Spark Structured Streaming:将数据落地按照数据字段进行分区方案

方案一(使用ForeachWriter Sink方式):

val query = wordCounts.writeStream.trigger(ProcessingTime(5.seconds))
  .outputMode("complete")
  .foreach(new ForeachWriter[Row] {
      var fileWriter: FileWriter = _

      override def process(value: Row): Unit = {
        fileWriter.append(value.toSeq.mkString(","))
      }

      override def close(errorOrNull: Throwable): Unit = {
        fileWriter.close()
      }

      override def open(partitionId: Long, version: Long): Boolean = {
        FileUtils.forceMkdir(new File(s"/tmp/example/${partitionId}"))
        fileWriter = new FileWriter(new File(s"/tmp/example/${partitionId}/temp"))
        true
      }
    }).start()

方案二(ds.writeStream().partitionBy("field")):

import org.apache.spark.sql.streaming.ProcessingTime
 
val query =  
  streamingSelectDF
    .writeStream
    .format("parquet")
    .option("path", "/mnt/sample/test-data")
    .option("checkpointLocation", "/mnt/sample/check")
    .partitionBy("zip", "day")
    .trigger(ProcessingTime("25 seconds"))
    .start()

java代码:

        // Write new data to Parquet files
        // can be "orc", "json", "csv", etc.
        String hdfsFileFormat = SparkHelper.getInstance().getLTEBaseSaveHdfsFileFormat();
        String queryName = "save" + this.getTopicEncodeName(topicName) + "DataToHdfs";
        String saveHdfsPath = SparkHelper.getInstance().getLTEBaseSaveHdfsPath();
        // The file path which partitioned by scan_start_time (format:yyyyMMddHH0000)
        dsParsed.writeStream()
                .format(hdfsFileFormat)
                .option("path", saveHdfsPath + topicName + "/")
                .option("checkpointLocation", this.checkPointPath + queryName + "/")
                .outputMode("append")
                .partitionBy("scan_start_time")
                .trigger(Trigger.ProcessingTime(5, TimeUnit.MINUTES))
                .start();

更多方式,请参考《在Spark结构化流readStream、writeStream 输入输出,及过程ETL

 

posted @ 2018-10-12 10:53  cctext  阅读(1785)  评论(0编辑  收藏  举报