Spark Streaming +Kafka 使用底层API直接读取Kafka的Partition数据，手动更新Offset到Zookeeper集群
Spark Streaming +Kafka 使用底层API直接读取Kafka的Partition数据，正常Offset存储在CheckPoint中。但是这样无法实现Kafka监控工具对Kafka的监控，所以手动更新Offset到Zookeeper集群中
相关源码简单介绍：
1:TopicAndPartition是对 topic和partition的id的封装的一个样例类
case class TopicAndPartition(topic: String, partitionId: Int)2：OffsetRange 是对topic name，partition id，fromOffset（当前消费的开始偏移），untilOffset（当前消费的结束偏移）的封装。所以OffsetRange 包含信息有：topic名字，分区Id，开始偏移，结束偏移。
/**
*
* @param topic Kafka topic name
* @param partition Kafka partition id
* @param fromOffset inclusive starting offset
* @param untilOffset exclusive ending offset
*/
final class OffsetRange private(val topic: String, val partition: Int, val fromOffset: Long, val untilOffset: Long) extends Serializable3：代码实现：
object Iteblog {
val brokerAddress = "http://www.iteblog.com:9092"
val groupID="testGroup"
val kafkaParams = Map[String, String](
"metadata.broker.list" -> brokerAddress,
"group.id" -> "iteblog")
def main(args: Array[String]) {
val sparkConf = new SparkConf().setAppName("Test")
sparkConf.set("spark.kryo.registrator", "utils.CpcKryoSerializer")
val sc = new SparkContext(sparkConf)
val ssc = new StreamingContext(sc, Seconds(2))
val topicsSet = Set("iteblog")
val messages = KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder](ssc, kafkaParams, topicsSet)
messages.foreachRDD(rdd => {
// 把RDD转成HasOffsetRanges类型（KafkaRDD extends HasOffsetRanges）
// OffsetRange 说明：Represents a range of offsets from a single Kafka TopicAndPartition.
// OffsetRange 说明： Instances of this class can be created with `OffsetRange.create()`.
val offsetsList: Array[OffsetRange] = rdd.asInstanceOf[HasOffsetRanges].offsetRanges
// offsetRanges 的实现代码（KafkaRDD中）：tp：TopicAndPartition,fo:fromOffset
// val offsetRanges = fromOffsets.map { case (tp, fo) =>
// val uo = untilOffsets(tp)
// OffsetRange(tp.topic, tp.partition, fo, uo.offset)
// }.toArray
val kc = new KafkaCluster(kafkaParams)
for (offsets <- offsetsList) {
//TopicAndPartition 主构造参数第一个是topic，第二个是 partition id
val topicAndPartition = TopicAndPartition("iteblog", offsets.partition) //offsets.partition表示的是Kafka partition id
val o = kc.setConsumerOffsets(groupID, Map((topicAndPartition, offsets.untilOffset)))//offsets.untilOffset:是
if (o.isLeft) {
println(s"Error updating the offset to Kafka cluster: ${o.left.get}")
}
}
})
ssc.start()
ssc.awaitTermination()
ssc.stop()
}
}核心代码讲解：
使用KafkaUtils的createDirectStream方法，调用底层API直接消费Kafka Partition的数据（Kafka Partition和RDD Partition 一一对应）。createDirectStream返回值是DStream，底层是RDD。
val messages = KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder](ssc, kafkaParams, topicsSet)messages.foreachRDD 是对messages底层RDD计算其偏移范围。KafkaRDD和HasOffsetRanges关系(构造参数和泛型省略，具体见源码)：
KafkaRDD extends RDD[R](sc, Nil) with Logging with HasOffsetRanges//rdd是messages.foreachRDD中的变量，rdd其类型是KafkaRDD，但是由于多态的原因rdd实际上不是KafkaRDD类型，而是RDD类型，所以需要向下转型为HasOffsetRanges，调用offsetRanges方法。（回忆OffsetRange是对什么的封装？答案：topic名字，分区Id，开始偏移，结束偏移。）
val offsetsList: Array[OffsetRange] = rdd.asInstanceOf[HasOffsetRanges].offsetRanges
val offsetRanges = fromOffsets.map { case (tp, fo) =>
val uo = untilOffsets(tp)
OffsetRange(tp.topic, tp.partition, fo, uo.offset)
}.toArrayKafkaCluster 完成对更新偏移到zookeeper集群的封装工具类。
val kc = new KafkaCluster(kafkaParams)对offsetRanges数组遍历：setConsumerOffsets是KafkaCluster
强调：OffsetRange是对什么的封装？答案：topic名字，分区Id，开始偏移，结束偏移
for (offsets <- offsetsList) { //offsets.untilOffset是结束偏移量直接上代码：
package org.apache.spark.streaming.kafka
//使用org.apache.spark.streaming.kafka的原因： private[spark] object SimpleConsumerConfig限制只在spark包中使用！
import kafka.api.OffsetCommitRequest
import kafka.common.{ErrorMapping, OffsetMetadataAndError, TopicAndPartition}
import kafka.consumer.SimpleConsumer
import org.apache.spark.SparkException
import org.apache.spark.streaming.kafka.KafkaCluster.SimpleConsumerConfig
import scala.collection.mutable.ArrayBuffer
import scala.util.Random
import scala.util.control.NonFatal
class KafkaCluster(val kafkaParams: Map[String, String]) extends Serializable { //Err是类型ArrayBuffer[Throwable]别名
