import org.apache.hadoop.hbase.HBaseConfiguration

import org.apache.hadoop.hbase.io.ImmutableBytesWritable

import org.apache.hadoop.hbase.mapred.TableOutputFormat

import org.apache.spark.{SparkConf, SparkContext}

import org.apache.hadoop.hbase.client.{Put, Result}

import org.apache.hadoop.hbase.util.Bytes

import org.apache.hadoop.mapred.JobConf

object 写Hbase数据 {

  def main(args: Array[String]): Unit = {

    val sparkConf=new SparkConf().setAppName("往Hbase中写数据").setMaster("local[2]")

    val sc=new SparkContext(sparkConf)

    val tableName="student"

   // sc.hadoopConfiguration.set(TableOutputFormat.OUTPUT_TABLE,tableName)

    val conf=HBaseConfiguration.create()

    val jobConf=new JobConf(conf)

     jobConf.setOutputFormat(classOf[TableOutputFormat])

    jobConf.set(TableOutputFormat.OUTPUT_TABLE,tableName)

    //构建新纪录

    val dataRDD=sc.makeRDD(Array("5,hadoop,B,29","6,spark,G,56"))

   val rdd=dataRDD.map(_.split(",")).map{x=>{

     val put=new Put(Bytes.toBytes(x())) //行健的值   Put.add方法接收三个参数：列族,列名,数据

     put.addColumn(Bytes.toBytes("info"), Bytes.toBytes("name"), Bytes.toBytes(x())) //info:name列的值

     put.addColumn(Bytes.toBytes("info"),Bytes.toBytes("gender"),Bytes.toBytes(x())) //info:gender列的值

     put.addColumn(Bytes.toBytes("info"),Bytes.toBytes("age"),Bytes.toBytes(x()))//info:age列的值

     (new ImmutableBytesWritable,put)  ////转化成RDD[(ImmutableBytesWritable,Put)]类型才能调用saveAsHadoopDataset

   }}

    rdd.saveAsHadoopDataset(jobConf)

  }

}

hbase(main)::> scan 'student'

ROW                                    COLUMN+CELL

                                      column=info:age, timestamp=, value=

                                      column=info:gender, timestamp=, value=B

                                      column=info:name, timestamp=, value=hadoop

                                      column=info:age, timestamp=, value=

                                      column=info:gender, timestamp=, value=G

                                      column=info:name, timestamp=, value=spark

                                      column=info:age, timestamp=, value=

                                      column=info:gender, timestamp=, value=B

                                      column=info:name, timestamp=, value=hadoop

                                      column=info:age, timestamp=, value=

                                      column=info:gender, timestamp=, value=G

                                      column=info:name, timestamp=, value=spark