Spark拉取Kafka的流数据，转插入HBase中

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 } }