sparkStreaming与kafka整合案例

今天药忘吃喽~ 2024-04-18 10:11 9阅读 0赞

pom文件

<?xml version="1.0" encoding="UTF-8"?>
    <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/xsd/maven-4.0.0.xsd">
        <modelVersion>4.0.0</modelVersion>
    
        <groupId>com.cll</groupId>
        <artifactId>day01</artifactId>
        <version>1.0-SNAPSHOT</version>
    
        
        <repositories>
            <repository>
                <id>aliyun</id>
                <url>http://maven.aliyun.com/nexus/content/groups/public/</url>
            </repository>
            <repository>
                <id>cloudera</id>
                <url>https://repository.cloudera.com/artifactory/cloudera-repos/</url>
            </repository>
            <repository>
                <id>jboss</id>
                <url>http://repository.jboss.com/nexus/content/groups/public</url>
            </repository>
        </repositories>
        <properties>
            <maven.compiler.source>1.8</maven.compiler.source>
            <maven.compiler.target>1.8</maven.compiler.target>
            <encoding>UTF-8</encoding>
            <scala.version>2.11.8</scala.version>
            <scala.compat.version>2.11</scala.compat.version>
            <hadoop.version>2.7.4</hadoop.version>
            <spark.version>2.2.0</spark.version>
        </properties>
        <dependencies>
            <dependency>
                <groupId>org.scala-lang</groupId>
                <artifactId>scala-library</artifactId>
                <version>${scala.version}</version>
            </dependency>
            <dependency>
                <groupId>org.apache.spark</groupId>
                <artifactId>spark-core_2.11</artifactId>
                <version>${spark.version}</version>
            </dependency>
            <dependency>
                <groupId>org.apache.spark</groupId>
                <artifactId>spark-sql_2.11</artifactId>
                <version>${spark.version}</version>
            </dependency>
            <dependency>
                <groupId>org.apache.spark</groupId>
                <artifactId>spark-hive_2.11</artifactId>
                <version>${spark.version}</version>
            </dependency>
            <dependency>
                <groupId>org.apache.spark</groupId>
                <artifactId>spark-hive-thriftserver_2.11</artifactId>
                <version>${spark.version}</version>
            </dependency>
            <dependency>
                <groupId>org.apache.spark</groupId>
                <artifactId>spark-streaming_2.11</artifactId>
                <version>${spark.version}</version>
            </dependency>
            
            <dependency>
                <groupId>org.apache.spark</groupId>
                <artifactId>spark-streaming-kafka-0-10_2.11</artifactId>
                <version>${spark.version}</version>
            </dependency>
            <dependency>
                <groupId>org.apache.spark</groupId>
                <artifactId>spark-sql-kafka-0-10_2.11</artifactId>
                <version>${spark.version}</version>
            </dependency>
    
            
    
            <dependency>
                <groupId>org.apache.hadoop</groupId>
                <artifactId>hadoop-client</artifactId>
                <version>2.7.4</version>
            </dependency>
            <dependency>
                <groupId>org.apache.hbase</groupId>
                <artifactId>hbase-client</artifactId>
                <version>1.3.1</version>
            </dependency>
            <dependency>
                <groupId>org.apache.hbase</groupId>
                <artifactId>hbase-server</artifactId>
                <version>1.3.1</version>
            </dependency>
            <dependency>
                <groupId>com.typesafe</groupId>
                <artifactId>config</artifactId>
                <version>1.3.3</version>
            </dependency>
            <dependency>
                <groupId>mysql</groupId>
                <artifactId>mysql-connector-java</artifactId>
                <version>5.1.38</version>
            </dependency>
        </dependencies>
    
        <build>
            <sourceDirectory>src/main/scala</sourceDirectory>
            <testSourceDirectory>src/test/scala</testSourceDirectory>
            <plugins>
                
                <plugin>
                    <groupId>org.apache.maven.plugins</groupId>
                    <artifactId>maven-compiler-plugin</artifactId>
                    <version>3.5.1</version>
                </plugin>
                
