【十五】Spark Streaming整合Kafka使用Direct方式（使用Scala語言）
Kafka提供了新的consumer api 在0.8版本和0.10版本之間。0.8的整合是相容0.9和0.10的。但是0.10的整合不相容以前的版本。
這裡使用的整合是spark-streaming-kafka-0-8。官方文件
配置SparkStreaming接收從kafka來的資料有兩種方式。老的方式要使用Receiver，新的方式是Spark1.3後引進的不用Receiver。
Approach 1: Receiver-based Approach
Approach 2: Direct Approach (No Receivers)
這裡介紹第二種使用Direct的方式。
這是一種新的模式，在Spark1.3中引進的，它有更加強壯的端到端的資料保障。它代替了使用Receiver接收資料。
它週期性的去查詢Kafka每一個topic partition最新的偏移量，通過每一個批次處理偏移範圍。
當啟動Job去處理資料以後，Kafka'simple consumer API 從Kafka中去讀偏移量的範圍（和讀檔案系統很類似）。
這個新特性在Spark1.3中支援Scala和Java，在1.4中可以支援Python。
這種方式和Receiver方式對比的優點：
1.簡化了並行度。不需要建立多個input Kafka streams再粘合起來。而是直接使用directStream來處理。Spark Streaming將建立多個RDD partitions對接到Kafka paritions去消費，這樣從Kafka中讀取的資料就是並行的。在Kafka和RDD partitions之間是一對一的對映。
2.效能更高。能夠達到零資料丟失。然而在Receiver方式中需要把資料寫到WAL（ Write Ahead Log）中才能零資料丟失。
3.能夠滿足“只執行一次“不重複消費。在Receiver方式中要使用Kafka的高階API，去儲存消費偏移量在zookeeper中，這是一種傳統的消費Kafka的資料方式。
而Direct方式也有一種缺點，不能更新zookeeper中的偏移量，所以基於zookeeper的kafka監控工具就沒辦法展示處理。每一個批次都需要自己把偏移量更新到zookeeper中去。
實戰
1.啟動zk
cd /app/zookeeper/bin
./zkServer.sh start
2.啟動kafka
cd /app/kafka
bin/kafka-server-start.sh -daemon config/server.properties &
3.建立topic
bin/kafka-topics.sh --create --zookeeper node1:2181 --replication-factor 1 --partitions 1 --topic spark_topic
4.控制檯測試topic是否能夠正常生成和消費資訊
傳送訊息
bin/kafka-console-producer.sh --broker-list node1:9092 --topic spark_topic
hello kafka
hello spark streaming
9092是server.properties中配置的監聽埠
消費訊息
bin/kafka-console-consumer.sh --zookeeper node1:2181 --topic spark_topic
5.專案目錄
6.pom.xml
<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/maven-v4_0_0.xsd">
<modelVersion>4.0.0</modelVersion>
<groupId>com.sid.spark</groupId>
<artifactId>spark-train</artifactId>
<version>1.0</version>
<inceptionYear>2008</inceptionYear>
<properties>
<scala.version>2.11.8</scala.version>
<kafka.version>0.8.2.1</kafka.version>
<spark.version>2.2.0</spark.version>
<hadoop.version>2.9.0</hadoop.version>
<hbase.version>1.4.4</hbase.version>
</properties>
<repositories>
<repository>
<id>scala-tools.org</id>
<name>Scala-Tools Maven2 Repository</name>
<url>http://scala-tools.org/repo-releases</url>
</repository>
</repositories>
<pluginRepositories>
<pluginRepository>
<id>scala-tools.org</id>
<name>Scala-Tools Maven2 Repository</name>
<url>http://scala-tools.org/repo-releases</url>
</pluginRepository>
</pluginRepositories>
<dependencies>
<dependency>
<groupId>org.scala-lang</groupId>
<artifactId>scala-library</artifactId>
<version>${scala.version}</version>
</dependency>
<dependency>
<groupId>org.apache.kafka</groupId>
<artifactId>kafka_2.11</artifactId>
<version>${kafka.version}</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-client</artifactId>
<version>${hadoop.version}</version>
<exclusions>
<exclusion>
<artifactId>servlet-api</artifactId>
<groupId>javax.servlet</groupId>
</exclusion>
</exclusions>
</dependency>
<!--<dependency>-->
<!--<groupId>org.apache.hbase</groupId>-->
<!--<artifactId>hbase-clinet</artifactId>-->
<!--<version>${hbase.version}</version>-->
<!--</dependency>-->
<!--<dependency>-->
<!--<groupId>org.apache.hbase</groupId>-->
<!--<artifactId>hbase-server</artifactId>-->
<!--<version>${hbase.version}</version>-->
<!--</dependency>-->
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-streaming_2.11</artifactId>
<version>${spark.version}</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-sql_2.11</artifactId>
<version>${spark.version}</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-streaming-flume_2.11</artifactId>
<version>${spark.version}</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-streaming-flume-sink_2.11</artifactId>
<version>${spark.version}</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-streaming-kafka-0-8_2.11</artifactId>
