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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

/**
 * 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
 */
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
}