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最近由于使用sparkstreaming的同学需要对接到部门内部的的kafka集群，由于官方的spark-streaming-kafka包和现有公司的kafka集群权限系统无法对接，需要研究下spark-streaming-kafka包原有代码以便改造，本文研究的代码版本为spark在github的tag的v1.6.1版本。
官方给出的JavaKafkaWordCount以及KafkaWordCount代码里产生kafka-streaming消费流数据的调用代码分别如下
JavaPairReceiverInputDStream<String, String> messages =
KafkaUtils.createStream(jssc, args[0], args[1], topicMap);
val lines = KafkaUtils.createStream(ssc, zkQuorum, group, topicMap).map(_._2)
可以看到无论是java还是scala调用的都是KafkaUtils内重载实现的createStream方法。
object KafkaUtils {
/**
* Create an input stream that pulls messages from Kafka Brokers.
* @param ssc StreamingContext object
* @param zkQuorum Zookeeper quorum (hostname:port,hostname:port,..)
* @param groupId The group id for this consumer
* @param topics Map of (topic_name -> numPartitions) to consume. Each partition is consumed
* in its own thread
* @param storageLevel Storage level to use for storing the received objects
* (default: StorageLevel.MEMORY_AND_DISK_SER_2)
* @return DStream of (Kafka message key, Kafka message value)
*/
def createStream(
ssc: StreamingContext,
zkQuorum: String,
groupId: String,
topics: Map[String, Int],
storageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK_SER_2
): ReceiverInputDStream[(String, String)] = {
val kafkaParams = Map[String, String](
"zookeeper.connect" -> zkQuorum, "group.id" -> groupId,
"zookeeper.connection.timeout.ms" -> "10000")
createStream[String, String, StringDecoder, StringDecoder](
ssc, kafkaParams, topics, storageLevel)
}
/**
* Create an input stream that pulls messages from Kafka Brokers.
* @param ssc StreamingContext object
* @param kafkaParams Map of kafka configuration parameters,
* see http://kafka.apache.org/08/configuration.html
* @param topics Map of (topic_name -> numPartitions) to consume. Each partition is consumed
* in its own thread.
* @param storageLevel Storage level to use for storing the received objects
* @tparam K type of Kafka message key
* @tparam V type of Kafka message value
* @tparam U type of Kafka message key decoder
* @tparam T type of Kafka message value decoder
* @return DStream of (Kafka message key, Kafka message value)
*/
def createStream[K: ClassTag, V: ClassTag, U <: Decoder[_]: ClassTag, T <: Decoder[_]: ClassTag](
ssc: StreamingContext,
kafkaParams: Map[String, String],
topics: Map[String, Int],
storageLevel: StorageLevel
): ReceiverInputDStream[(K, V)] = {
val walEnabled = WriteAheadLogUtils.enableReceiverLog(ssc.conf)
new KafkaInputDStream[K, V, U, T](ssc, kafkaParams, topics, walEnabled, storageLevel)
}
/**
* Create an input stream that pulls messages from Kafka Brokers.
* Storage level of the data will be the default StorageLevel.MEMORY_AND_DISK_SER_2.
* @param jssc JavaStreamingContext object
* @param zkQuorum Zookeeper quorum (hostname:port,hostname:port,..)
* @param groupId The group id for this consumer
* @param topics Map of (topic_name -> numPartitions) to consume. Each partition is consumed
* in its own thread
* @return DStream of (Kafka message key, Kafka message value)
*/
def createStream(
jssc: JavaStreamingContext,
zkQuorum: String,
groupId: String,
topics: JMap[String, JInt]
): JavaPairReceiverInputDStream[String, String] = {
createStream(jssc.ssc, zkQuorum, groupId, Map(topics.asScala.mapValues(_.intValue()).toSeq: _*))
}
/**
* Create an input stream that pulls messages from Kafka Brokers.
