Github user rdblue commented on a diff in the pull request: https://github.com/apache/spark/pull/19269#discussion_r144097061 --- Diff: sql/core/src/main/java/org/apache/spark/sql/sources/v2/writer/DataSourceV2Writer.java --- @@ -0,0 +1,88 @@ +/* + * 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 DataWriter}. + * + * The writing procedure is: + * 1. Create a writer factory by {@link #createWriterFactory()}, serialize and send it to all the + * partitions of the input data(RDD). + * 2. For each partition, create the data writer, 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()}. + * 3. If all writers are successfully committed, call {@link #commit(WriterCommitMessage[])}. If + * some writers are aborted, or the job failed with an unknown reason, call + * {@link #abort(WriterCommitMessage[])}. + * + * Spark won't retry failed writing jobs, users should do it manually in their Spark applications if + * they want to retry. + * + * Please refer to the document of commit/abort methods for detailed specifications. + * + * Note that, this interface provides a protocol between Spark and data sources for transactional + * data writing, but the transaction here is Spark-level transaction, which may not be the + * underlying storage transaction. For example, Spark successfully writes 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 writer factory which will be serialized and sent to executors. + */ + DataWriterFactory<Row> createWriterFactory(); + + /** + * Commits this writing job with a list of commit messages. The commit messages are collected from + * successful data writers and are produced by {@link DataWriter#commit()}. If this method + * fails(throw exception), this writing job is considered to be failed, and + * {@link #abort(WriterCommitMessage[])} will be called. The written data should only be visible + * to data source readers if this method successes. + * + * Note that, one partition may have multiple committed data writers because of speculative tasks. + * Spark will pick the first successful one and get its commit message. Implementations should be + * aware of this and handle it correctly, e.g., have a mechanism to make sure only one data writer + * can commit successfully, or have a way to clean up the data of already-committed writers. + */ + void commit(WriterCommitMessage[] messages); + + /** + * Aborts this writing job because some data writers are failed to write the records and aborted, + * or the Spark job fails with some unknown reasons, or {@link #commit(WriterCommitMessage[])} + * fails. If this method fails(throw exception), the underlying data source may have garbage that + * need to be cleaned manually, but these garbage should not be visible to data source readers. + * + * Unless the abortion is triggered by the failure of commit, the given messages should have some --- End diff -- I think the correct noun is simply "abort".
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