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to you under the Apache License, Version 2.0 (the
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  http://www.apache.org/licenses/LICENSE-2.0

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"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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specific language governing permissions and limitations
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*/
package org.apache.griffin.measure.datasource.connector.streaming

import scala.util.{Failure, Success, Try}

import kafka.serializer.Decoder
import org.apache.spark.streaming.dstream.InputDStream

import org.apache.griffin.measure.utils.ParamUtil._

/**
  * streaming data connector for kafka
  */
trait KafkaStreamingDataConnector extends StreamingDataConnector {

  type KD <: Decoder[K]
  type VD <: Decoder[V]
  type OUT = (K, V)

  val config = dcParam.getConfig

  val KafkaConfig = "kafka.config"
  val Topics = "topics"

  val kafkaConfig = config.getAnyRef(KafkaConfig, Map[String, String]())
  val topics = config.getString(Topics, "")

  def init(): Unit = {
    // register fan in
    streamingCacheClientOpt.foreach(_.registerFanIn)

    val ds = stream match {
      case Success(dstream) => dstream
      case Failure(ex) => throw ex
    }
    ds.foreachRDD((rdd, time) => {
      val ms = time.milliseconds
      val saveDfOpt = try {
        // coalesce partition number
        val prlCount = rdd.sparkContext.defaultParallelism
        val ptnCount = rdd.getNumPartitions
        val repartitionedRdd = if (prlCount < ptnCount) {
          rdd.coalesce(prlCount)
        } else rdd

        val dfOpt = transform(repartitionedRdd)

        // pre-process
        preProcess(dfOpt, ms)
      } catch {
        case e: Throwable =>
          error(s"streaming data connector error: ${e.getMessage}")
          None
      }

      // save data frame
      streamingCacheClientOpt.foreach(_.saveData(saveDfOpt, ms))
    })
  }

  def stream(): Try[InputDStream[OUT]] = Try {
    val topicSet = topics.split(",").toSet
    createDStream(topicSet)
  }

  protected def createDStream(topicSet: Set[String]): InputDStream[OUT]

}