type Err = ArrayBuffer[Throwable]
@transient private var _config: SimpleConsumerConfig = null
def config: SimpleConsumerConfig = this.synchronized {
if (_config == null) {
//SimpleConsumerConfig的apply方法部分代码：
//val brokers = kafkaParams.get("metadata.broker.list").orElse(kafkaParams.get("bootstrap.servers"))
//所以kafkaParams必须包含key=metadata.broker.list或者bootstrap.servers对应的Value
_config = SimpleConsumerConfig(kafkaParams)
}
_config
}
/**
*
* @param groupId: String
* @param offsets: Map[TopicAndPartition, Long]
* @return
*/
def setConsumerOffsets(groupId: String,
offsets: Map[TopicAndPartition, Long]
): Either[Err, Map[TopicAndPartition, Short]] = {
setConsumerOffsetMetadata(groupId, offsets.map { kv =>
kv._1 -> OffsetMetadataAndError(kv._2)
})
}
def setConsumerOffsetMetadata(groupId: String,
metadata: Map[TopicAndPartition, OffsetMetadataAndError]
): Either[Err, Map[TopicAndPartition, Short]] = {
var result = Map[TopicAndPartition, Short]()
val req = OffsetCommitRequest(groupId, metadata)
val errs = new Err
val topicAndPartitions = metadata.keySet
withBrokers(Random.shuffle(config.seedBrokers), errs) { consumer =>
val resp = consumer.commitOffsets(req)
val respMap = resp.requestInfo
val needed = topicAndPartitions.diff(result.keySet)
needed.foreach { tp: TopicAndPartition =>
respMap.get(tp).foreach { err: Short =>
if (err == ErrorMapping.NoError) {
result += tp -> err
} else {
errs.append(ErrorMapping.exceptionFor(err))
}
}
}
if (result.keys.size == topicAndPartitions.size) {
return Right(result)
}
}
val missing = topicAndPartitions.diff(result.keySet)
errs.append(new SparkException(s"Couldn't set offsets for ${missing}"))
Left(errs)
}
private def withBrokers(brokers: Iterable[(String, Int)], errs: Err)
(fn: SimpleConsumer => Any): Unit = {
brokers.foreach { hp =>
var consumer: SimpleConsumer = null
try {
consumer = connect(hp._1, hp._2)
fn(consumer)
} catch {
case NonFatal(e) =>
errs.append(e)
} finally {
if (consumer != null) {
consumer.close()
}
}
}
}
def connect(host: String, port: Int): SimpleConsumer =
new SimpleConsumer(host, port, config.socketTimeoutMs,
config.socketReceiveBufferBytes, config.clientId)
}
小拓展：
Map的连续获取值用法：
object MapDemo extends App {
val map = Map("1" -> "11", "2" -> "22", "3" -> "33")
map.map {
case (a, b) =>
println(a + " " + b)
}
//语法：先获取key=4的value，如果存在返回否则获取key=5的value，如果存在返回，不存在的话直接异常。
val result = map.get("4").orElse(map.get("5")).getOrElse(throw new Exception("exception"))
println(result)
}核心代码讲解之SimpleConsumerConfig的apply方法：
/**
* Make a consumer config without requiring group.id or zookeeper.connect,
* since communicating with brokers also needs common settings such as timeout
*/
def apply(kafkaParams: Map[String, String]): SimpleConsumerConfig = {
// These keys are from other pre-existing kafka configs for specifying brokers,accept either // map连续获取值用法见上面示例
val brokers = kafkaParams.get("metadata.broker.list")
.orElse(kafkaParams.get("bootstrap.servers"))
.getOrElse(throw new SparkException(
"Must specify metadata.broker.list or bootstrap.servers"))
val props = new Properties()
kafkaParams.foreach { case (key, value) =>
// prevent warnings on parameters ConsumerConfig doesn't know about
if (key != "metadata.broker.list" && key != "bootstrap.servers") {
props.put(key, value)
}
}
//如果没有zookeeper.connect和group.id，设置其value为空字符串。
Seq("zookeeper.connect", "group.id").foreach { s =>
if (!props.contains(s)) {
props.setProperty(s, "")
}
}
new SimpleConsumerConfig(brokers, props)
}OffsetMetadataAndError样例类：
case class OffsetMetadataAndError(offsetMetadata: OffsetMetadata, error: Short = Errors.NONE.code) {
def offset = offsetMetadata.offset
def metadata = offsetMetadata.metadata
override def toString = "[%s,ErrorCode %d]".format(offsetMetadata, error)
}TopicAndPartition是对 topic和partition的id的封装的一个样例类
/**
*
* @param groupId: String
* @param offsets: Map[TopicAndPartition, Long] * Map中key和value含义TopicAndPartition 是topic和partition id封装long是消费的结束偏移具体更多细节见代码注释吧，写的很详细。就不在重复贴在博客了。
github地址：https://github.com/Dax1n/spark-kafka-directstream-zookeeper