org.apache.spark.sql.sources.v2.writer.DataWriterFactory Maven / Gradle / Ivy
/*
* 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 java.io.Serializable;
import org.apache.spark.annotation.InterfaceStability;
/**
* A factory of {@link DataWriter} returned by {@link DataSourceWriter#createWriterFactory()},
* which is responsible for creating and initializing the actual data writer at executor side.
*
* Note that, the writer factory will be serialized and sent to executors, then the data writer
* will be created on executors and do the actual writing. So {@link DataWriterFactory} must be
* serializable and {@link DataWriter} doesn't need to be.
*/
@InterfaceStability.Evolving
public interface DataWriterFactory extends Serializable {
/**
* 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.
*/
DataWriter createDataWriter(int partitionId, int attemptNumber);
}