Github user jose-torres commented on a diff in the pull request: https://github.com/apache/spark/pull/20752#discussion_r172617421 --- Diff: sql/core/src/main/java/org/apache/spark/sql/sources/v2/writer/streaming/StreamingDataWriterFactory.java --- @@ -0,0 +1,51 @@ +/* + * 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.streaming; + +import org.apache.spark.annotation.InterfaceStability; +import org.apache.spark.sql.sources.v2.writer.DataWriter; +import org.apache.spark.sql.sources.v2.writer.DataWriterFactory; + +@InterfaceStability.Evolving +public interface StreamingDataWriterFactory<T> extends DataWriterFactory<T> { + /** + * Returns a data writer to do the actual writing work. + * + * If this method fails (by throwing an exception), the action would fail and no Spark job was + * submitted. + * + * @param partitionId A unique id of the RDD partition that the returned writer will process. + * Usually Spark processes many RDD partitions at the same time, + * implementations should use the partition id to distinguish writers for + * different partitions. + * @param attemptNumber Spark may launch multiple tasks with the same task id. For example, a task + * failed, Spark launches a new task wth the same task id but different + * attempt number. Or a task is too slow, Spark launches new tasks wth the + * same task id but different attempt number, which means there are multiple + * tasks with the same task id running at the same time. Implementations can + * use this attempt number to distinguish writers of different task attempts. + * @param epochId A monotonically increasing id for streaming queries that are split in to + * discrete periods of execution. For non-streaming queries, + * this ID will always be 0. + */ + DataWriter<T> createDataWriter(int partitionId, int attemptNumber, long epochId); + + @Override default DataWriter<T> createDataWriter(int partitionId, int attemptNumber) { + throw new IllegalStateException("Streaming data writer factory cannot create data writers without epoch."); --- End diff -- If there's no common interface, DataSourceRDD would need to take a java.util.List[Any] instead of java.util.List[DataWriterFactory[T]]. This kind of pattern is present in a lot of DataSourceV2 interfaces, and I think it's endemic to the general design.
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