq;
})
val groupResult = mapResult.groupByKey(result => String.valueOf(result.get("key")))
.mapGroups((key, valueList) => {
val result = StringBuilder.newBuilder.append(key).append(",").append(valueList.count(_=>true))
result.toString()
})
groupResult.printSchema()
groupResult.write.text(targetPath + "-result-" + System.currentTimeMillis())

输出显示这是一个仓

root
|-- value: binary (nullable = true)

Spark发出错误，无法将二进制文件写入文本：

Exception in thread "main" org.apache.spark.sql.AnalysisException: Text data source supports only a string column, but you have binary.;
at org.apache.spark.sql.execution.datasources.text.TextFileFormat.verifySchema(TextFileFormat.scala:55)
at org.apache.spark.sql.execution.datasources.text.TextFileFormat.prepareWrite(TextFileFormat.scala:78)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:140)
at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand.run(InsertIntoHadoopFsRelationCommand.scala:154)
at org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult$lzycompute(commands.scala:104)
at org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult(commands.scala:102)
at org.apache.spark.sql.execution.command.DataWritingCommandExec.doExecute(commands.scala:122)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:131)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:127)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:127)
at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:80)
at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:80)
at org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:654)
at org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:654)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:77)
at org.apache.spark.sql.DataFrameWriter.runCommand(DataFrameWriter.scala:654)
at org.apache.spark.sql.DataFrameWriter.saveToV1Source(DataFrameWriter.scala:273)
at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:267)
at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:225)
at org.apache.spark.sql.DataFrameWriter.text(DataFrameWriter.scala:595)

Answer 1

正如@user10938362所说，原因是以下代码将所有数据编码为字节

implicit def kryoEncoder[A](implicit ct: ClassTag[A]): Encoder[A] = Encoders.kryo[A](ct)

用以下代码替换它只会为GenericRecord 启用此编码

implicit def kryoEncoder: Encoder[GenericRecord] = Encoders.kryo