ForeachWriter

The abstract class for writing custom logic to process data generated by a query. This is often used to write the output of a streaming query to arbitrary storage systems. Any implementation of this base class will be used by Spark in the following way.

• A single instance of this class is responsible of all the data generated by a single task in a query. In other words, one instance is responsible for processing one partition of the data generated in a distributed manner.
• Any implementation of this class must be serializable because each task will get a fresh serialized-deserialized copy of the provided object. Hence, it is strongly recommended that any initialization for writing data (e.g. opening a connection or starting a transaction) is done after the open(...) method has been called, which signifies that the task is ready to generate data.
• The lifecycle of the methods are as follows.

  For each partition with `partitionId`:
      For each batch/epoch of streaming data (if its streaming query) with `epochId`:
          Method `open(partitionId, epochId)` is called.
          If `open` returns true:
               For each row in the partition and batch/epoch, method `process(row)` is called.
          Method `close(errorOrNull)` is called with error (if any) seen while processing rows.

Important points to note:

• The partitionId and epochId can be used to deduplicate generated data when failures cause reprocessing of some input data. This depends on the execution mode of the query. If the streaming query is being executed in the micro-batch mode, then every partition represented by a unique tuple (partitionId, epochId) is guaranteed to have the same data. Hence, (partitionId, epochId) can be used to deduplicate and/or transactionally commit data and achieve exactly-once guarantees. However, if the streaming query is being executed in the continuous mode, then this guarantee does not hold and therefore should not be used for deduplication.
• The close() method will be called if open() method returns successfully (irrespective of the return value), except if the JVM crashes in the middle.

Scala example:

datasetOfString.writeStream.foreach(new ForeachWriter[String] {

  def open(partitionId: Long, version: Long): Boolean = {
    // open connection
  }

  def process(record: String) = {
    // write string to connection
  }

  def close(errorOrNull: Throwable): Unit = {
    // close the connection
  }
})

Java example:

datasetOfString.writeStream().foreach(new ForeachWriter<String>() {

  @Override
  public boolean open(long partitionId, long version) {
    // open connection
  }

  @Override
  public void process(String value) {
    // write string to connection
  }

  @Override
  public void close(Throwable errorOrNull) {
    // close the connection
  }
});