<version>${spark.version}</version>
</dependency>
<dependency>
<groupId>net.jpountz.lz4</groupId>
<artifactId>lz4</artifactId>
<version>1.3.0</version>
</dependency>
<dependency>
<groupId>mysql</groupId>
<artifactId>mysql-connector-java</artifactId>
<version>5.1.31</version>
</dependency>
<dependency>
<groupId>org.apache.commons</groupId>
<artifactId>commons-lang3</artifactId>
<version>3.5</version>
</dependency>
</dependencies>
<build>
<sourceDirectory>src/main/scala</sourceDirectory>
<testSourceDirectory>src/test/scala</testSourceDirectory>
<plugins>
<plugin>
<groupId>org.scala-tools</groupId>
<artifactId>maven-scala-plugin</artifactId>
<executions>
<execution>
<goals>
<goal>compile</goal>
<goal>testCompile</goal>
</goals>
</execution>
</executions>
<configuration>
<scalaVersion>${scala.version}</scalaVersion>
<args>
<arg>-target:jvm-1.5</arg>
</args>
</configuration>
</plugin>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-eclipse-plugin</artifactId>
<configuration>
<downloadSources>true</downloadSources>
<buildcommands>
<buildcommand>ch.epfl.lamp.sdt.core.scalabuilder</buildcommand>
</buildcommands>
<additionalProjectnatures>
<projectnature>ch.epfl.lamp.sdt.core.scalanature</projectnature>
</additionalProjectnatures>
<classpathContainers>
<classpathContainer>org.eclipse.jdt.launching.JRE_CONTAINER</classpathContainer>
<classpathContainer>ch.epfl.lamp.sdt.launching.SCALA_CONTAINER</classpathContainer>
</classpathContainers>
</configuration>
</plugin>
</plugins>
</build>
<reporting>
<plugins>
<plugin>
<groupId>org.scala-tools</groupId>
<artifactId>maven-scala-plugin</artifactId>
<configuration>
<scalaVersion>${scala.version}</scalaVersion>
</configuration>
</plugin>
</plugins>
</reporting>
</project>
7.程式碼
package com.sid.spark
import kafka.serializer.StringDecoder
import org.apache.spark.SparkConf
import org.apache.spark.streaming.kafka.KafkaUtils
import org.apache.spark.streaming.{Seconds, StreamingContext}
/**
* Created by jy02268879 on 2018/7/19.
*
* Spark Streaming 基於 Direct 對接Kafka
*/
object KafkaDirect {
def main(args: Array[String]): Unit = {
if(args.length != 2){
System.err.println("Usage: KafkaDirect <brokers> <topics>")
System.exit(1)
}
val Array(brokers,topics) = args
val sparkConf = new SparkConf().setAppName("KafkaReceiver").setMaster("local[3]")
val ssc = new StreamingContext(sparkConf,Seconds(5))
/**
* @param ssc StreamingContext object
* @param kafkaParams Kafka <a href="http://kafka.apache.org/documentation.html#configuration">
* configuration parameters</a>. Requires "metadata.broker.list" or "bootstrap.servers"
* to be set with Kafka broker(s) (NOT zookeeper servers), specified in
* host1:port1,host2:port2 form.
* If not starting from a checkpoint, "auto.offset.reset" may be set to "largest" or "smallest"
* to determine where the stream starts (defaults to "largest")
* @param topics Names of the topics to consume
* @tparam K type of Kafka message key
* @tparam V type of Kafka message value
* @tparam KD type of Kafka message key decoder
* @tparam VD type of Kafka message value decoder
* @return DStream of (Kafka message key, Kafka message value)
*/
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
)
messages.print()
messages.map(_._2).flatMap(_.split(" ")).map((_,1)).reduceByKey(_+_).print()
ssc.start()
ssc.awaitTermination()
}
}
8.執行程式碼
9.在Kafka生成資料 a a a b b c c
10.IDEA檢視結果
本地執行成功後測試提交到伺服器上執行
修改程式碼註釋掉setAppName和setMaster
maven打包
把target生成的jar包傳到spark伺服器上去
執行
cd /app/spark/spark-2.2.0-bin-2.9.0/bin
./spark-submit --class com.sid.spark.KafkaDirect --master local[2] --name KafkaDirect --packages org.apache.spark:spark-streaming-kafka-0-8_2.11:2.2.0 /app/spark/test_data/spark-train-1.0-SNAPSHOT.jar node1:9092 spark_topic
UI
