转载：http://blog.csdn.net/ligt0610/article/details/47311771
由于目前每天需要从kafka中消费20亿条左右的消息，集群压力有点大，会导致job不同程度的异常退出。原来使用spark1.1.0版本中的createStream函数，但是在数据处理速度跟不上数据消费速度且job异常退出的情况下，可能造成大量的数据丢失。幸好，Spark后续版本对这一情况有了很大的改进，1.2版本加入WAL特性，但是性能应该会受到一些影响（本人未测试），1.3版本可以直接通过低阶API从kafka的topic消费消息，并且不再向zookeeper中更新consumer offsets，使得基于zookeeper的consumer offsets的监控工具都会失效。
官方只是非常简单的描述了可以用以下方法修改zookeeper中的consumer offsets（可以查看http://spark.apache.org/docs/1.4.1/streaming-kafka-integration.html）：
// Hold a reference to the current offset ranges, so it can be used downstream
var offsetRanges = Array[OffsetRange]()
directKafkaStream.transform { rdd =>
offsetRanges = rdd.asInstanceOf[HasOffsetRanges].offsetRanges
rdd
}.map {
...
}.foreachRDD { rdd =>
for (o <- offsetRanges) {
println(s"${o.topic} ${o.partition} ${o.fromOffset} ${o.untilOffset}")
}
...
}
所以更新zookeeper中的consumer offsets还需要自己去实现，并且官方提供的两个createDirectStream重载并不能很好的满足我的需求，需要进一步封装。具体看以下KafkaManager类的代码：
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
/**
* Created by knowpigxia on 15-8-5.
*/
class KafkaManager(val kafkaParams: Map[String, String]) extends Serializable {
private val kc = new KafkaCluster(kafkaParams)
/**
* 创建数据流
* @param ssc
* @param kafkaParams
* @param topics
* @tparam K
* @tparam V
* @tparam KD
* @tparam VD
* @return
*/
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.get("group.id").get
// 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
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
*/
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)
}
})
if (!offsets.isEmpty) {
kc.setConsumerOffsets(groupId, offsets)
}
} else {// 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}")
}
}
}
}
接下来再给一个简单的例子：
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._
import org.apache.spark.streaming.{Seconds, StreamingContext}
/**
* Created by knowpigxia on 15-8-4.
*/
object DirectKafkaWordCount {
def dealLine(line: String): String = {
val list = AnalysisUtil.dealString(line, ‘,‘, ‘"‘)// 把dealString函数当做split即可
list.get(0).substring(0, 10) + "-" + list.get(26)
}
def processRdd(rdd: RDD[(String, String)]): Unit = {
val lines = rdd.map(_._2)
val words = lines.map(dealLine(_))
val wordCounts = words.map(x => (x, 1L)).reduceByKey(_ + _)
wordCounts.foreach(println)
}
def main(args: Array[String]) {
if (args.length < 3) {
System.err.println( s"""
|Usage: DirectKafkaWordCount <brokers> <topics> <groupid>
| <brokers> is a list of one or more Kafka brokers
| <topics> is a list of one or more kafka topics to consume from
| <groupid> 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[*]")
sparkConf.set("spark.streaming.kafka.maxRatePerPartition", "5")
sparkConf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
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,
"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()
}
}
时间： 2024-03-21 19:47:53