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我正在从 Kafka 读取数据并尝试以 ORC 格式将其写入 HDFS 文件系统。我使用了他们官方网站上的以下链接参考。但我可以看到 Flink 为所有数据写入完全相同的内容并制作了这么多文件并且所有文件都可以 103KB

https://ci.apache.org/projects/flink/flink-docs-release-1.11/dev/connectors/streamfile_sink.html#orc-format

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))
  }

}
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1 回答 1

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对于批量格式（例如 ORC），StreamingFileSink每个检查点都会滚动到新文件。如果减少检查点间隔（当前为 5 秒），它就不会写入这么多文件。

于 2020-07-10T09:27:54.530 回答