Github user rdblue commented on a diff in the pull request: https://github.com/apache/spark/pull/19269#discussion_r139838459 --- Diff: sql/core/src/main/java/org/apache/spark/sql/sources/v2/writer/DataSourceV2Writer.java --- @@ -0,0 +1,71 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.sources.v2.writer; + +import org.apache.spark.annotation.InterfaceStability; +import org.apache.spark.sql.Row; +import org.apache.spark.sql.SaveMode; +import org.apache.spark.sql.sources.v2.DataSourceV2Options; +import org.apache.spark.sql.sources.v2.WriteSupport; +import org.apache.spark.sql.types.StructType; + +/** + * A data source writer that is returned by + * {@link WriteSupport#createWriter(StructType, SaveMode, DataSourceV2Options)}. + * It can mix in various writing optimization interfaces to speed up the data saving. The actual + * writing logic is delegated to {@link WriteTask} that is returned by {@link #createWriteTask()}. + * + * The writing procedure is: + * 1. Create a write task by {@link #createWriteTask()}, serialize and send it to all the + * partitions of the input data(RDD). + * 2. For each partition, create a data writer with the write task, and write the data of the + * partition with this writer. If all the data are written successfully, call + * {@link DataWriter#commit()}. If exception happens during the writing, call + * {@link DataWriter#abort()}. This step may repeat several times as Spark will retry failed + * tasks. + * 3. Wait until all the writers/partitions are finished, i.e., either committed or aborted. If + * all partitions are written successfully, call {@link #commit(WriterCommitMessage[])}. If + * some partitions failed and aborted, call {@link #abort()}. + * + * Note that, data sources are responsible for providing transaction ability by implementing the + * `commit` and `abort` methods of {@link DataSourceV2Writer} and {@link DataWriter} correctly. + * The transaction here is Spark-level transaction, which may not be the underlying storage + * transaction. For example, Spark successfully write data to a Cassandra data source, but + * Cassandra may need some more time to reach consistency at storage level. + */ +@InterfaceStability.Evolving +public interface DataSourceV2Writer { + + /** + * Creates a write task which will be serialized and sent to executors. For each partition of the + * input data(RDD), there will be one write task to write the records. + */ + WriteTask<Row> createWriteTask(); + + /** + * Commits this writing job with a list of commit messages. The commit messages are collected from + * all data writers for this writing job and are produced by {@link DataWriter#commit()}. This + * also means all the data are written successfully and all data writers are committed. --- End diff -- I think this should state the guarantees when this method is called: * One and only one attempt for every task has committed successfully * Messages contains the commit message from every committed task attempt, which is no more than one per task. * All other attempts have been successfully aborted (is this a guarantee, or just that aborts have been attemtped?)
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