2018-12-25
spark-streaming消费kafka数据：
首次消费截图：
手动kill，再次启动：
KafkaManager类：
package org.apache.spark.streaming.kafka
import kafka.common.TopicAndPartition
import kafka.message.MessageAndMetadata
import kafka.serializer.Decoder
import org.apache.spark.SparkException
import org.apache.spark.rdd.RDD
import org.apache.spark.streaming.StreamingContext
import org.apache.spark.streaming.dstream.InputDStream
import org.apache.spark.streaming.kafka.KafkaCluster.LeaderOffset
import scala.reflect.ClassTag
/**
* 手动管理偏移量
*/
class KafkaManager(val kafkaParams:Map[String,String])extends Serializable {
private val kc =new KafkaCluster(kafkaParams)
/**
* 创建数据流
*/
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 groupId = kafkaParams("group.id")
// 在zookeeper上读取offsets前先根据实际情况更新offsets
setOrUpdateOffsets(topics, groupId)
//从zookeeper上读取offset开始消费message
val messages = {
val partitionsE =kc.getPartitions(topics)
if (partitionsE.isLeft)
throw new SparkException(s"get kafka partition failed: ${partitionsE.left.get}")
val partitions = partitionsE.right.get
val consumerOffsetsE =kc.getConsumerOffsets(groupId, partitions)
if (consumerOffsetsE.isLeft)
throw new SparkException(s"get kafka consumer offsets failed: ${consumerOffsetsE.left.get}")
val consumerOffsets = consumerOffsetsE.right.get
KafkaUtils.createDirectStream[K,V,KD,VD, (K,V)](
ssc, kafkaParams, consumerOffsets, (mmd: MessageAndMetadata[K,V]) => (mmd.key, mmd.message))
}
messages
}
/**
* 创建数据流前，根据实际消费情况更新消费offsets
* @param topics
* @param groupId
*/
private def setOrUpdateOffsets(topics:Set[String], groupId:String): Unit = {
topics.foreach(topic => {
var hasConsumed =true
val partitionsE =kc.getPartitions(Set(topic))
if (partitionsE.isLeft)
throw new SparkException(s"get kafka partition failed: ${partitionsE.left.get}")
val partitions = partitionsE.right.get
//kc根据消费者组和主题对应的分区从zookeeper获取偏移量
val consumerOffsetsE =kc.getConsumerOffsets(groupId, partitions)
if (consumerOffsetsE.isLeft) hasConsumed =false
if (hasConsumed) {// 消费过
/**
* 如果streaming程序执行的时候出现kafka.common.OffsetOutOfRangeException，
* 说明zk上保存的offsets已经过时了，即kafka的定时清理策略已经将包含该offsets的文件删除。
* 针对这种情况，只要判断一下zk上的consumerOffsets和earliestLeaderOffsets的大小，
* 如果consumerOffsets比earliestLeaderOffsets还小的话，说明consumerOffsets已过时,
* 这时把consumerOffsets更新为earliestLeaderOffsets
*/
println("------消费过------")
val earliestLeaderOffsetsE =kc.getEarliestLeaderOffsets(partitions)
if (earliestLeaderOffsetsE.isLeft)
throw new SparkException(s"get earliest leader offsets failed: ${earliestLeaderOffsetsE.left.get}")
val earliestLeaderOffsets = earliestLeaderOffsetsE.right.get
val consumerOffsets = consumerOffsetsE.right.get
// 可能只是存在部分分区consumerOffsets过时，所以只更新过时分区的consumerOffsets为earliestLeaderOffsets
var offsets:Map[TopicAndPartition, Long] =Map()
consumerOffsets.foreach({case(tp, n) =>
val earliestLeaderOffset = earliestLeaderOffsets(tp).offset
if (n < earliestLeaderOffset) {
println("consumer group:" + groupId +",topic:" + tp.topic +",partition:" + tp.partition +
" offsets已经过时，更新为" + earliestLeaderOffset)
offsets += (tp -> earliestLeaderOffset)
}
})
//若是kafka分区发生新增,则对应的分区偏移量设置为从头开始消费
val earliestTopicAndPartition:Set[TopicAndPartition] = earliestLeaderOffsets.keySet
for(topicAndPartition <- earliestTopicAndPartition){
if(!consumerOffsets.contains(topicAndPartition)){