* @param jssc JavaStreamingContext object
* @param zkQuorum Zookeeper quorum (hostname:port,hostname:port,..).
* @param groupId The group id for this consumer.
* @param topics Map of (topic_name -> numPartitions) to consume. Each partition is consumed
* in its own thread.
* @param storageLevel RDD storage level.
* @return DStream of (Kafka message key, Kafka message value)
*/
def createStream(
jssc: JavaStreamingContext,
zkQuorum: String,
groupId: String,
topics: JMap[String, JInt],
storageLevel: StorageLevel
): JavaPairReceiverInputDStream[String, String] = {
createStream(jssc.ssc, zkQuorum, groupId, Map(topics.asScala.mapValues(_.intValue()).toSeq: _*),
storageLevel)
}
/**
* Create an input stream that pulls messages from Kafka Brokers.
* @param jssc JavaStreamingContext object
* @param keyTypeClass Key type of DStream
* @param valueTypeClass value type of Dstream
* @param keyDecoderClass Type of kafka key decoder
* @param valueDecoderClass Type of kafka value decoder
* @param kafkaParams Map of kafka configuration parameters,
* see http://kafka.apache.org/08/configuration.html
* @param topics Map of (topic_name -> numPartitions) to consume. Each partition is consumed
* in its own thread
* @param storageLevel RDD storage level.
* @tparam K type of Kafka message key
* @tparam V type of Kafka message value
* @tparam U type of Kafka message key decoder
* @tparam T type of Kafka message value decoder
* @return DStream of (Kafka message key, Kafka message value)
*/
def createStream[K, V, U <: Decoder[_], T <: Decoder[_]](
jssc: JavaStreamingContext,
keyTypeClass: Class[K],
valueTypeClass: Class[V],
keyDecoderClass: Class[U],
valueDecoderClass: Class[T],
kafkaParams: JMap[String, String],
topics: JMap[String, JInt],
storageLevel: StorageLevel
): JavaPairReceiverInputDStream[K, V] = {
implicit val keyCmt: ClassTag[K] = ClassTag(keyTypeClass)
implicit val valueCmt: ClassTag[V] = ClassTag(valueTypeClass)
implicit val keyCmd: ClassTag[U] = ClassTag(keyDecoderClass)
implicit val valueCmd: ClassTag[T] = ClassTag(valueDecoderClass)
createStream[K, V, U, T](
jssc.ssc,
kafkaParams.asScala.toMap,
Map(topics.asScala.mapValues(_.intValue()).toSeq: _*),
storageLevel)
}
其中java相关的第三个和第四个createStream调用了第一个createStream，而第一个createStream最后调用的是第二个createStream，所以所有的rdd数据流都是从下面这句代码产生的：
new KafkaInputDStream[K, V, U, T](ssc, kafkaParams, topics, walEnabled, storageLevel)
查看KafkaInputDStream类定义，发现获取receiver有两种类型：KafkaReceiver和ReliableKafkaReceiver。
def getReceiver(): Receiver[(K, V)] = {
if (!useReliableReceiver) {
new KafkaReceiver[K, V, U, T](kafkaParams, topics, storageLevel)
} else {
new ReliableKafkaReceiver[K, V, U, T](kafkaParams, topics, storageLevel)
}
}
其中，KafkaReceiver实现比较简单，调用的是kafka的high level api产生数据流，产生的每个线程的数据流都被放到一个线程池由单独的线程来消费
val topicMessageStreams = consumerConnector.createMessageStreams(
topics, keyDecoder, valueDecoder)
ReliableKafkaReceiver是结合了spark的预写日志（Write Ahead Logs）功能，开启这个功能需要设置sparkconf属性 spark.streaming.receiver.writeAheadLog.enable为真（默认值是假）
这个receiver会把收到的kafka数据首先存储到日志上，然后才会向kafka提交offset，这样保证了在driver程序出现问题的时候不会丢失kafka数据。
参考文章 Spark Streaming容错的改进和零数据丢失