Spark Streaming在企业级使用中，一般会使用no receiver的方式读取数据，对应kafka中的Direct方式，采用no receiver的方式可以提高数据读取效率并保证事务的一致性，看看在Spark Streaming中是怎样使用kafka的Direct方式
首先展示一个Demo,代码如下
object DirectKafkaWordCount {
def main(args: Array[String]) { if (args.length < 2) {
System.err.println(s"""
|Usage: DirectKafkaWordCount <brokers> <topics>
| <brokers> is a list of one or more Kafka brokers
| <topics> is a list of one or more kafka topics to consume from
|
""".stripMargin)
System.exit(1)
}
StreamingExamples.setStreamingLogLevels()
val Array(brokers, topics) = args // Create context with 2 second batch interval
val sparkConf = new SparkConf().setAppName("DirectKafkaWordCount")
val ssc = new StreamingContext(sparkConf, Seconds(2)) // Create direct kafka stream with brokers and topics
val topicsSet = topics.split(",").toSet
val kafkaParams = Map[String, String]("metadata.broker.list" -> brokers)
val messages = KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder](
ssc, kafkaParams, topicsSet) // Get the lines, split them into words, count the words and print
val lines = messages.map(_._2)
val words = lines.flatMap(_.split(" "))
val wordCounts = words.map(x => (x, 1L)).reduceByKey(_ + _)
wordCounts.print() // Start the computation
ssc.start()
ssc.awaitTermination()
}
}这里使用了KafkaUtils.createDirectStream方法来创建了一个DirectKafkaInputDStream,createDirectStream的代码如下
def createDirectStream[
K: ClassTag, V: ClassTag,
KD <: Decoder[K]: ClassTag,
VD <: Decoder[V]: ClassTag] (
ssc: StreamingContext, kafkaParams: Map[String, String], topics: Set[String]
): InputDStream[(K, V)] = {
val messageHandler = (mmd: MessageAndMetadata[K, V]) => (mmd.key, mmd.message)
val kc = new KafkaCluster(kafkaParams)
val fromOffsets = getFromOffsets(kc, kafkaParams, topics) new DirectKafkaInputDStream[K, V, KD, VD, (K, V)](ssc, kafkaParams, fromOffsets, messageHandler)
}首先实例化了一个KafkaCluster，KafkaCluster里面封装了Spark Streaming操作kafka Api的所有方法。重点来了，如果我们在其他项目中需要读取kafka的数据，KafkaCluster就是最好的参考文档了。接着看getFromOffsets(kc, kafkaParams, topics)方法，返回的fromOffsets 变量类型为 Map[TopicAndPartition, Long]，TopicAndPartition是kafka里的对象，代表了topic和他的第几个分区，Long的值就是offset(数据偏移量)，kafka是一个分布式消息队列，每一个topic可以有多个partition，多个partition可以提高kafka的吞吐量。
接着看DirectKafkaInputDStream内部代码，compute()方法代码如下
//这里没有使用receiver，而是直接包装了KafkaRDD进行数据读取override def compute(validTime: Time): Option[KafkaRDD[K, V, U, T, R]] = { // 获取每个topic中每个partition的offset结束偏移量，对应currentOffsets。代表了每个partition数据的开始和结束位置
val untilOffsets = clamp(latestLeaderOffsets(maxRetries)) // KafkaRDD用来从kafka中读取数据，类似hadoopRDD从hdfs中读取数据，如果我们读取其他数据来源，也可以自定义RDD
val rdd = KafkaRDD[K, V, U, T, R](context.sparkContext, kafkaParams, currentOffsets, untilOffsets, messageHandler) // Report the record number and metadata of this batch interval to InputInfoTracker.
