/*
 * 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.griffin.measure.sink

import org.apache.spark.rdd.RDD
import org.apache.spark.sql.DataFrame

import org.apache.griffin.measure.Loggable

/**
 * Base trait for batch and Streaming Sinks.
 * To implement custom sinks, extend your classes with this trait.
 */
trait Sink extends Loggable with Serializable {

  val jobName: String
  val timeStamp: Long

  val config: Map[String, Any]

  val block: Boolean

  /**
   * Ensures that the pre-requisites (if any) of the Sink are met before opening it.
   */
  def validate(): Boolean

  /**
   * Allows initialization of the connection to the sink (if required).
   *
   * @param applicationId Spark Application ID
   */
  def open(applicationId: String): Unit = {}

  /**
   * Allows clean up for the sink (if required).
   */
  def close(): Unit = {}

  /**
   * Implementation of persisting records for streaming pipelines.
   */
  def sinkRecords(records: RDD[String], name: String): Unit = {}

  /**
   * Implementation of persisting records for streaming pipelines.
   */
  def sinkRecords(records: Iterable[String], name: String): Unit = {}

  /**
   * Implementation of persisting metrics.
   */
  def sinkMetrics(metrics: Map[String, Any]): Unit = {}

  /**
   * Implementation of persisting records for batch pipelines.
   */
  def sinkBatchRecords(dataset: DataFrame, key: Option[String] = None): Unit = {}
}