Package org.apache.spark.sql.connector.write

Interface DataWriter<T>

All Superinterfaces:
AutoCloseable, Closeable
All Known Subinterfaces:
DeltaWriter<T>
@Evolving public interface DataWriter<T> extends Closeable
A data writer returned by DataWriterFactory.createWriter(int, long) and is responsible for writing data for an input RDD partition.

One Spark task has one exclusive data writer, so there is no thread-safe concern.

write(Object) is called for each record in the input RDD partition. If one record fails the write(Object), abort() is called afterwards and the remaining records will not be processed. If all records are successfully written, commit() is called.

Once a data writer returns successfully from commit() or abort(), Spark will call Closeable.close() to let DataWriter doing resource cleanup. After calling Closeable.close(), its lifecycle is over and Spark will not use it again.

If this data writer succeeds(all records are successfully written and commit() succeeds), a WriterCommitMessage will be sent to the driver side and pass to BatchWrite.commit(WriterCommitMessage[]) with commit messages from other data writers. If this data writer fails(one record fails to write or commit() fails), an exception will be sent to the driver side, and Spark may retry this writing task a few times. In each retry, DataWriterFactory.createWriter(int, long) will receive a different `taskId`. Spark will call BatchWrite.abort(WriterCommitMessage[]) when the configured number of retries is exhausted.

Besides the retry mechanism, Spark may launch speculative tasks if the existing writing task takes too long to finish. Different from retried tasks, which are launched one by one after the previous one fails, speculative tasks are running simultaneously. It's possible that one input RDD partition has multiple data writers with different `taskId` running at the same time, and data sources should guarantee that these data writers don't conflict and can work together. Implementations can coordinate with driver during commit() to make sure only one of these data writers can commit successfully. Or implementations can allow all of them to commit successfully, and have a way to revert committed data writers without the commit message, because Spark only accepts the commit message that arrives first and ignore others.

Note that, Currently the type T can only be InternalRow.

Since:
3.0.0

  • Method Details

    • write

      void write(T record) throws IOException
      Writes one record.

      If this method fails (by throwing an exception), abort() will be called and this data writer is considered to have been failed.

      Throws:
      IOException - if failure happens during disk/network IO like writing files.

    • writeAll

      default void writeAll(Iterator<T> records) throws IOException
      Writes all records provided by the given iterator. By default, it calls the write(T) method for each record in the iterator.

      If this method fails (by throwing an exception), abort() will be called and this data writer is considered to have been failed.

      Throws:
      IOException - if failure happens during disk/network IO like writing files.
      Since:
      4.0.0

    • commit

      WriterCommitMessage commit() throws IOException
      Commits this writer after all records are written successfully, returns a commit message which will be sent back to driver side and passed to BatchWrite.commit(WriterCommitMessage[]).

      The written data should only be visible to data source readers after BatchWrite.commit(WriterCommitMessage[]) succeeds, which means this method should still "hide" the written data and ask the BatchWrite at driver side to do the final commit via WriterCommitMessage.

      If this method fails (by throwing an exception), abort() will be called and this data writer is considered to have been failed.

      Throws:
      IOException - if failure happens during disk/network IO like writing files.

    • abort

      void abort() throws IOException
      Aborts this writer if it is failed. Implementations should clean up the data for already written records.

      This method will only be called if there is one record failed to write, or commit() failed.

      If this method fails(by throwing an exception), the underlying data source may have garbage that need to be cleaned by BatchWrite.abort(WriterCommitMessage[]) or manually, but these garbage should not be visible to data source readers.

      Throws:
      IOException - if failure happens during disk/network IO like writing files.

    • currentMetricsValues

      default CustomTaskMetric[] currentMetricsValues()
      Returns an array of custom task metrics. By default it returns empty array. Note that it is not recommended to put heavy logic in this method as it may affect writing performance.