println("consumer group:" + groupId +",topic:" + topicAndPartition.topic +",partition:" + topicAndPartition.partition +
" kafka分区新增设置偏移量为0L")
offsets += (topicAndPartition ->0L)
}
}
if (offsets.nonEmpty) {
kc.setConsumerOffsets(groupId, offsets)
}
}else {// 没有消费过
println("------没有消费过------")
val reset = kafkaParams.get("auto.offset.reset").map(_.toLowerCase)
var leaderOffsets:Map[TopicAndPartition, LeaderOffset] =null
if (reset ==Some("smallest")) {
val leaderOffsetsE =kc.getEarliestLeaderOffsets(partitions)
if (leaderOffsetsE.isLeft)
throw new SparkException(s"get earliest leader offsets failed: ${leaderOffsetsE.left.get}")
leaderOffsets = leaderOffsetsE.right.get
}else {
val leaderOffsetsE =kc.getLatestLeaderOffsets(partitions)
if (leaderOffsetsE.isLeft)
throw new SparkException(s"get latest leader offsets failed: ${leaderOffsetsE.left.get}")
leaderOffsets = leaderOffsetsE.right.get
}
val offsets = leaderOffsets.map {
case (tp, offset) => (tp, offset.offset)
}
kc.setConsumerOffsets(groupId, offsets)
}
})
}
/**
* 更新zookeeper上的消费offsets
* @param rdd
*/
def updateZKOffsets(rdd: RDD[(String,String)]) : Unit = {
val groupId = kafkaParams.get("group.id").get
val offsetsList = rdd.asInstanceOf[HasOffsetRanges].offsetRanges
for (offsets <- offsetsList) {
val topicAndPartition =TopicAndPartition(offsets.topic, offsets.partition)
val o =kc.setConsumerOffsets(groupId,Map((topicAndPartition, offsets.untilOffset)))
if (o.isLeft) {
println(s"Error updating the offset to Kafka cluster: ${o.left.get}")
}
}
}
}
测试object：
package streaming
import kafka.serializer.StringDecoder
import org.apache.log4j.{Level, Logger}
import org.apache.spark.SparkConf
import org.apache.spark.rdd.RDD
import org.apache.spark.streaming.kafka.KafkaManager
import org.apache.spark.streaming.{Seconds, StreamingContext}
/**
*
*/
object SparkKafkaStreaming{
def processRdd(rdd: RDD[(String,String)]): Unit = {
val lines = rdd.map(_._2)
lines.foreach(println)
}
def main(args: Array[String]) {
if (args.length <3) {
System.err.println(
s"""
|Usage: DirectKafkaWordCount
| is a list of one or more Kafka brokers
| is a list of one or more kafka topics to consume from
| is a consume group
|
""".stripMargin)
System.exit(1)
}
Logger.getLogger("org").setLevel(Level.WARN)
val Array(brokers, topics, groupId) = args
// Create context with 2 second batch interval
val sparkConf =new SparkConf().setAppName("DirectKafkaWordCount")
sparkConf.setMaster("local[3]")
sparkConf.set("spark.streaming.kafka.maxRatePerPartition","5")
sparkConf.set("spark.serializer","org.apache.spark.serializer.KryoSerializer")
val ssc =new StreamingContext(sparkConf,Seconds(5))
ssc.sparkContext.setLogLevel("WARN")
// Create direct kafka stream with brokers and topics
val topicsSet = topics.split(",").toSet
val kafkaParams =Map[String,String](
"metadata.broker.list" -> brokers,
"group.id" -> groupId,
"auto.offset.reset" ->"smallest"
)
val km =new KafkaManager(kafkaParams)
val messages = km.createDirectStream[String,String, StringDecoder, StringDecoder](
ssc, kafkaParams, topicsSet)
messages.foreachRDD(rdd => {
if (!rdd.isEmpty()) {
// 先处理消息
processRdd(rdd)
// 再更新offsets
km.updateZKOffsets(rdd)
}
})
ssc.start()
ssc.awaitTermination()
}
}
修改点：
若是kafka新增分区,zookeeper无对应的分区，消费从头开始消费
