Spark拉取Kafka的流数据，转插入HBase中
pom.xml文件样例
<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.yys.spark</groupId>
<artifactId>spark</artifactId>
<version>1.0</version>
<inceptionYear>2008</inceptionYear>
<properties>
<scala.version>2.11.12</scala.version>
<kafka.version>0.9.0.1</kafka.version>
<spark.version>2.2.0</spark.version>
<hadoop.version>2.7.5</hadoop.version>
<hbase.version>1.4.0</hbase.version>
</properties>
<dependencies>
<dependency>
<groupId>org.scala-lang</groupId>
<artifactId>scala-library</artifactId>
<version>${scala.version}</version>
</dependency>
<!-- Kafka 依赖-->
<dependency>
<groupId>org.apache.kafka</groupId>
<artifactId>kafka_2.11</artifactId>
<version>${kafka.version}</version>
</dependency>
<!-- Hadoop 依赖-->
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-client</artifactId>
<version>${hadoop.version}</version>
</dependency>
<!-- HBase 依赖-->
<dependency>
<groupId>org.apache.hbase</groupId>
<artifactId>hbase-client</artifactId>
<version>${hbase.version}</version>
</dependency>
<dependency>
<groupId>org.apache.hbase</groupId>
<artifactId>hbase-server</artifactId>
<version>${hbase.version}</version>
</dependency>
<!-- Spark Streaming 依赖-->
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-streaming_2.11</artifactId>
<version>${spark.version}</version>
</dependency>
<!-- Spark Streaming整合Flume 依赖-->
<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>${spark.version}</version>
</dependency>
<dependency>
<groupId>org.apache.commons</groupId>
<artifactId>commons-lang3</artifactId>
<version>3.5</version>
</dependency>
<!-- Spark SQL 依赖-->
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-sql_2.11</artifactId>
<version>${spark.version}</version>
</dependency>
<dependency>
<groupId>com.fasterxml.jackson.module</groupId>
<artifactId>jackson-module-scala_2.11</artifactId>
<version>2.6.5</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>5.1.44</version>
</dependency>
<dependency>
<groupId>org.apache.flume.flume-ng-clients</groupId>
<artifactId>flume-ng-log4jappender</artifactId>
<version>1.8.0</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>scala代码：
import org.apache.hadoop.hbase.HBaseConfiguration
import org.apache.hadoop.hbase.HColumnDescriptor
import org.apache.hadoop.hbase.HTableDescriptor
import org.apache.hadoop.hbase.client.HBaseAdmin
import org.apache.hadoop.hbase.client.HTable
import org.apache.hadoop.hbase.client.Put
import org.apache.hadoop.hbase.client.Result
import org.apache.hadoop.hbase.client.Scan
import org.apache.hadoop.hbase.util.Bytes
import org.apache.spark._
import org.apache.spark.SparkContext
import org.apache.hadoop.hbase.client.Get
import org.apache.spark.serializer.KryoSerializer
import org.apache.hadoop.hbase.io.ImmutableBytesWritable
import org.apache.hadoop.hbase.mapreduce.TableInputFormat
import org.apache.hadoop.mapred.JobConf
import org.apache.hadoop.hbase.mapred.TableOutputFormat
import org.apache.spark.rdd.RDD.rddToPairRDDFunctions
import org.apache.hadoop.hbase.util.Bytes
//拉取kafka的数据流，转插入hbase中
object Kafka2Hbase {
def main(args: Array[String]): Unit = {
val sparkConf = new SparkConf().setMaster("local").setAppName("HBaseTest")
.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer");
val sc = new SparkContext(sparkConf)
//hbase中的表名称
var table_name = "create_table_at_first"
val conf = HBaseConfiguration.create()
//hbase的配置信息可以从/home/hadoop/HBase/hbase/conf/hbase-site.xml得到
conf.set("hbase.rootdir", "hdfs://master:9000/hbase_db")
conf.set("hbase.zookeeper.quorum", "master,Slave1,Slave2")
conf.set("hbase.zookeeper.property.clientPort", "2181")
conf.set("hbase.master", "60000")
conf.set(TableInputFormat.INPUT_TABLE, table_name)
//初始化jobconf，TableOutputFormat必须是org.apache.hadoop.hbase.mapred包下的！
val jobConf = new JobConf(conf)
jobConf.setOutputFormat(classOf[TableOutputFormat])
jobConf.set(TableOutputFormat.OUTPUT_TABLE, table_name)
val indataRDD = sc.makeRDD(Array("1,jack15,15", "2,mike16,16"))
val rdd = indataRDD.map(_.split(',')).map { arr => {
/*一个Put对象就是一行记录，在构造方法中指定主键
* 所有插入的数据必须用org.apache.hadoop.hbase.util.Bytes.toBytes方法转换
* Put.add方法接收三个参数：列族，列名，数据
* myfamily：为列族名
*/
val put = new Put(Bytes.toBytes(arr(0).toInt))
put.add(Bytes.toBytes("myfamily"), Bytes.toBytes("name"), Bytes.toBytes(arr(1)))
put.add(Bytes.toBytes("myfamily"), Bytes.toBytes("age"), Bytes.toBytes(arr(2).toInt))
//转化成RDD[(ImmutableBytesWritable,Put)]类型才能调用saveAsHadoopDataset
(new ImmutableBytesWritable, put)
}
}
rdd.saveAsHadoopDataset(jobConf)
sc.stop()
//之后在hbase中，可以get 'create_table_at_first','jack15','myfamily' 查询这条数据即可
}
}