SparkStreaming消費kafka數據
阿新 • • 發佈：2018-10-31
字符串 targe val offset 1.0 error .org 依賴 oot 概要：本例子為SparkStreaming消費kafka消息的例子，實現的功能是將數據實時的進行抽取、過濾、轉換，然後存儲到HDFS中。
實例代碼
package com.fwmagic.test import com.alibaba.fastjson.{JSON, JSONException} import org.apache.kafka.common.serialization.StringDeserializer import org.apache.spark.SparkConf import org.apache.spark.sql.{SaveMode, SparkSession} import org.apache.spark.streaming.kafka010._ import org.apache.spark.streaming.{Seconds, StreamingContext} import org.slf4j.LoggerFactory /** * created by fwmagic */ object RealtimeEtl { private val logger = LoggerFactory.getLogger(PVUV.getClass) def main(args: Array[String]): Unit = { System.setProperty("HADOOP_USER_NAME", "hadoop") val conf = new SparkConf().setAppName("RealtimeEtl").setMaster("local[*]") val spark = SparkSession.builder().config(conf).getOrCreate() val streamContext = new StreamingContext(spark.sparkContext, Seconds(5)) //直連方式相當於跟kafka的Topic至直接連接 //"auto.offset.reset:earliest(每次重啟重新開始消費)，latest(重啟時會從最新的offset開始讀取) val kafkaParams = Map[String, Object]( "bootstrap.servers" -> "hd1:9092,hd2:9092,hd3:9092", "key.deserializer" -> classOf[StringDeserializer], "value.deserializer" -> classOf[StringDeserializer], "group.id" -> "fwmagic", "auto.offset.reset" -> "latest", "enable.auto.commit" -> (false: java.lang.Boolean) ) val topics = Array("access") val kafkaDStream = KafkaUtils.createDirectStream[String, String]( streamContext, LocationStrategies.PreferConsistent, ConsumerStrategies.Subscribe[String, String](topics, kafkaParams) ) //如果使用SparkStream和Kafka直連方式整合，生成的kafkaDStream必須調用foreachRDD kafkaDStream.foreachRDD(kafkaRDD => { if (!kafkaRDD.isEmpty()) { //獲取當前批次的RDD的偏移量 val offsetRanges = kafkaRDD.asInstanceOf[HasOffsetRanges].offsetRanges //拿出kafka中的數據 val lines = kafkaRDD.map(_.value()) //將lines字符串轉換成json對象 val logBeanRDD = lines.map(line => { var logBean: LogBean = null try { logBean = JSON.parseObject(line, classOf[LogBean]) } catch { case e: JSONException => { //logger記錄 logger.error("json解析錯誤！line:" + line, e) } } logBean }) //過濾 val filteredRDD = logBeanRDD.filter(_ != null) //將RDD轉化成DataFrame,因為RDD中裝的是case class import spark.implicits._ val df = filteredRDD.toDF() df.show() //將數據寫到hdfs中:hdfs://hd1:9000/360 df.repartition(1).write.mode(SaveMode.Append).parquet(args(0)) //提交當前批次的偏移量，偏移量最後寫入kafka kafkaDStream.asInstanceOf[CanCommitOffsets].commitAsync(offsetRanges) } }) //啟動 streamContext.start() streamContext.awaitTermination() streamContext.stop() } } case class LogBean(time:String, longitude:Double, latitude:Double, openid:String, page:String, evnet_type:Int)
依賴環境（pom.xml）
<?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.fwmagic.360</groupId> <artifactId>fwmagic-360</artifactId> <version>1.0</version> <properties> <maven.compiler.source>1.8</maven.compiler.source> <maven.compiler.target>1.8</maven.compiler.target> <scala.version>2.11.7</scala.version> <spark.version>2.2.2</spark.version> <hadoop.version>2.7.7</hadoop.version> <encoding>UTF-8</encoding> </properties> <dependencies> <!-- 導入scala的依賴 --> <dependency> <groupId>org.scala-lang</groupId> <artifactId>scala-library</artifactId> <version>${scala.version}</version> </dependency> <!-- 導入spark的依賴 --> <dependency> <groupId>org.apache.spark</groupId> <artifactId>spark-core_2.11</artifactId> <version>${spark.version}</version> </dependency> <!-- 導入spark-sql的依賴 --> <dependency> <groupId>org.apache.spark</groupId> <artifactId>spark-sql_2.11</artifactId> <version>${spark.version}</version> </dependency> <!-- spark streamingd的依賴 --> <dependency> <groupId>org.apache.spark</groupId> <artifactId>spark-streaming_2.11</artifactId> <version>${spark.version}</version> </dependency> <dependency> <groupId>org.apache.spark</groupId> <artifactId>spark-streaming-kafka-0-10_2.11</artifactId> <version>${spark.version}</version> </dependency> <!-- 指定hadoop-client API的版本 --> <dependency> <groupId>org.apache.hadoop</groupId> <artifactId>hadoop-client</artifactId> <version>${hadoop.version}</version> </dependency> <!-- 指定hadoop-client API的版本 --> <dependency> <groupId>org.apache.hadoop</groupId> <artifactId>hadoop-client</artifactId> <version>${hadoop.version}</version> </dependency> <dependency> <groupId>com.alibaba</groupId> <artifactId>fastjson</artifactId> <version>1.2.39</version> </dependency> </dependencies> <build> <pluginManagement> <plugins> <!-- 編譯scala的插件 --> <plugin> <groupId>net.alchim31.maven</groupId> <artifactId>scala-maven-plugin</artifactId> <version>3.2.2</version> </plugin> <!-- 編譯java的插件 --> <plugin> <groupId>org.apache.maven.plugins</groupId> <artifactId>maven-compiler-plugin</artifactId> <version>3.5.1</version> </plugin> </plugins> </pluginManagement> <plugins> <plugin> <groupId>net.alchim31.maven</groupId> <artifactId>scala-maven-plugin</artifactId> <executions> <execution> <id>scala-compile-first</id> <phase>process-resources</phase> <goals> <goal>add-source</goal> <goal>compile</goal> </goals> </execution> <execution> <id>scala-test-compile</id> <phase>process-test-resources</phase> <goals> <goal>testCompile</goal> </goals> </execution> </executions> </plugin> <plugin> <groupId>org.apache.maven.plugins</groupId> <artifactId>maven-compiler-plugin</artifactId> <executions> <execution> <phase>compile</phase> <goals> <goal>compile</goal> </goals> </execution> </executions> </plugin> <!-- 打jar插件 --> <plugin> <groupId>org.apache.maven.plugins</groupId> <artifactId>maven-shade-plugin</artifactId> <version>2.4.3</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> </configuration> </execution> </executions> </plugin> </plugins> </build> </project>
SparkStreaming消費kafka數據