Spark学习笔记——读写Hbase

1.首先在Hbase中建立一张表，名字为student

参考 Hbase学习笔记——基本CRUD操作

一个cell的值,取决于Row,Column family,Column Qualifier和Timestamp

Hbase表结构

2.往Hbase中写入数据，写入的时候，需要写family和column

build.sbt

libraryDependencies ++= Seq(
  "org.apache.spark" %% "spark-core" % "2.1.0",
  "mysql" % "mysql-connector-java" % "5.1.31",
  "org.apache.spark" %% "spark-sql" % "2.1.0",
  "org.apache.hbase" % "hbase-common" % "1.3.0",
  "org.apache.hbase" % "hbase-client" % "1.3.0",
  "org.apache.hbase" % "hbase-server" % "1.3.0",
  "org.apache.hbase" % "hbase" % "1.2.1"
)

在hbaseshell中写数据的时候，写的是String，但是在idea中写代码的话，如果写的是int类型的，就会出现x00...的情况

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.sql._
import java.util.Properties

import com.google.common.collect.Lists
import org.apache.spark.sql.types.{ArrayType, StringType, StructField, StructType}
import org.apache.hadoop.hbase.HBaseConfiguration
import org.apache.hadoop.hbase.client.{Get, Put, Result, Scan}
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.util.Bytes
import org.apache.hadoop.mapred.JobConf

/**
  * Created by mi on 17-4-11.
  */

case class resultset(name: String,
                     info: String,
                     summary: String)

case class IntroItem(name: String, value: String)


case class BaikeLocation(name: String,
                         url: String = "",
                         info: Seq[IntroItem] = Seq(),
                         summary: Option[String] = None)

case class MewBaikeLocation(name: String,
                            url: String = "",
                            info: Option[String] = None,
                            summary: Option[String] = None)


object MysqlOpt {

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

    // 本地模式运行,便于测试
    val conf = new SparkConf().setAppName("WordCount").setMaster("local")
    // 创建 spark context
    val sc = new SparkContext(conf)
    val sqlContext = new SQLContext(sc)
    import sqlContext.implicits._

    //定义数据库和表信息
    val url = "jdbc:mysql://localhost:3306/baidubaike?useUnicode=true&characterEncoding=UTF-8"
    val table = "baike_pages"

    // 读取Hbase文件，在hbase的/usr/local/hbase/conf/hbase-site.xml中写的地址
    val hbasePath = "file:///usr/local/hbase/hbase-tmp"

    // 创建hbase configuration
    val hBaseConf = HBaseConfiguration.create()
    hBaseConf.set(TableInputFormat.INPUT_TABLE, "student")

    // 初始化jobconf，TableOutputFormat必须是org.apache.hadoop.hbase.mapred包下的！
    val jobConf = new JobConf(hBaseConf)
    jobConf.setOutputFormat(classOf[TableOutputFormat])
    jobConf.set(TableOutputFormat.OUTPUT_TABLE, "student")

    val indataRDD = sc.makeRDD(Array("1,99,98","2,97,96","3,95,94"))

    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
       */
      val put = new Put(Bytes.toBytes(arr(0)))
      put.add(Bytes.toBytes("course"),Bytes.toBytes("math"),Bytes.toBytes(arr(1)))
      put.add(Bytes.toBytes("course"),Bytes.toBytes("english"),Bytes.toBytes(arr(2)))
      //转化成RDD[(ImmutableBytesWritable,Put)]类型才能调用saveAsHadoopDataset
      (new ImmutableBytesWritable, put)
    }}

    rdd.saveAsHadoopDataset(jobConf)

    sc.stop()

  }

}

3.从Hbase中读取数据

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.sql._
import java.util.Properties

import com.google.common.collect.Lists
import org.apache.spark.sql.types.{ArrayType, StringType, StructField, StructType}
import org.apache.hadoop.hbase.HBaseConfiguration
import org.apache.hadoop.hbase.client.{Get, Put, Result, Scan}
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.util.Bytes
import org.apache.hadoop.mapred.JobConf

/**
  * Created by mi on 17-4-11.
  */

case class resultset(name: String,
                     info: String,
                     summary: String)

case class IntroItem(name: String, value: String)


case class BaikeLocation(name: String,
                         url: String = "",
                         info: Seq[IntroItem] = Seq(),
                         summary: Option[String] = None)

case class MewBaikeLocation(name: String,
                            url: String = "",
                            info: Option[String] = None,
                            summary: Option[String] = None)


object MysqlOpt {

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

    // 本地模式运行,便于测试
    val conf = new SparkConf().setAppName("WordCount").setMaster("local")
    // 创建 spark context
    val sc = new SparkContext(conf)
    val sqlContext = new SQLContext(sc)
    import sqlContext.implicits._

    //定义数据库和表信息
    val url = "jdbc:mysql://localhost:3306/baidubaike?useUnicode=true&characterEncoding=UTF-8"
    val table = "baike_pages"

    // 读取Hbase文件，在hbase的/usr/local/hbase/conf/hbase-site.xml中写的地址
    val hbasePath = "file:///usr/local/hbase/hbase-tmp"

    // 创建hbase configuration
    val hBaseConf = HBaseConfiguration.create()
    hBaseConf.set(TableInputFormat.INPUT_TABLE, "student")

    // 从数据源获取数据并转化成rdd
    val hBaseRDD = sc.newAPIHadoopRDD(hBaseConf, classOf[TableInputFormat], classOf[ImmutableBytesWritable], classOf[Result])

    println(hBaseRDD.count())

    // 将数据映射为表  也就是将 RDD转化为 dataframe schema
    hBaseRDD.foreach{case (_,result) =>{
      //获取行键
      val key = Bytes.toString(result.getRow)
      //通过列族和列名获取列
      val math = Bytes.toString(result.getValue("course".getBytes,"math".getBytes))
      println("Row key:"+key+" Math:"+math)
    }}

    sc.stop()

  }


}

 输出

Row key:   
 Math:99
Row key:    Math:97
Row key:    Math:95
Row key:1 Math:99
Row key:1000 Math:99
Row key:2 Math:97
Row key:3 Math:95