// offsetRanges 表示每次读取partition数据的范围
val offsetRanges = currentOffsets.map { case (tp, fo) =>
val uo = untilOffsets(tp)
OffsetRange(tp.topic, tp.partition, fo, uo.offset)
}
val description = offsetRanges.filter { offsetRange => // Don't display empty ranges.
offsetRange.fromOffset != offsetRange.untilOffset
}.map { offsetRange =>
s"topic: ${offsetRange.topic}\tpartition: ${offsetRange.partition}\t" +
s"offsets: ${offsetRange.fromOffset} to ${offsetRange.untilOffset}"
}.mkString("\n") // Copy offsetRanges to immutable.List to prevent from being modified by the user
val metadata = Map("offsets" -> offsetRanges.toList,StreamInputInfo.METADATA_KEY_DESCRIPTION -> description)
val inputInfo = StreamInputInfo(id, rdd.count, metadata)
ssc.scheduler.inputInfoTracker.reportInfo(validTime, inputInfo) // 更新currentOffsets，这次的结束位置就下次的开始位置
currentOffsets = untilOffsets.map(kv => kv._1 -> kv._2.offset)
Some(rdd)
}主要是获取untilOffsets ，把currentOffsets和untilOffsets 传递到KafkaRDD中，这样KafkaRDD就知道读取哪些数据了。
看KafkaRDD中都有哪些内容，首先使用KafkaRDD的伴生对象去创建Kafka的实例，看一下Kafka的apply方法，代码如下
def apply[
K: ClassTag, V: ClassTag,
U <: Decoder[_]: ClassTag,
T <: Decoder[_]: ClassTag, R: ClassTag](
sc: SparkContext, kafkaParams: Map[String, String], fromOffsets: Map[TopicAndPartition, Long], untilOffsets: Map[TopicAndPartition, LeaderOffset], messageHandler: MessageAndMetadata[K, V] => R
): KafkaRDD[K, V, U, T, R] = {
val leaders = untilOffsets.map { case (tp, lo) =>
tp -> (lo.host, lo.port)
}.toMap // offsetRanges代表了数据偏移量的开始和结束位置
val offsetRanges = fromOffsets.map { case (tp, fo) =>
val uo = untilOffsets(tp)
OffsetRange(tp.topic, tp.partition, fo, uo.offset)
}.toArray new KafkaRDD[K, V, U, T, R](sc, kafkaParams, offsetRanges, leaders, messageHandler)
}主要就是生成了一个offsetRanges，和leaders(数据所在的ip地址和端口)，messageHandler也是一个很有意思的函数，一会再介绍
看KafkaRDD的代码，自定义一个RDD，最关键的三个方法分别是getPartitions、getPreferredLocations、compute。
(1)首先看getPartitions，代码如下
override def getPartitions: Array[Partition] = {
offsetRanges.zipWithIndex.map { case (o, i) =>
val (host, port) = leaders(TopicAndPartition(o.topic, o.partition)) new KafkaRDDPartition(i, o.topic, o.partition, o.fromOffset, o.untilOffset, host, port)
}.toArray
}每一个topic的每一个partition对应一个Spark中的partition，从这里我们可以评估一个kafka数据源（topic）需要多少个core资源来计算
(2)再看getPreferredLocations方法，代码如下
override def getPreferredLocations(thePart: Partition): Seq[String] = {
val part = thePart.asInstanceOf[KafkaRDDPartition]
// TODO is additional hostname resolution necessary here
Seq(part.host)
}getPreferredLocations决定了数据本地性，如果kafka中broker和Spark在同一个集群中，此时getPreferredLocations获取本地性就可以极大提高效率，因为没有了数据网络传输的成本
(3)最后看compute方法，代码如下
override def compute(thePart: Partition, context: TaskContext): Iterator[R] = {
val part = thePart.asInstanceOf[KafkaRDDPartition] assert(part.fromOffset <= part.untilOffset, errBeginAfterEnd(part)) if (part.fromOffset == part.untilOffset) {
log.info(s"Beginning offset ${part.fromOffset} is the same as ending offset " +
s"skipping ${part.topic} ${part.partition}")
Iterator.empty
} else {
new KafkaRDDIterator(part, context)
}
}主要功能封装在KafkaRDDIterator中，代码如下
private class KafkaRDDIterator(part: KafkaRDDPartition,context: TaskContext) extends NextIterator[R] {
