ForeachSink¶

ForeachSink is a typed streaming sink that passes rows (of the type T) to ForeachWriter (one record at a time per partition).

ForeachSink is used exclusively in foreach operator.

val records = spark.
  readStream
  format("text").
  load("server-logs/*.out").
  as[String]

import org.apache.spark.sql.ForeachWriter
val writer = new ForeachWriter[String] {
  override def open(partitionId: Long, version: Long) = true
  override def process(value: String) = println(value)
  override def close(errorOrNull: Throwable) = {}
}

records.writeStream
  .queryName("server-logs processor")
  .foreach(writer)
  .start

Internally, addBatch (the only method from the <>) takes records from the input spark-sql-dataframe.md[DataFrame] (as data), transforms them to expected type T (of this ForeachSink) and (now as a spark-sql-dataset.md[Dataset]) spark-sql-dataset.md#foreachPartition[processes each partition].

[source, scala]¶

addBatch(batchId: Long, data: DataFrame): Unit¶

addBatch then opens the constructor's datasources/foreach/ForeachWriter.md[ForeachWriter] (for the spark-taskscheduler-taskcontext.md#getPartitionId[current partition] and the input batch) and passes the records to process (one at a time per partition).

CAUTION: FIXME Why does Spark track whether the writer failed or not? Why couldn't it finally and do close?

CAUTION: FIXME Can we have a constant for "foreach" for source in DataStreamWriter?