A data writer returned by 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 #close() to let DataWriter doing resource cleanup. After calling #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, 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 org.apache.spark.sql.catalyst.InternalRow.