Spark Structured Streaming ForeachWriter and Database Performance

Question

I have an implementation implementing a structured stream like this ...

myDataSet
  .map(r =>  StatementWrapper.Transform(r))
  .writeStream
  .foreach(MyWrapper.myWriter)
  .start()
  .awaitTermination()

It all works, but viewing the performance of MyWrapper.myWriter is terrible. It effectively tries to become a JDBC receiver, it looks like this:

val myWriter: ForeachWriter[Seq[String]] = new ForeachWriter[Seq[String]] {

  var connection: Connection = _

  override def open(partitionId: Long, version: Long): Boolean = {
    Try (connection = getRemoteConnection).isSuccess
  }

  override def process(row: Seq[String]) {
    val statement = connection.createStatement()
    try {
      row.foreach( s => statement.execute(s) )
    } catch {
      case e: SQLSyntaxErrorException => println(e)
      case e: SQLException => println(e)
    } finally {
      statement.closeOnCompletion()
    }
  }

  override def close(errorOrNull: Throwable) {
    connection.close()
  }
}

So my question is: has a new ForeachWriter been created for each row? so, for each row in the dataset, the open () and close () functions are called?

Is there a better design for better throughput?

How to parse an SQL statement once and execute many times, also keep the database connection open?

Answer 1

ForeachWriter.

, ForeachWriter, ForeachSink, , :

data.queryExecution.toRdd.foreachPartition { iter =>
  if (writer.open(TaskContext.getPartitionId(), batchId)) {
    try {
      while (iter.hasNext) {
        writer.process(encoder.fromRow(iter.next()))
      }
    } catch {
      case e: Throwable =>
        writer.close(e)
        throw e
    }
    writer.close(null)
  } else {
    writer.close(null)
  }
}

, . , open close , .

, , Sink, DataFrame:

trait Sink {
  def addBatch(batchId: Long, data: DataFrame): Unit
}