數據集 Spark scala 的批處理

Question

我正在嘗試在 Spark 中創建成批的Dataset行。 為了保持發送到服務的記錄數量，我想對項目進行批處理，以便我可以保持發送數據的速率。 為了，

case class Person(name:String, address: String)
case class PersonBatch(personBatch: List[Person])

對於給定的Dataset[Person]我想創建Dataset[PersonBatch]

例如，如果輸入Dataset[Person]有 100 條記錄，則輸出Dataset應該類似於Dataset[PersonBatch] ，其中每個PersonBatch應該是n條記錄（Person）的列表。

我試過這個，但它不起作用。

object DataBatcher extends Logger {

  var batchList: ListBuffer[PersonBatch] = ListBuffer[PersonBatch]()
  var batchSize: Long = 500  //default batch size

  def addToBatchList(batch: PersonBatch): Unit = {
    batchList += batch
  }

  def clearBatchList(): Unit = {
    batchList.clear()
  }

  def createBatches(ds: Dataset[Person]): Dataset[PersonBatch] = {

    val dsCount = ds.count()
    logger.info(s"Count of dataset passed for creating batches : ${dsCount}")
    val batchElement = ListBuffer[Person]()
    val batch = PersonBatch(batchElement)
    ds.foreach(x => {
      batch.personBatch += x
      if(batch.personBatch.length == batchSize) {
        addToBatchList(batch)
        batch.requestBatch.clear()
      }
    })
    if(batch.personBatch.length > 0) {
      addToBatchList(batch)
      batch.personBatch.clear()
    }
    sparkSession.createDataset(batchList)
  }  
}

我想在 Hadoop 集群上運行這個作業。 有人能幫我解決這個問題嗎？

Answer 1

rdd.iterator 的分組功能可能對你有用。

例如 ：

iter.grouped(batchSize)

使用 iter.grouped(batchsize) 批量插入的示例代碼片段，這里是 1000，我試圖插入到數據庫中

   df.repartition(numofpartitionsyouwant) // numPartitions ~ number of simultaneous DB connections you can planning to give...
def insertToTable(sqlDatabaseConnectionString: String,
                  sqlTableName: String): Unit = {

  val tableHeader: String = dataFrame.columns.mkString(",")
  dataFrame.foreachPartition { partition =>
    //NOTE : EACH PARTITION ONE CONNECTION (more better way is to use connection pools)
    val sqlExecutorConnection: Connection =
      DriverManager.getConnection(sqlDatabaseConnectionString)
    //Batch size of 1000 is used since some databases cant use batch size more than 1000 for ex : Azure sql
    partition.grouped(1000).foreach { group =>
      val insertString: scala.collection.mutable.StringBuilder =
        new scala.collection.mutable.StringBuilder()

      group.foreach { record =>
        insertString.append("('" + record.mkString(",") + "'),")
      }

      sqlExecutorConnection
        .createStatement()
        .executeUpdate(f"INSERT INTO [$sqlTableName] ($tableHeader) VALUES "
          + insertString.stripSuffix(","))
    }

    sqlExecutorConnection.close() // close the connection so that connections wont exhaust.
  }
}

Answer 2

val tableHeader: String = dataFrame.columns.mkString(",")
dataFrame.foreachPartition((it: Iterator[Row]) => {
      println("partition index: " )
      val url = "jdbc:...+ "user=;password=;"
      val conn = DriverManager.getConnection(url)
      conn.setAutoCommit(true)
      val stmt = conn.createStatement()
      val batchSize = 10
      var i =0
      while (it.hasNext) {
        val row = it.next
        import java.sql.SQLException
        import java.sql.SQLIntegrityConstraintViolationException
        try {
          stmt.addBatch(" UPDATE TABLE SET STATUS = 0 , " +
            " DATE ='" + new java.sql.Timestamp(System.currentTimeMillis()) +"'" +
            " where id = " + row.getAs("IDNUM")  )
          i += 1
          if (  i  % batchSize == 0 ) {
            stmt.executeBatch
            conn.commit
          }
        } catch {
          case e: SQLIntegrityConstraintViolationException =>
          case e: SQLException =>
            e.printStackTrace()
        }
        finally {
             stmt.executeBatch
             conn.commit
        }

      }
      import java.util
      val ret = stmt.executeBatch
      System.out.println("Ret val: " + util.Arrays.toString(ret))
      System.out.println("Update count: " + stmt.getUpdateCount)
      conn.commit
      stmt.close