//设置package包名称以及导入依赖的类
package contrib.kafka.serializer

import kafka.serializer.Decoder
import kafka.utils.VerifiableProperties

import org.apache.avro.io.BinaryDecoder
import org.apache.avro.io.DecoderFactory
import org.apache.avro.specific.SpecificDatumReader

import org.apache.flume.Event
import org.apache.flume.event.EventBuilder
import org.apache.flume.source.avro.AvroFlumeEvent

import java.io.ByteArrayInputStream

class AvroFlumeEventDecoder(props: VerifiableProperties = null)
  extends Decoder[Event] {

  private val reader: SpecificDatumReader[AvroFlumeEvent] =
    new SpecificDatumReader[AvroFlumeEvent](classOf[AvroFlumeEvent])
  private var decoder: BinaryDecoder = null.asInstanceOf[BinaryDecoder]

  override def fromBytes(bytes: Array[Byte]): Event = {
    val inputStream = new ByteArrayInputStream(bytes)
    decoder = DecoderFactory.get.directBinaryDecoder(inputStream, decoder)
    val avroEvent: AvroFlumeEvent = reader.read(null, decoder)
    EventBuilder.withBody(
      avroEvent.getBody.array,
      toStringJavaMap(avroEvent.getHeaders))
  }

  def toStringJavaMap(
    charSeqMap: JMap[CharSequence, CharSequence]): JMap[String, String] = {

    import scala.collection.JavaConversions._
    for ((k: CharSequence, v: CharSequence) <- charSeqMap)
      yield (k.toString, v.toString)
  }
} 

//设置package包名称以及导入依赖的类
package me.jie.ksrdd

import kafka.serializer.Decoder
import kafka.utils.VerifiableProperties



class DefaultDecoder(props: VerifiableProperties = null) extends Decoder[Array[Byte]] with Serializable{
    def fromBytes(bytes: Array[Byte]): Array[Byte] = bytes
}

class StringDecoder(props: VerifiableProperties = null) extends Decoder[String] with Serializable {
  val encoding =
    if(props == null)
      "UTF8"
    else
      props.getString("serializer.encoding", "UTF8")

  def fromBytes(bytes: Array[Byte]): String = {
    new String(bytes, encoding)
  }
} 

//设置package包名称以及导入依赖的类
package example.consumer

import kafka.consumer.{Consumer => KafkaConsumer, ConsumerIterator, Whitelist}
import kafka.serializer.{DefaultDecoder, Decoder}
import scala.collection.JavaConversions._

case class StreamConsumer(topics: List[String]) extends Consumer(topics) {
  //topics to listen
  private val filterSpec = new Whitelist(topics.mkString(","))

  protected val keyDecoder: Decoder[Array[Byte]] = new DefaultDecoder()
  protected val valueDecoder: Decoder[Array[Byte]] = new DefaultDecoder()

  private lazy val consumer = KafkaConsumer.create(config)
  private lazy val stream = consumer.createMessageStreamsByFilter(filterSpec, 1, keyDecoder, valueDecoder).get(0)

  def read(): Stream[String] = Stream.cons(new String(stream.head.message()), read())
}

object StreamConsumer {
  def apply(topics: List[String], kDecoder: Decoder[Array[Byte]], vDecoder: Decoder[Array[Byte]]) = new StreamConsumer(topics) {
    override val keyDecoder = kDecoder
    override val valueDecoder = vDecoder
  }
}

case class SingleTopicConsumer(topic: String) extends Consumer(List(topic)) {
  private lazy val consumer = KafkaConsumer.create(config)
  val threadNum = 1

  private lazy val consumerMap = consumer.createMessageStreams(Map(topic -> threadNum))
  private lazy val stream = consumerMap.getOrElse(topic, List()).head

  override def read(): Stream[String] = Stream.cons(new String(stream.head.message()), read())
} 

//设置package包名称以及导入依赖的类
package com.kakao.cuesheet.convert

import java.util.Arrays.copyOfRange

import kafka.serializer.Decoder
import kafka.utils.VerifiableProperties
import org.apache.avro.Schema
import org.apache.avro.generic.{GenericDatumReader, GenericRecord}


sealed trait AvroDecoder[T] extends Decoder[T] {

  def props: VerifiableProperties

  protected val schema = new Schema.Parser().parse(props.getString(Avro.SCHEMA))
  protected val skipBytes = props.getInt(Avro.SKIP_BYTES, 0)

  protected val reader = new GenericDatumReader[GenericRecord](schema)
  protected val decoder = Avro.recordDecoder(reader)

  private def skip(bytes: Array[Byte], size: Int): Array[Byte] = {
    val length = bytes.length
    length - size match {
      case remaining if remaining > 0 => copyOfRange(bytes, size, length)
      case _ => new Array[Byte](0)
    }
  }

  def parse(bytes: Array[Byte]): GenericRecord = {
    val data = if (skipBytes == 0) bytes else skip(bytes, skipBytes)
    decoder(data)
  }
}

class AvroRecordDecoder(val props: VerifiableProperties) extends AvroDecoder[GenericRecord] {
  override def fromBytes(bytes: Array[Byte]): GenericRecord = parse(bytes)
}

class AvroMapDecoder(val props: VerifiableProperties) extends AvroDecoder[Map[String, Any]] {
  override def fromBytes(bytes: Array[Byte]): Map[String, Any] = Avro.toMap(parse(bytes))
}

class AvroJsonDecoder(val props: VerifiableProperties) extends AvroDecoder[String] {
  override def fromBytes(bytes: Array[Byte]): String = Avro.toJson(parse(bytes))
} 

//设置package包名称以及导入依赖的类
package com.ippontech.kafka

import com.ippontech.kafka.stores.OffsetsStore
import com.typesafe.scalalogging.slf4j.LazyLogging
import kafka.message.MessageAndMetadata
import kafka.serializer.Decoder
import org.apache.spark.streaming.StreamingContext
import org.apache.spark.streaming.dstream.InputDStream
import org.apache.spark.streaming.kafka.KafkaUtils

import scala.reflect.ClassTag

object KafkaSource extends LazyLogging {

  def kafkaStream[K: ClassTag, V: ClassTag, KD <: Decoder[K] : ClassTag, VD <: Decoder[V] : ClassTag]
  (ssc: StreamingContext, kafkaParams: Map[String, String], offsetsStore: OffsetsStore, topic: String): InputDStream[(K, V)] = {

    val topics = Set(topic)

    val storedOffsets = offsetsStore.readOffsets(topic)
    val kafkaStream = storedOffsets match {
      case None =>
        // start from the latest offsets
        KafkaUtils.createDirectStream[K, V, KD, VD](ssc, kafkaParams, topics)
      case Some(fromOffsets) =>
        // start from previously saved offsets
        val messageHandler = (mmd: MessageAndMetadata[K, V]) => (mmd.key, mmd.message)
        KafkaUtils.createDirectStream[K, V, KD, VD, (K, V)](ssc, kafkaParams, fromOffsets, messageHandler)
    }

    // save the offsets
    kafkaStream.foreachRDD(rdd => offsetsStore.saveOffsets(topic, rdd))

    kafkaStream
  }

  // Kafka input stream
  def kafkaStream[K: ClassTag, V: ClassTag, KD <: Decoder[K] : ClassTag, VD <: Decoder[V] : ClassTag]
  (ssc: StreamingContext, brokers: String, offsetsStore: OffsetsStore, topic: String): InputDStream[(K, V)] =
    kafkaStream(ssc, Map("metadata.broker.list" -> brokers), offsetsStore, topic)

}