context.addTaskCompletionListener{ context => closeIfNeeded() }
log.info(s"Computing topic ${part.topic}, partition ${part.partition} " +
s"offsets ${part.fromOffset} -> ${part.untilOffset}")
val kc = new KafkaCluster(kafkaParams)
val keyDecoder = classTag[U].runtimeClass.getConstructor(classOf[VerifiableProperties])
.newInstance(kc.config.props)
.asInstanceOf[Decoder[K]]
val valueDecoder = classTag[T].runtimeClass.getConstructor(classOf[VerifiableProperties])
.newInstance(kc.config.props)
.asInstanceOf[Decoder[V]]
val consumer = connectLeader
var requestOffset = part.fromOffset
var iter: Iterator[MessageAndOffset] = null
// The idea is to use the provided preferred host, except on task retry attempts,
// to minimize number of kafka metadata requests
private def connectLeader: SimpleConsumer = { if (context.attemptNumber > 0) {
kc.connectLeader(part.topic, part.partition).fold(
errs => throw new SparkException(s"Couldn't connect to leader for topic ${part.topic} ${part.partition}: " +
errs.mkString("\n")),
consumer => consumer
)
} else {
kc.connect(part.host, part.port)
}
} private def handleFetchErr(resp: FetchResponse) { if (resp.hasError) {
val err = resp.errorCode(part.topic, part.partition) if (err == ErrorMapping.LeaderNotAvailableCode ||
err == ErrorMapping.NotLeaderForPartitionCode) {
log.error(s"Lost leader for topic ${part.topic} partition ${part.partition}, " +
s" sleeping for ${kc.config.refreshLeaderBackoffMs}ms")
Thread.sleep(kc.config.refreshLeaderBackoffMs)
} // Let normal rdd retry sort out reconnect attempts
throw ErrorMapping.exceptionFor(err)
}
} // 3、获取一批数据
private def fetchBatch: Iterator[MessageAndOffset] = { // 4、包装一个request请求对象
val req = new FetchRequestBuilder()
.addFetch(part.topic, part.partition, requestOffset, kc.config.fetchMessageMaxBytes)
.build() // 5、使用SimpleConsumer对象发送fetch请求，返回response
val resp = consumer.fetch(req)
handleFetchErr(resp) // kafka may return a batch that starts before the requested offset
// 5、从返回的response中得到一个数据的迭代器
resp.messageSet(part.topic, part.partition)
.iterator
.dropWhile(_.offset < requestOffset)
} override def close(): Unit = { if (consumer != null) {
consumer.close()
}
} // 1、从读取数据开始看
override def getNext(): R = { if (iter == null || !iter.hasNext) { // 2、获取一些数据，数据包装在迭代器中
iter = fetchBatch
} if (!iter.hasNext) { assert(requestOffset == part.untilOffset, errRanOutBeforeEnd(part))
finished = true
null.asInstanceOf[R]
} else { // 6、不断读取数据
val item = iter.next() if (item.offset >= part.untilOffset) { assert(item.offset == part.untilOffset, errOvershotEnd(item.offset, part))
finished = true
null.asInstanceOf[R]
} else {
requestOffset = item.nextOffset // 7、将数据封装到MessageAndMetadata类中
messageHandler(new MessageAndMetadata(part.topic, part.partition, item.message, item.offset, keyDecoder, valueDecoder))
}
}
}
}跟着KafkaRDDIterator里的步骤来看代码
(4)最后返回的消息被封装到了MessageAndMetadata中，那么messageHandler是个什么东东，我们顺着代码向上找，是在KafkaUtils的createDirectStream方法有定义的，代码如下
val messageHandler = (mmd: MessageAndMetadata[K, V]) => (mmd.key, mmd.message)
原来是取消息中的key和message，如果我们想取出其他参数可以自定义messageHandler函数，是不是有点意思。
作者：海纳百川_spark
链接：https://www.jianshu.com/p/7d71f34eb4c4
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