                <plugin>
                    <groupId>net.alchim31.maven</groupId>
                    <artifactId>scala-maven-plugin</artifactId>
                    <version>3.2.2</version>
                    <executions>
                        <execution>
                            <goals>
                                <goal>compile</goal>
                                <goal>testCompile</goal>
                            </goals>
                            <configuration>
                                <args>
                                    <arg>-dependencyfile</arg>
                                    <arg>${project.build.directory}/.scala_dependencies</arg>
                                </args>
                            </configuration>
                        </execution>
                    </executions>
                </plugin>
                <plugin>
                    <groupId>org.apache.maven.plugins</groupId>
                    <artifactId>maven-surefire-plugin</artifactId>
                    <version>2.18.1</version>
                    <configuration>
                        <useFile>false</useFile>
                        <disableXmlReport>true</disableXmlReport>
                        <includes>
                            <include>**/*Test.*</include>
                            <include>**/*Suite.*</include>
                        </includes>
                    </configuration>
                </plugin>
                <plugin>
                    <groupId>org.apache.maven.plugins</groupId>
                    <artifactId>maven-shade-plugin</artifactId>
                    <version>2.3</version>
                    <executions>
                        <execution>
                            <phase>package</phase>
                            <goals>
                                <goal>shade</goal>
                            </goals>
                            <configuration>
                                <filters>
                                    <filter>
                                        <artifact>*:*</artifact>
                                        <excludes>
                                            <exclude>META-INF/*.SF</exclude>
                                            <exclude>META-INF/*.DSA</exclude>
                                            <exclude>META-INF/*.RSA</exclude>
                                        </excludes>
                                    </filter>
                                </filters>
                                <transformers>
                                    <transformer
                                            implementation="org.apache.maven.plugins.shade.resource.ManifestResourceTransformer">
                                        <mainClass></mainClass>
                                    </transformer>
                                </transformers>
                            </configuration>
                        </execution>
                    </executions>
                </plugin>
            </plugins>
        </build>
    </project>

代码：

object SparkStreamingKafka {
      def main(args: Array[String]): Unit = {
        //创建streamingContext
        val conf: SparkConf = new SparkConf().setAppName("wc").setMaster("local[*]")
        val sc: SparkContext = new SparkContext(conf)
        sc.setLogLevel("WARN")
    
        val ssc = new StreamingContext(sc,Seconds(5))
        ssc.checkpoint("./ssc")
    
        //2|准备连接参数
        val kafkaParams = Map[String, Object](
          "bootstrap.servers" -> "node02:9092",
          "key.deserializer" -> classOf[StringDeserializer],
          "value.deserializer" -> classOf[StringDeserializer],
          "group.id" -> "ssc",
          //earliest 当各个分区下有已经提交的offset时，从提交的offset开始消费，无提交的offset时，从头开始消费
          //latest 当各个分区下有已经提交的offset时，从提交的offset开始消费，无提交的offset时，消费新产生的该分区下的数据
          //none:topic各个分区都存在已经提交的offset时，从offset后开始消费，只要有一个分区不存在已经提交的额offset，则跑出异常
    
          //这里配置的latest自动充值偏移量为罪行的偏移量，即如果有偏移量，从偏移量位置开始消费，如果没有偏移量，从新来的额数据开始消费
          "auto.offset.reset" -> "latest",
          //false表示手动提交，默认是由sparkstreaming提交到checkpoint,也可以手动提交到其他地方checkpoint，如mysql/redis
          "enable.auto.commit" -> (false: java.lang.Boolean)
        )
    
        //3、声明连接的主题
        val topics = Array("spark_kafka")
        //4、连接对应的分区，接收数据
        val recordDStream: InputDStream[ConsumerRecord[String, String]] = KafkaUtils.createDirectStream[String, String](
          ssc,
          LocationStrategies.PreferConsistent, //位置策略，开发spark和kafka一般不会再一台机器，所以使用源码中强烈推荐的粗略
          ConsumerStrategies.Subscribe[String, String](topics, kafkaParams)//消费策略，使用源码中强烈推荐的
        )
    
        //5、获取value
        //DStream，发送的一行行数据
        val valueDstream: DStream[String] = recordDStream.map(_.value())//_表示一条数据，就是ConsumerRecord[String, String]
    
        //6、wordcount
        val resDStream: DStream[(String, Int)] = valueDstream.flatMap(_.split(" ")).map((_,1)).reduceByKey(_+_)
    
        //7、输出
        resDStream.print
    
        //8.开启
        ssc.start()
        //9、等待终止
        ssc.awaitTermination()
    
      }
    
    }