spark操作HBASE

import org.apache.hadoop.hbase.util.Bytes
import org.apache.hadoop.hbase.{HColumnDescriptor, HTableDescriptor, TableName, HBaseConfiguration}
import org.apache.hadoop.hbase.client._
import org.apache.spark.SparkContext
import scala.collection.JavaConversions._

/**
 * HBase 1.0.0 新版API, CRUD 的基本操作代码示例
 **/
object HBaseNewAPI {
  def main(args: Array[String]) {
    val sc = new SparkContext("local", "SparkHBase")
    val conf = HBaseConfiguration.create()
    conf.set("hbase.zookeeper.property.clientPort", "2181")
    conf.set("hbase.zookeeper.quorum", "master")


    //Connection 的创建是个重量级的工作，线程安全，是操作hbase的入口
    val conn = ConnectionFactory.createConnection(conf)

    //从Connection获得 Admin 对象(相当于以前的 HAdmin)
    val admin = conn.getAdmin

    //本例将操作的表名
    val userTable = TableName.valueOf("user")

    //创建 user 表
    val tableDescr = new HTableDescriptor(userTable)
    tableDescr.addFamily(new HColumnDescriptor("basic".getBytes))
    println("Creating table `user`. ")
    if (admin.tableExists(userTable)) {
      admin.disableTable(userTable)
      admin.deleteTable(userTable)
    }
    admin.createTable(tableDescr)
    println("Done!")

    try{
      //获取 user 表
      val table = conn.getTable(userTable)

      try{
        //准备插入一条 key 为 id001 的数据
        val p = new Put("id001".getBytes)
        //为put操作指定 column 和 value （以前的 put.add 方法被弃用了）
        p.addColumn("basic".getBytes,"name".getBytes, "wuchong".getBytes)
        //提交
        table.put(p)

        //查询某条数据
        val g = new Get("id001".getBytes)
        val result = table.get(g)
        val value = Bytes.toString(result.getValue("basic".getBytes,"name".getBytes))
        println("GET id001 :"+value)

        //扫描数据
        val s = new Scan()
        s.addColumn("basic".getBytes,"name".getBytes)
        val scanner = table.getScanner(s)

        try{
          for(r <- scanner){
            println("Found row: "+r)
            println("Found value: "+Bytes.toString(r.getValue("basic".getBytes,"name".getBytes)))
          }
        }finally {
          //确保scanner关闭
          scanner.close()
        }

        //删除某条数据,操作方式与 Put 类似
        val d = new Delete("id001".getBytes)
        d.addColumn("basic".getBytes,"name".getBytes)
        table.delete(d)

      }finally {
        if(table != null) table.close()
      }

    }finally {
      conn.close()
    }
  }
}

　　

import org.apache.hadoop.hbase.client.Put
import org.apache.hadoop.hbase.filter.CompareFilter.CompareOp
import org.apache.hadoop.hbase.filter.SingleColumnValueFilter
import org.apache.hadoop.hbase.io.ImmutableBytesWritable
import org.apache.hadoop.hbase.mapred.TableOutputFormat
import org.apache.hadoop.hbase.mapreduce.TableInputFormat
import org.apache.hadoop.hbase.protobuf.ProtobufUtil
import org.apache.hadoop.hbase.util.{Base64, Bytes}
import org.apache.hadoop.hbase.HBaseConfiguration
import org.apache.hadoop.mapred.JobConf
import org.apache.spark.SparkContext
import org.apache.hadoop.hbase.client._

/**
 * Spark 读取和写入 HBase
 **/
object SparkOnHBase {


  def convertScanToString(scan: Scan) = {
    val proto = ProtobufUtil.toScan(scan)
    Base64.encodeBytes(proto.toByteArray)
  }

  def main(args: Array[String]) {
    val sc = new SparkContext("local","SparkOnHBase")

    val conf = HBaseConfiguration.create()
    conf.set("hbase.zookeeper.property.clientPort", "2181")
    conf.set("hbase.zookeeper.quorum", "master")


    // ======Save RDD to HBase========
    // step 1: JobConf setup
    val jobConf = new JobConf(conf,this.getClass)
    jobConf.setOutputFormat(classOf[TableOutputFormat])
    jobConf.set(TableOutputFormat.OUTPUT_TABLE,"user")


    // step 2: rdd mapping to table

    // 在 HBase 中表的 schema 一般是这样的
    // *row   cf:col_1    cf:col_2
    // 而在Spark中，我们操作的是RDD元组，比如(1,"lilei",14) , (2,"hanmei",18)
    // 我们需要将 *RDD[(uid:Int, name:String, age:Int)]* 转换成 *RDD[(ImmutableBytesWritable, Put)]*
    // 我们定义了 convert 函数做这个转换工作
    def convert(triple: (Int, String, Int)) = {
      val p = new Put(Bytes.toBytes(triple._1))
      p.addColumn(Bytes.toBytes("basic"),Bytes.toBytes("name"),Bytes.toBytes(triple._2))
      p.addColumn(Bytes.toBytes("basic"),Bytes.toBytes("age"),Bytes.toBytes(triple._3))
      (new ImmutableBytesWritable, p)
    }

    // step 3: read RDD data from somewhere and convert
    val rawData = List((1,"lilei",14), (2,"hanmei",18), (3,"someone",38))
    val localData = sc.parallelize(rawData).map(convert)

    //step 4: use `saveAsHadoopDataset` to save RDD to HBase
    localData.saveAsHadoopDataset(jobConf)
    // =================================


    // ======Load RDD from HBase========
    // use `newAPIHadoopRDD` to load RDD from HBase
    //直接从 HBase 中读取数据并转成 Spark 能直接操作的 RDD[K,V]

    //设置查询的表名
    conf.set(TableInputFormat.INPUT_TABLE, "user")

    //添加过滤条件，年龄大于 18 岁
    val scan = new Scan()
    scan.setFilter(new SingleColumnValueFilter("basic".getBytes,"age".getBytes,
      CompareOp.GREATER_OR_EQUAL,Bytes.toBytes(18)))
    conf.set(TableInputFormat.SCAN,convertScanToString(scan))

    val usersRDD = sc.newAPIHadoopRDD(conf, classOf[TableInputFormat],
      classOf[org.apache.hadoop.hbase.io.ImmutableBytesWritable],
      classOf[org.apache.hadoop.hbase.client.Result])

    val count = usersRDD.count()
    println("Users RDD Count:" + count)
    usersRDD.cache()

    //遍历输出
    usersRDD.foreach{ case (_,result) =>
      val key = Bytes.toInt(result.getRow)
      val name = Bytes.toString(result.getValue("basic".getBytes,"name".getBytes))
      val age = Bytes.toInt(result.getValue("basic".getBytes,"age".getBytes))
      println("Row key:"+key+" Name:"+name+" Age:"+age)
    }
    // =================================
  }
}

　　

转：https://gist.github.com/wuchong/95630f80966d07d7453b#file-hbasenewapi-scala

http://wuchong.me/blog/2015/04/04/spark-on-yarn-cluster-deploy/