我正在读取Kafka的数据，并试图将其以orc格式写入hdfs文件系统。我使用了他们官方网站的以下链接。但我可以看到，flink为所有数据编写了完全相同的内容，生成了这么多文件，所有文件都是103kb
https://ci.apache.org/projects/flink/flink-docs-release-1.11/dev/connectors/streamfile_sink.html#orc-格式
请在下面找到我的代码。

object BeaconBatchIngest extends StreamingBase {
  val env: StreamExecutionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment
  def getTopicConfig(configs: List[Config]): Map[String, String]  = (for (config: Config <- configs) yield (config.getString("sourceTopic"), config.getString("destinationTopic"))).toMap

  def setKafkaConfig():Unit ={
    val kafkaParams = new Properties()
    kafkaParams.setProperty("bootstrap.servers","")
    kafkaParams.setProperty("zookeeper.connect","")
    kafkaParams.setProperty("group.id", DEFAULT_KAFKA_GROUP_ID)
    kafkaParams.setProperty("auto.offset.reset", "latest")

    val kafka_consumer:FlinkKafkaConsumer[String] = new FlinkKafkaConsumer[String]("sourceTopics", new SimpleStringSchema(),kafkaParams)
    kafka_consumer.setStartFromLatest()
    val stream: DataStream[DataParse] = env.addSource(kafka_consumer).map(new temp)
    val schema: String = "struct<_col0:string,_col1:bigint,_col2:string,_col3:string,_col4:string>"
    val writerProperties = new Properties()

    writerProperties.setProperty("orc.compress", "ZLIB")
    val writerFactory = new OrcBulkWriterFactory(new PersonVectorizer(schema),writerProperties,new org.apache.hadoop.conf.Configuration);
    val sink: StreamingFileSink[DataParse] = StreamingFileSink
          .forBulkFormat(new Path("hdfs://warehousestore/hive/warehouse/metrics_test.db/upp_raw_prod/hour=1/"), writerFactory)
          .build()
    stream.addSink(sink)
  }

  def main(args: Array[String]): Unit = {
    setKafkaConfig()
    env.enableCheckpointing(5000)
    env.execute("Kafka_Flink_HIVE")
  }
}
class temp extends MapFunction[String,DataParse]{

  override def map(record: String): DataParse = {
    new DataParse(record)
  }
}

class DataParse(data : String){
  val parsedJason = parse(data)
  val timestamp = compact(render(parsedJason \ "timestamp")).replaceAll("\"", "").toLong
  val event = compact(render(parsedJason \ "event")).replaceAll("\"", "")
  val source_id = compact(render(parsedJason \ "source_id")).replaceAll("\"", "")
  val app = compact(render(parsedJason \ "app")).replaceAll("\"", "")
  val json = data
}
class PersonVectorizer(schema: String) extends Vectorizer[DataParse](schema) {

  override def vectorize(element: DataParse, batch: VectorizedRowBatch): Unit = {
    val eventColVector = batch.cols(0).asInstanceOf[BytesColumnVector]
    val timeColVector = batch.cols(1).asInstanceOf[LongColumnVector]
    val sourceIdColVector = batch.cols(2).asInstanceOf[BytesColumnVector]
    val appColVector = batch.cols(3).asInstanceOf[BytesColumnVector]
    val jsonColVector = batch.cols(4).asInstanceOf[BytesColumnVector]
    timeColVector.vector(batch.size + 1) = element.timestamp
    eventColVector.setVal(batch.size + 1, element.event.getBytes(StandardCharsets.UTF_8))
    sourceIdColVector.setVal(batch.size + 1, element.source_id.getBytes(StandardCharsets.UTF_8))
    appColVector.setVal(batch.size + 1, element.app.getBytes(StandardCharsets.UTF_8))
    jsonColVector.setVal(batch.size + 1, element.json.getBytes(StandardCharsets.UTF_8))
  }

}