Spark createDirectStream 维护 Kafka offset(Scala)
createDirectStream方式需要自己维护offset,使程序可以实现中断后从中断处继续消费数据。
KafkaManager.scala
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}")
}
}
}
}
主程序中
def initKafkaParams = {
Map[String, String](
"metadata.broker.list" -> Constants.KAFKA_BROKERS,
"group.id " -> Constants.KAFKA_CONSUMER_GROUP,
"fetch.message.max.bytes" -> "20971520",
"auto.offset.reset" -> "smallest"
)
}
// kafka参数
val kafkaParams = initKafkaParams
val manager = new KafkaManager(kafkaParams)
val messageDstream = manager.createDirectStream[String, String, StringDecoder, StringDecoder](ssc, kafkaParams, Set(topic))
// 更新offsets
manager.updateZKOffsets(rdd)
Spark createDirectStream 维护 Kafka offset(Scala)的更多相关文章
- Spark自定义维护kafka的offset到zk
import kafka.common.TopicAndPartition import kafka.message.MessageAndMetadata import kafka.serialize ...
- spark streaming中维护kafka偏移量到外部介质
spark streaming中维护kafka偏移量到外部介质 以kafka偏移量维护到redis为例. redis存储格式 使用的数据结构为string,其中key为topic:partition, ...
- scala spark-streaming整合kafka (spark 2.3 kafka 0.10)
Maven组件如下: ) { System.err.println() } StreamingExamples.setStreamingLogLevels() )) ) { System.) } )) ...
- spark streaming从指定offset处消费Kafka数据
spark streaming从指定offset处消费Kafka数据 -- : 770人阅读 评论() 收藏 举报 分类: spark() 原文地址:http://blog.csdn.net/high ...
- Spark Streaming消费Kafka Direct保存offset到Redis,实现数据零丢失和exactly once
一.概述 上次写这篇文章文章的时候,Spark还是1.x,kafka还是0.8x版本,转眼间spark到了2.x,kafka也到了2.x,存储offset的方式也发生了改变,笔者根据上篇文章和网上文章 ...
- 【转】Spark Streaming和Kafka整合开发指南
基于Receivers的方法 这个方法使用了Receivers来接收数据.Receivers的实现使用到Kafka高层次的消费者API.对于所有的Receivers,接收到的数据将会保存在Spark ...
- 基于Spark Streaming + Canal + Kafka对Mysql增量数据实时进行监测分析
Spark Streaming可以用于实时流项目的开发,实时流项目的数据源除了可以来源于日志.文件.网络端口等,常常也有这种需求,那就是实时分析处理MySQL中的增量数据.面对这种需求当然我们可以通过 ...
- spark streaming 整合kafka(二)
转载:https://www.iteblog.com/archives/1326.html 和基于Receiver接收数据不一样,这种方式定期地从Kafka的topic+partition中查询最新的 ...
- Spark之 Spark Streaming整合kafka(Java实现版本)
pom依赖 <properties> <scala.version>2.11.8</scala.version> <hadoop.version>2.7 ...
随机推荐
- 【转】mybatis循环map的一些技巧
原文地址:http://blog.csdn.net/linminqin/article/details/39154133 循环key: <foreach collection="con ...
- Android开发工具--AndroidStudio
1.Android studio更改快捷键File->setttings 搜索key map就可以更改成自己喜欢的会计键风格了
- ajax邮箱、用户名唯一性验证
<script type="text/javascript"> $(function () { $("#txtEmail").blur(functi ...
- pxe+kickstart 无人值守安装CentOS7.1
CentOS6.6下PXE+Kickstart无人值守安装CentOS7.1操作系统 一.简介 1.1 什么是PXE Pxe(Pre-boot Execution Environment,预启动执行 ...
- elasticsearch中client.transport.sniff的使用方法和注意事项
https://blog.csdn.net/J_bean/article/details/79507559
- 【转】巧用局部变量提升javascript性能
转自:http://www.jb51.net/article/47219.htm 巧用局部变量可以有效提升javascript性能,下面有个不错的示例,大家可以参考下 javascript中一 ...
- Visualbox安装Ubuntu网络设置
注意:Windows 10在安装Visualbox后,创建的Ubuntu系统只有32位的,没有64位供选择,原因是Windows 10系统自带的Hyper-V系统占用了CPU虚拟化技术,解决的方法是取 ...
- Nodejs调用Aras Innovator服务,处理AML并返回AML
公司已经布署了Aras Innovator服务器,如果需要与Aras Innovator进行交互,需要进行自主开发程序,例如使用C#.VB.Java等,都是可以与它进行交互的 C#:调用Aras In ...
- 构建ASP.NET MVC4+EF5+EasyUI+Unity2.x注入的后台管理系统
http://www.tuicool.com/articles/NfyqQr 本节主要知识点是easyui 的手风琴加树结构做菜单导航 有园友抱怨原来菜单非常难看,但是基于原有树形无限级别的设计,没有 ...
- Codeforces 811 A. Vladik and Courtesy
A. Vladik and Courtesy time limit per test 2 seconds memory limit per test 256 megabytes input sta ...