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/*
 * 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

import org.apache.spark.annotation.InterfaceStability

/**
 * The abstract class for writing custom logic to process data generated by a query.
 * This is often used to write the output of a streaming query to arbitrary storage systems.
 * Any implementation of this base class will be used by Spark in the following way.
 *
 * 
 * A single instance of this class is responsible of all the data generated by a single task
 *     in a query. In other words, one instance is responsible for processing one partition of the
 *     data generated in a distributed manner.
 *
 * 
Any implementation of this class must be serializable because each task will get a fresh
 *     serialized-deserialized copy of the provided object. Hence, it is strongly recommended that
 *     any initialization for writing data (e.g. opening a connection or starting a transaction)
 *     is done after the `open(...)` method has been called, which signifies that the task is
 *     ready to generate data.
 *
 * 
The lifecycle of the methods are as follows.
 *
 *    *   For each partition with `partitionId`:
 *       For each batch/epoch of streaming data (if its streaming query) with `epochId`:
 *           Method `open(partitionId, epochId)` is called.
 *           If `open` returns true:
 *                For each row in the partition and batch/epoch, method `process(row)` is called.
 *           Method `close(errorOrNull)` is called with error (if any) seen while processing rows.
 *   
 *
 * 
 *
 * Important points to note:
 * 
 * Spark doesn't guarantee same output for (partitionId, epochId), so deduplication
 *     cannot be achieved with (partitionId, epochId). e.g. source provides different number of
 *     partitions for some reason, Spark optimization changes number of partitions, etc.
 *     Refer SPARK-28650 for more details. If you need deduplication on output, try out
 *     `foreachBatch` instead.
 *
 * 
The `close()` method will be called if `open()` method returns successfully (irrespective
 *     of the return value), except if the JVM crashes in the middle.
 * 
 *
 * Scala example:
 * {{{
 *   datasetOfString.writeStream.foreach(new ForeachWriter[String] {
 *
 *     def open(partitionId: Long, version: Long): Boolean = {
 *       // open connection
 *     }
 *
 *     def process(record: String) = {
 *       // write string to connection
 *     }
 *
 *     def close(errorOrNull: Throwable): Unit = {
 *       // close the connection
 *     }
 *   })
 * }}}
 *
 * Java example:
 * {{{
 *  datasetOfString.writeStream().foreach(new ForeachWriter() {
 *
 *    @Override
 *    public boolean open(long partitionId, long version) {
 *      // open connection
 *    }
 *
 *    @Override
 *    public void process(String value) {
 *      // write string to connection
 *    }
 *
 *    @Override
 *    public void close(Throwable errorOrNull) {
 *      // close the connection
 *    }
 *  });
 * }}}
 *
 * @since 2.0.0
 */
@InterfaceStability.Evolving
abstract class ForeachWriter[T] extends Serializable {

  // TODO: Move this to org.apache.spark.sql.util or consolidate this with batch API.

  /**
   * Called when starting to process one partition of new data in the executor. See the class
   * docs for more information on how to use the `partitionId` and `epochId`.
   *
   * @param partitionId the partition id.
   * @param epochId a unique id for data deduplication.
   * @return `true` if the corresponding partition and version id should be processed. `false`
   *         indicates the partition should be skipped.
   */
  def open(partitionId: Long, epochId: Long): Boolean

  /**
   * Called to process the data in the executor side. This method will be called only if `open`
   * returns `true`.
   */
  def process(value: T): Unit

  /**
   * Called when stopping to process one partition of new data in the executor side. This is
   * guaranteed to be called either `open` returns `true` or `false`. However,
   * `close` won't be called in the following cases:
   *
   * 
   * JVM crashes without throwing a `Throwable`
   * `open` throws a `Throwable`.
   * 
   *
   * @param errorOrNull the error thrown during processing data or null if there was no error.
   */
  def close(errorOrNull: Throwable): Unit
}