spark DataWriterFactory 源码
spark DataWriterFactory 代码
文件路径：/sql/catalyst/src/main/java/org/apache/spark/sql/connector/write/DataWriterFactory.java
/*
* 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.connector.write;
import java.io.Serializable;
import org.apache.spark.TaskContext;
import org.apache.spark.annotation.Evolving;
import org.apache.spark.sql.catalyst.InternalRow;
/**
* A factory of {@link DataWriter} returned by
* {@link BatchWrite#createBatchWriterFactory(PhysicalWriteInfo)}, which is responsible for
* creating and initializing the actual data writer at executor side.
* <p>
* 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 this interface must be
* serializable and {@link DataWriter} doesn't need to be.
*
* @since 3.0.0
*/
@Evolving
public interface DataWriterFactory extends Serializable {
/**
* Returns a data writer to do the actual writing work. Note that, Spark will reuse the same data
* object instance when sending data to the data writer, for better performance. Data writers
* are responsible for defensive copies if necessary, e.g. copy the data before buffer it in a
* list.
* <p>
* If this method fails (by throwing an exception), the corresponding Spark write task would fail
* and get retried until hitting the maximum retry times.
*
* @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 taskId The task id returned by {@link TaskContext#taskAttemptId()}. Spark may run
* multiple tasks for the same partition (due to speculation or task failures,
* for example).
*/
DataWriter<InternalRow> createWriter(int partitionId, long taskId);
}
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